Hiro development journal

Moving the Hiro journal to a dedicated subdomain

2026-08-02T11:22:46-07:00

Executive summary The Hiro development journal has been reworked to use https://hiro.bballstatistics.com as its canonical public origin, with the journal index at the subdomain root. All generated journal pages, JSON indexes, aliases, feed entries, discovery files, sitemap URLs, validation checks, and IndexNow submission settings now target the dedicated subdomain. The NBA statistics homepage no longer links to the Hiro journal, and its own sitemap and robots metadata no longer advertise journal content. The new hostname is active through a DNS-only Cloudflare CNAME and Vercel TLS; production validation exposed two physical-file routing collisions, and targeted precedence rules were added for the journal root and sitemap. Work completed Journal URL migration (Deployed and production-validated) Changed canonical HTML metadata and all current internal journal links from the NBA site's /hiro path to root-relative paths on the Hiro subdomain. Changed machine-readable index URLs, Atom identifiers and links, sitemap URLs, llms.txt endpoints, retrieval diagnostics, and IndexNow host settings to hiro.bballstatistics.com. Kept historical journal source statements intact because they document facts and URLs that were true when those entries were published. Host routing and legacy redirects (Completed in source) Added a host-conditioned Vercel rewrite that serves the existing generated journal directory at the root of hiro.bballstatistics.com. Added permanent redirects from the old apex and www /hiro paths to equivalent paths on the new subdomain. Added cleanup redirects so an accidental /hiro path on the new subdomain resolves to the canonical root-relative URL. After production showed that Vercel's physical NBA index and sitemap files took precedence over the catch-all rewrite, added two narrowly scoped host routes that override those exact collisions without changing NBA-host behavior. NBA site separation (Completed in source) Removed the Hiro development updates link from the NBA site's opening page. Removed the old root-level Hiro HTML and llms.txt aliases from the NBA deployment surface. Replaced the NBA site's journal-only sitemap with a homepage sitemap and moved journal-specific robots, sitemap, llms.txt, and IndexNow verification files under the journal output used by the subdomain rewrite. Live domain and retrieval diagnosis (Activated and tested) Confirmed that bballstatistics.com and www.bballstatistics.com are still proxied through Cloudflare and currently serve the Vercel deployment successfully to ordinary HTTP clients. Confirmed that the AI web retrieval layer still rejects the Cloudflare-proxied bballstatistics.com URLs before returning page content, reproducing the unresolved retrieval problem. Confirmed that hiro.bballstatistics.com resolves through the project-specific Vercel CNAME with Cloudflare proxying disabled, has a working TLS certificate, and serves the generated journal endpoints. The first production retrieval workflow made 66 requests successfully except for the journal sitemap content checks; it proved that user-agent and query-string retrieval works on the DNS-only subdomain. Decisions and reasoning Use one Vercel deployment with hostname-conditioned routing rather than duplicate the journal content in a second repository or build pipeline. Use exact host-conditioned routes for the two paths that collide with physical NBA build files, while retaining the catch-all rewrite for every non-colliding journal path. Serve journal URLs at the subdomain root so the public address is concise and independent of the NBA site's /hiro path. Preserve old public journal URLs with permanent redirects once the new hostname is live. Keep the journal public and unlisted with noindex metadata, while retaining direct AI discovery endpoints and not blocking the journal in robots.txt. Recommend a DNS-only Cloudflare CNAME for the new subdomain so requests reach Vercel without passing through the Cloudflare proxy implicated in the unresolved AI retrieval failure. Validation and evidence Hiro timestamped-entry tests: passed — The journal source identity and same-day preservation tests passed with this entry included. Generated journal validation: passed — The generator produced 56 journal pages and passed timestamp schema, unique identity, subdomain routing, filesystem-override, legacy redirect, homepage separation, aliases, sitemap, Atom, noindex, and IndexNow checks. Frontend production build: passed — TypeScript compilation and the Vite production build completed successfully with this entry and all migration changes included. Live hiro.bballstatistics.com retrieval: passed — DNS and TLS are active. After correcting the root and sitemap routing collisions, the production validator completed 67 requests with zero failures across browser, command-line, generic-bot, and ChatGPT-style user agents. It verified the journal homepage identity, content types, sitemap dates, discovery files, feed, dated entries, latest aliases, JSON schemas, and query-string retrieval. Current state The main migration is deployed and hiro.bballstatistics.com is reachable through DNS-only Cloudflare configuration and Vercel TLS. The NBA opening page no longer contains a Hiro journal link, and old apex and www /hiro URLs permanently redirect to the new hostname. Generated journal content remains under the deployment's internal /hiro directory and is presented at root-relative subdomain URLs by Vercel routing. The journal index and sitemap precedence corrections are deployed, and the production retrieval workflow is green. Next steps Keep the strengthened production retrieval workflow enabled for future deployments. Monitor the DNS-only Hiro hostname and revisit Cloudflare proxying only after the external AI retrieval incompatibility is demonstrably resolved. Continue publishing future timestamped journal entries at the subdomain root without restoring an NBA homepage navigation link. Continue using DNS-only mode for the Hiro subdomain while the Cloudflare-proxy incompatibility remains unresolved.

Hard-excluding USPS and sign-in notices

2026-07-26T19:44:31-07:00

Executive summary Hiro now applies two deterministic mailbox rules before local Qwen sees a conversation: USPS-originated mail is always excluded, and automated sign-in or login notices are excluded from task and reading creation. The rules override the normal Important and self-sent eligibility channels. Excluded conversations receive durable rule-excluded source records so later scans advance without repeatedly presenting them to the model. Automated cleanup is kept separate from owner preference learning. Soft-removing a USPS or sign-in item does not become a negative Qwen example. A live reconciliation found eight existing tasks that violated the new rules: five USPS-derived tasks and three sign-in-notice tasks. All eight were soft-removed with audit and source evidence retained; no reading items required removal. Hiro was restarted and the private task page now discloses the exclusions alongside the existing self-sent and durable-reading rules. Work completed Deterministic source rules (Completed) Added sender-based USPS detection for usps.com addresses and United States Postal Service sender identities. Added subject-based detection for sign-in, login, and logged-in notices. Attached an explicit rule reason to each Gmail conversation record before classification. USPS and sign-in rules take precedence over Important, self-sent, task, and reading decisions. Pre-model exclusion and forward progress (Completed) The importer persists excluded mail as rule_excluded with the exact reason and source provenance. Rule-excluded conversations are removed from pending Qwen batches and cannot create a task or reading item. Background jobs count rule exclusions as processed progress and continue to older eligible mail even when an entire retrieval batch is excluded. Historical self-sent and Important-and-Unread synchronization phases also skip rule-excluded records. Historical reconciliation (Completed) Added reconciliation across active Gmail-derived tasks and reading items. Matching tasks are soft-removed without invoking the owner-removal learning hook. Matching reading items would be soft-removed under the same rule, although none matched in the live state. Source evidence is updated with the rule reason and retained for audit and deduplication. Learning isolation and interface (Completed) Excluded rule-driven deletions from the compact owner-rejection query used by Qwen. Verified that the owner-removal example count remained unchanged after live policy cleanup. Added job counters for new rule exclusions and policy-cleaned tasks and reading items. Updated the Gmail dialog to state that USPS and automated sign-in notices are excluded before Qwen. Decisions and reasoning Implement these as deterministic policy rather than examples because the user described universal rules. Use sender identity for USPS to avoid excluding unrelated personal tasks that merely mention postal service activity. Use subject-level sign-in and login detection to target automated authentication notices. Persist exclusions so repeated scans do not waste local-model time. Soft-remove historical violations so audit history and recoverability are retained. Never mix automated policy cleanup into owner-driven false-positive learning. Validation and evidence Focused automated regression suite: passed — Twenty-four tests passed. New coverage verifies USPS and sign-in recognition, Qwen bypass, durable skip reasons, historical task and reading cleanup, learning isolation, and background forward progress alongside existing Gmail, self-sent, reading-state, tag, date, persistence, and API behavior. Live historical reconciliation: passed — The live list contained 122 tasks and 53 reading items before cleanup. Reconciliation soft-removed eight tasks: five USPS-derived and three sign-in notices. The resulting state contained 114 tasks and 53 reading items. Learning isolation audit: passed — Eight rule-excluded source records were present after cleanup, while the compact explicit owner-removal example count remained eight. Restarted private runtime: passed — Hiro restarted successfully and the private remote task page returned status 200. Private interface disclosure: passed — The remote Gmail dialog contained the USPS and sign-in pre-Qwen exclusion explanation and retained Mark read and Mark unread controls. Task-page JavaScript syntax: passed — The embedded task-page script parsed successfully after the rule status and disclosure changes. Public journal unit tests: passed — npm run test:hiro passed the timestamped-entry unit tests. Public journal production build: passed — npm run build generated and validated 55 journal pages, then completed the TypeScript and Vite production build. Current state USPS mail never reaches Qwen and cannot create tasks or reading items. Automated sign-in and login notices never reach Qwen and cannot create tasks or reading items. Important and self-sent eligibility continues for all other Gmail conversations. Captured reading remains durable and can be marked Read or Unread in Hiro. Explicit owner task removals continue to teach Qwen; rule cleanup does not. Gmail remains read-only and no mailbox state was changed. Next steps Continue reviewing tasks and remove genuine false positives to refine Qwen examples. Report any authentication notice whose subject avoids the current sign-in and login wording so the deterministic pattern can be extended. Add other universal sender exclusions as explicit rules rather than relying on model learning.

Making reading state durable and self-sent mail mandatory

2026-07-26T19:12:52-07:00

Executive summary The Gmail productivity rules were refined again: Important remains the normal outer gate, while email sent from the owner to the owner is an explicit exception that must always become exactly one task or reading item. Unread is now an intake condition rather than a retention condition. Important-and-Unread conversations are captured into the reading list, but later Gmail read-state changes no longer remove them. The reading interface now uses explicit Mark read and Mark unread actions. A captured item retains its source, summary, tags, and audit history while its Hiro reading status changes. Hiro reconciled historical self-sent mail from the last thirty days. An aggregate exclusivity check found sixty-three self-sent sources: fifty-three linked to tasks, ten linked to reading items, zero linked to both, and zero linked to neither. The complete live background scan remained bounded and resilient. It reviewed 146 newly eligible conversation versions and continued past seven isolated classification failures. Work completed Eligibility model (Completed) Replaced the single Important-only search with a server-built Gmail scope representing Important OR owner-to-owner self-sent mail, followed by the user date and spam and trash constraints. Resolved the mailbox owner address through the Gmail profile and detected self-sent messages only when Gmail marks the message Sent and the owner address appears in both From and To. Each fetched conversation now records Important, Important-and-Unread, self-sent, and overall processing eligibility as separate machine facts. Non-important mail remains excluded unless it satisfies the exact self-sent exception. Mandatory self-sent classification (Completed) Added self_sent to the structured local-Qwen input and instructed the model that self-sent mail may never be ignored. Added a deterministic fallback: if bounded and individual model attempts cannot decide, a self-sent conversation with links becomes reading and one without links becomes a task. Removed the former requirement that reading mail contain an external article URL because the email conversation itself is the reading object. Added historical reconciliation for self-sent conversations already recorded as ignored. Explicitly deleted task or reading records remain respected and are not recreated. Important-and-Unread synchronization skips a self-sent source already represented by a task, preserving exactly-one semantics. Durable reading state (Completed) Stopped background jobs from removing captured reading items merely because Gmail later loses the Unread label. Important-and-Unread remains the intake feed for ordinary reading capture. Reading items link back to their Gmail conversation and keep their existing status during subsequent synchronization. Renamed the reading completion state to Read and added Mark read and Mark unread controls in the task-page interface. Live reconciliation and runtime (Completed) Restarted Hiro with the revised query, record metadata, background phases, and reading controls. Validated the combined Gmail query with read-only Gmail count requests before starting reconciliation. Ran one complete server-owned background job. The primary phase reviewed 146 conversation versions; historical self-sent reconciliation added thirty-four tasks and eight reading items beyond outcomes already created in the primary phase. The captured Important-and-Unread phase ensured forty intake items and reported one tagging fallback after bounded retries. Verified the private remote page serves the self-sent explanation plus Mark read and Mark unread controls. Decisions and reasoning Interpret Unread as capture eligibility, not automatic deletion eligibility. Keep reading status local to Hiro so the user can mark a captured item Read or Unread without changing Gmail. Treat owner-to-owner email as intentional capture and require exactly one task or reading result. Respect explicit owner deletion even for self-sent mail; a removed item remains deleted and contributes task feedback where applicable. Use Gmail conversation links for reading entries rather than arbitrary URLs extracted from message bodies. Continue through isolated model failures and use deterministic self-sent fallback only when bounded Qwen retries fail. Validation and evidence Focused automated regression suite: passed — Twenty-one tests passed, covering self-sent detection, mandatory non-ignore behavior, historical ignored-source conversion, Important-or-self query construction, persistent reading state, Important-and-Unread intake, Read and Unread UI syntax, removal learning, reconciliation, bounded Qwen batches, dates, tags, persistence, jobs, and API behavior. Read-only Gmail query validation: passed — Important, self-sent, and combined thirty-day Gmail queries all returned bounded result metadata without changing mailbox state. Complete live background job: passed with isolated errors — The job reviewed 146 newly eligible conversation versions in thirteen safe retrieval batches. It analyzed 139, created eighty-seven task outcomes and five reading outcomes in the primary phase, ignored forty-seven eligible non-self conversations, and reported seven isolated failures without stopping. Historical self-sent reconciliation: passed — The reconciliation phase created thirty-four tasks and eight reading items with zero self-sent classification failures. Self-sent exclusivity audit: passed — Sixty-three self-sent source records were active: fifty-three had a task, ten had a reading item, zero had both, and zero had neither. Reading intake state: passed — Important-and-Unread synchronization ensured forty captured intake conversations. The full reading list contained fifty-three items after adding self-sent reading outcomes, all in Review before owner action. Private remote interface: passed — The private remote task page returned status 200 and contained the self-sent rule, Mark read, and Mark unread controls. Task-page JavaScript syntax: passed — The embedded task-page script parsed successfully after the reading-state and status-reporting changes. Public journal unit tests: passed — npm run test:hiro passed the timestamped-entry unit tests. Public journal production build: passed — npm run build generated and validated 54 journal pages, then completed the TypeScript and Vite production build. Current state Normal Gmail processing is limited to Important mail from the selected date window, with owner-to-owner self-sent mail as the only additional source. Every known self-sent conversation is represented by exactly one task or reading item unless the owner explicitly removed it. Important-and-Unread mail is captured for reading, then remains in Hiro when Gmail becomes Read. Reading items can be marked Read or Unread in Hiro without mutating Gmail. Task removals continue to provide compact local negative examples for later Qwen decisions. Gmail remains read-only throughout all scans and reconciliation. Next steps Review the new task outcomes and remove false positives so the local negative-example set becomes more representative. Use Mark read and Mark unread directly from the reading list as items are consumed. Consider adding incremental phase progress for self-sent and reading synchronization if future scans are large. Retry the seven isolated failures during a later Analyze run after additional removal feedback has refined task decisions.

Teaching task rejection and enforcing Gmail importance

2026-07-26T18:39:15-07:00

Executive summary The Gmail productivity workflow now uses Gmail importance as a hard outer gate. Non-important conversations never reach local Qwen or participate in task or reading creation. Task analysis applies the date-window query only within Gmail Important. The reading list is a deterministic narrower mirror of conversations that are both Important and Unread. Removing a Gmail-imported task now records an owner rejection on its durable source. Future classifications receive compact recent examples of deleted imported tasks as negative evidence, including historical soft-deletions created before this change. A live audit found that the previous broad scan had created forty-nine reading entries from mail outside the intended Gmail section. Those entries were soft-removed with audit and source history retained, then replaced by the forty conversations actually in Important and Unread. Hiro was restarted with the corrected policy. The private remote page now shows the Important-only query and explains the separate task and reading scopes. Work completed Owner-removal learning loop (Completed) Enhanced task soft-deletion to identify Gmail provenance before removal. Owner-deleted imported tasks mark their source as rejected-task feedback and retain the removed task wording locally in source evidence. The Qwen classifier receives up to eight recent removed-task examples with bounded excerpts and instructions to treat them as negative evidence unless a new conversation clearly presents a different unresolved action. Historical deleted Gmail tasks are also selected as feedback, so learning includes removals that occurred before the new deletion hook. Policy-driven removals for non-important mail are explicitly excluded from owner-preference learning. Hard Gmail importance gate (Completed) Every task-import query is server-enforced to include Gmail is:important, even if the browser query omits it. The default dashboard query visibly includes is:important with the thirty-day window and spam and trash exclusions. Added reconciliation for active imported tasks: any source no longer marked Important is soft-removed without becoming negative preference feedback. The one remaining active imported task passed the live Important-state reconciliation. Correct reading-list semantics (Completed) Separated reading eligibility from LLM classification. Gmail Important plus Unread is now the deterministic membership rule. Reading items link to the Gmail conversation itself instead of extracting arbitrary article links from broad mail. Local Qwen supplies summaries and applicable tags for eligible reading conversations but cannot add a noneligible conversation. Added reconciliation that soft-removes Gmail reading items outside Important and Unread and preserves manual reading items. The corrected live sync established forty active reading items corresponding to the forty current Important-and-Unread conversations. Reliability and runtime (Completed) Background jobs reconcile task and reading eligibility before and after bounded task analysis as appropriate. Job status now reports policy-removed tasks, policy-removed reading items, and the final Important-and-Unread reading-list count. Reduced negative-example prompt size after the first full reading-tag sync showed that oversized correction context could exhaust reliable local-model classification. A subsequent non-persisting one-conversation Qwen check succeeded with all eight historical owner-removal examples and returned a valid kind and tag set. Restarted Hiro and verified the private remote task page serves the corrected policy and query. Decisions and reasoning Treat Gmail IMPORTANT as mandatory input eligibility rather than an LLM feature or suggestion. Use IMPORTANT plus UNREAD as the complete reading-list membership rule; Qwen may describe and tag eligible mail but may not decide membership. Learn only from explicit owner task removals, not automated policy reconciliation. Keep feedback local in SQLite and send only compact recent examples to the local Qwen model. Soft-remove incorrect imported items so audit and source provenance remain recoverable. Link reading entries to their Gmail conversations because the email itself is the reading object. Validation and evidence Focused automated regression suite: passed — Eighteen tests passed, covering the enforced Important query, Important-and-Unread label detection, task and reading reconciliation, owner-removal feedback, historical feedback retrieval, policy-removal exclusion, email reading links, multi-tag sync, background jobs, bounded Qwen batches, individual fallback, dates, persistence, and API behavior. Live mailbox eligibility audit: passed — The mailbox reported forty Important-and-Unread conversations. Forty-nine previously imported reading items were outside that set and were soft-removed; the corrected list contains forty eligible Gmail conversations. Live task reconciliation: passed — The remaining active imported task was confirmed to have an Important Gmail source. Eight historical owner-deleted imported tasks were available as negative examples. Non-persisting local-model check: passed — One eligible Important conversation was classified read-only with all eight compact owner-removal examples; Qwen returned a valid classification and validated tag output without creating an item. Restarted private runtime: passed — Hiro restarted successfully. The private remote task page returned status 200, showed the Important-only query, and served the hard-gate explanation. Task-page JavaScript syntax: passed — The embedded task-page script parsed successfully after the updated query, removal explanation, and job-status fields. Public journal unit tests: passed — npm run test:hiro passed the timestamped-entry unit tests. Public journal production build: passed — npm run build generated and validated 53 journal pages, then completed the TypeScript and Vite production build. Current state Only Gmail conversations marked Important can participate in task analysis. The default task scope is Important mail from the last thirty days, excluding spam and trash. The reading list contains forty current Important-and-Unread Gmail conversations and no broad-mail article extractions. One imported task remains active and its source is Important. Eight prior explicit task removals are available as local negative feedback for subsequent scans. Gmail remains read-only; no labels, read state, archive state, or message data were changed. Next steps Use the Review section to remove any remaining false task; each explicit removal becomes another negative example. Run Analyze later as needed; the server will enforce Important-only scope even if the visible query is edited. Review the forty Important-and-Unread reading entries and adjust tags when necessary. Consider adding an explicit false-positive reason selector only if simple removal feedback proves too ambiguous.

Turning Gmail review into one complete background scan

2026-07-26T18:03:32-07:00

Executive summary The earlier pagination repair exposed the next twelve unanalyzed conversations, but it still required the user to click Analyze once per twelve-conversation group. Analyze now starts one server-owned background job that continues through the complete Gmail query until no eligible conversations remain. Twelve remains an internal safety batch size for local Qwen 9B and is no longer a user workflow limit. The task dashboard polls compact job status and displays cumulative conversations reviewed, task and reading imports, ignore decisions, model failures, and completion state. Closing the dialog or losing the phone connection does not stop the server job. The job retains the existing three-conversation Qwen request limit and individual-conversation fallback. A conversation that still cannot be classified is recorded in the job total and excluded from the current run so the remaining period continues. Hiro was restarted and the private remote task page now serves the full-scan interface and background-job API. The real mailbox scan was intentionally not started during validation. Work completed Background scan orchestration (Completed) Added one active Gmail import job per task-list owner with queued, running, completed, completed-with-errors, authorization-required, and failed states. The background worker repeatedly requests the next internal batch until the Gmail query is exhausted. Cumulative progress includes batches, processed and analyzed conversations, imported tasks, imported reading items, ignores, failures, and conversions from prior ignore decisions. A duplicate Analyze request while a job is active returns the current job instead of launching competing model work. Forward progress and failure isolation (Completed) Extended the batch importer with transient exclusions used only within a background scan. Normal scans temporarily exclude conversations that fail both batch and individual Qwen classification, preventing one difficult conversation from creating an infinite retry loop. Ignored-item reanalysis temporarily excludes every source already handled in that job because an unchanged ignore remains eligible in persistent storage. The worker stops safely if an importer response cannot demonstrate forward progress. Remote progress experience (Completed) Changed the Gmail dialog copy to state that the complete query is reviewed in safe background batches. Analyze and Reanalyze ignored now launch background jobs rather than holding one long browser request. The dialog polls current status every two seconds and refreshes task and reading counts at completion. Reopening the dialog reconnects to the current server job, including from the private phone-access page. Runtime deployment (Completed) Restarted Hiro with the new job module and routes. Verified the private remote page serves the complete-query copy and progress code. Verified the API exposes job start, current-job, and job-status endpoints and reports no job before the user initiates the scan. Decisions and reasoning Keep twelve as the internal retrieval batch and three as the maximum structured Qwen request size because those bounds have passed prior reliability testing. Move long-running orchestration to Hiro so browser disconnects do not cancel the scan. Allow at most one active Gmail scan per owner to avoid duplicate imports and competing local-model load. Continue through isolated model failures and report them at completion instead of repeatedly retrying the same conversation or stopping the remaining scan. Do not start a full live mailbox scan as part of deployment validation because classification creates Review items and should begin from an explicit user action. Validation and evidence Focused automated regression suite: passed — Eight tests passed. Coverage includes multi-batch completion, cumulative totals, continued scanning after an isolated classification failure, transient exclusion during ignored-item reanalysis, task and reading persistence, multiple tags, due dates, bounded Qwen batches, individual fallback, API round trips, and incremental source handling. Task-page JavaScript syntax: passed — The embedded task-page script parsed successfully after the background polling changes. Restarted Hiro runtime: passed — Hiro restarted in approximately 2.3 seconds and returned the task page normally. Private remote page: passed — The private remote task page returned status 200 and contained the complete-query explanation and cumulative progress behavior. Background job API availability: passed — The live OpenAPI contract exposed the job start route, and the current-job route returned an empty state before user initiation. Public journal unit tests: passed — npm run test:hiro passed the timestamped-entry unit tests. Public journal production build: passed — npm run build generated and validated 52 journal pages, then completed the TypeScript and Vite production build. Current state One Analyze click reviews the entire entered Gmail query, including the default thirty-day period. The user can close the import dialog and later reopen it to see the active job status. Tasks and reading items are still created in Review with Qwen-selected applicable tags and source-derived due dates. Gmail remains read-only and the actual full scan has not been started during this deployment session. Next steps Initiate Analyze once from the refreshed task page and allow the background job to complete the thirty-day query. Review created tasks and reading items after completion, correcting tags or wording where necessary. Persist job progress to SQLite if restart-resilient live status becomes important beyond the existing source-level resume behavior. Add pause and cancel controls only if real full-period runs show a practical need.

Advancing Gmail import beyond previously analyzed mail

2026-07-26T17:53:26-07:00

Executive summary The task dashboard correctly reported that all eleven conversations on the first Gmail result page had already been analyzed, but the importer stopped there instead of continuing to older matching mail. The importer now paginates through Gmail search results and returns the next requested set of conversation versions that Hiro has not previously handled. A separate Reanalyze ignored action safely revisits only prior ignore decisions. Existing tasks and reading items are excluded from reanalysis, while an ignored source can be converted into a reviewed task or reading item without duplicating its source record. A read-only live check confirmed that twelve distinct unanalyzed conversations are now reachable beyond the eleven already recorded. No new mail was classified or persisted during that check. Hiro was restarted and both the local and private remote task pages now serve the updated controls and API contract. Work completed Root-cause diagnosis (Completed) Inspected aggregate source state and confirmed eleven Gmail conversation versions were already recorded. Checked Gmail search pagination metadata and found substantially more matching results plus a continuation page. Traced the zero-attempt result to a first-page-only fetch that discarded known conversations without advancing to the next page. Incremental pagination (Completed) Changed Gmail retrieval to follow stable continuation tokens, deduplicate threads, and scan forward until the requested number of eligible conversation versions is collected. Normal Analyze excludes every existing Gmail source before classification, allowing repeated runs to advance instead of repeatedly reporting the same first page. Bounded the scan and requested batch size so Gmail reads remain controlled while supporting the current query volume. Safe reanalysis (Completed) Added an explicit reanalyze_ignored request option and dashboard control. Reanalysis selects only sources whose current classification is ignore; sources already linked to tasks or reading items are protected. When Qwen changes an ignore decision to task or reading, Hiro updates the same source evidence, creates one reviewed item, preserves all applicable validated tags, and reports the conversion count. Dashboard feedback and runtime (Completed) Clarified that Analyze moves past conversations already handled. Separated normal analysis and ignored-item reanalysis status messages and removed the misleading first-page skipped count from the primary result summary. Restarted Hiro with the updated backend and verified the private remote page serves the new UI. Decisions and reasoning Keep normal Analyze strictly incremental so previously evaluated mail does not consume the model budget. Make reanalysis an explicit user action because it may create new reviewed items from past ignore decisions. Never reanalyze or overwrite an existing imported task or reading item through the ignored-item path. Retain Gmail as a read-only source; this work does not label, archive, delete, or otherwise mutate mailbox data. Validate live pagination with a read-only fetch and leave the newly reachable conversations untouched until the user initiates analysis. Validation and evidence Focused automated regression suite: passed — Six tests passed covering task, reading, and ignore classification; multiple tags; due-date extraction; incremental behavior; three-conversation model batches; individual fallback; pagination handoff; safe ignore conversion; and protection from repeated reanalysis. Read-only live Gmail pagination: passed — With eleven known conversation versions excluded, retrieval found twelve distinct next unanalyzed conversation versions. The check performed no classification or persistence. Restarted API and dashboard: passed — Hiro restarted in approximately 1.7 seconds. The task page returned status 200, the API schema exposed reanalyze_ignored, and the dashboard contained the new explanatory copy and Reanalyze ignored control. Private remote access: passed — The private remote task URL returned status 200 and served the updated controls. Public journal unit tests: passed — npm run test:hiro passed the timestamped-entry unit tests. Public journal production build: passed — npm run build generated and validated 51 journal pages, then completed the TypeScript and Vite production build. The first sandboxed Vite attempt was blocked by filesystem permissions; the required rerun outside that restriction passed. Current state Analyze now advances to the next unanalyzed Gmail conversations instead of stopping at the eleven previously recorded results. Reanalyze ignored is available beside Google reauthorization. The existing source store contains prior ignore and reading decisions; no additional live classifications were created during this repair. Hiro is running with local Qwen 9B as the classifier and Gmail remains unchanged. Next steps Use Analyze once from the refreshed page to classify the twelve newly reachable conversations. Use Reanalyze ignored if the earlier nine ignore decisions should be reconsidered under the current prompt. Review any created tasks and reading items, especially their multiple tags and extracted due dates. Use corrections from Review to refine the classifier before enabling direct, automatic insertion or recurring imports.

Making full-size Gmail analysis reliable

2026-07-26T17:42:38-07:00

Executive summary A screenshot from the private task dashboard showed that the corrected label-free Gmail importer still failed when the default Analyze action processed a larger set of full conversations. The controlled three-conversation run had succeeded, isolating the problem to the size of the single structured Qwen request rather than Gmail access, authorization, tag validation, or item persistence. Hiro now limits each local Qwen request to three conversations. If a three-conversation batch fails, those conversations are retried individually so one difficult thread cannot discard the rest of the import. The dashboard now reports attempted, successfully analyzed, imported, ignored, previously analyzed, and failed counts. The same default live Analyze action completed successfully after the fix: eight remaining conversations were analyzed, one tagged reading item was added, seven were ignored, three prior conversations were skipped, and no classifications failed. Work completed Failure isolation (Completed) Used the dashboard screenshot to identify the exact user-visible failure message. Compared the failing default-size request with the previously successful three-conversation live request. Confirmed that full transcripts and a multi-item JSON schema were exceeding Qwen's reliable single-request classification envelope. Bounded model batches (Completed) Limited structured Qwen classification requests to three Gmail conversations each. Retained one schema-constrained response per batch, including task-versus-reading-versus-ignore classification, title, notes, priority, reason, and all applicable validated tags. Added single-conversation fallback when a batch fails. Successful batches and fallback classifications continue through persistence even if a different conversation cannot be classified. Transparent import results (Completed) Added attempted and failed counts to the import response. Updated the dashboard result message to distinguish new analysis from previously analyzed conversations. The UI now reports partial failures instead of reducing an otherwise useful import to one generic error. Decisions and reasoning Use three conversations as the maximum local Qwen classification batch because that size passed controlled and live validation. Retry a failed multi-conversation batch one conversation at a time before reporting failure. Preserve multiple applicable tags on every successful task or reading classification. Return partial success details rather than rolling back unrelated successful classifications. Continue using incremental source identifiers so completed analysis is not repeated. Validation and evidence Focused importer and productivity tests: passed — Thirteen tests passed, including three-item batch limits, individual fallback after batch failure, partial-failure reporting, multiple tags, task and reading classification, ignore decisions, dates, authorization, persistence, and deduplication. Default live Analyze action: passed — Eleven recent conversation versions were found. Eight remaining versions were analyzed in approximately 26.8 seconds; one tagged reading item was created, seven conversations were ignored, three were already analyzed, and zero failed. Repeat default import: passed — All eleven conversation versions were skipped without model work in approximately 3.4 seconds, with no duplicates or failures. Private dashboard: passed — The task page returned status 200 and served the updated import result reporting. Current state Hiro is running with bounded Qwen classification batches. The default Gmail Analyze action completes successfully on the current recent-mail set. Imported reading items retain validated tags and enter Review. Gmail remains read-only and no mailbox state is changed. Next steps Add a background import job with per-batch progress so longer scans visibly advance. Review the newly imported reading item and correct its tags or wording if necessary. Use those corrections to refine the personal-role prompt and tag examples. Add the planned recurring import only after progress reporting and model-versus-human field separation are complete.

Replacing label import with Qwen email interpretation

2026-07-26T17:37:28-07:00

Executive summary The first Gmail importer incorrectly assumed that the mailbox owner would organize messages under dedicated task and reading labels. The original design document was re-read and confirmed a different model: scan recent conversations, let an LLM decide what still requires action, identify saved reading, ignore non-actionable mail, and retain the result locally. Hiro now scans recent non-spam Gmail conversations without requiring labels, includes the owner's sent replies as context, and asks local Qwen 9B to classify each conversation as task, reading, or ignore. The same Qwen classification assigns every applicable tag from Hiro's controlled tag set, plus task priority. Deterministic source parsing continues to extract due dates and horizon from the conversation. A controlled live run analyzed three conversations in approximately 7.4 seconds, created one tagged review reading item, ignored two non-actionable conversations, and changed no Gmail state. Work completed Workflow correction (Completed) Removed the Gmail-label requirement from the dashboard and import request. Changed the import dialog to explain that Qwen reads recent conversations and decides whether each belongs in tasks, reading, or neither. Added an advanced Gmail search query with a safe default covering recent mail while excluding spam and trash. Changed one import run to populate both task and reading sections instead of depending on the currently selected dashboard tab. Conversation ingestion (Completed) Gmail message search results are deduplicated by conversation before full threads are fetched. Up to eight recent messages from each conversation are assembled into a bounded transcript. Messages carrying Gmail's sent marker are explicitly identified as owner messages so the model can recognize actions already handled by a reply. Each analyzed conversation receives a versioned external identifier based on the conversation and its newest message, allowing new replies to be reconsidered while preventing immediate duplicates. Structured Qwen classification (Completed) The first free-form Qwen prompt consumed its response budget in hidden reasoning and produced no parseable classification. The classifier was moved to LM Studio's schema-constrained JSON output mode. The schema requires an exact classification, source-preserving identifier, title, concise notes, priority, reason, and a tag array restricted to Hiro's controlled tag vocabulary. Hiro accepts structured JSON from either the ordinary response channel or the model's reasoning channel because this Qwen build emits schema-constrained output through the latter. Tags are validated, normalized, deduplicated, and default to Other only when no specific allowed tag applies. Review-first persistence (Completed) Tasks and reading items created by Qwen enter Review rather than becoming silently active. Task due dates retain their source phrase and confidence, and due dates determine Current, Medium-term, Long-term, or Someday horizon. Ignored conversations are still recorded as analyzed sources so repeated imports do not spend model time on the same mailbox state. Gmail remains read-only: importing does not apply labels, mark messages read, archive, delete, or otherwise modify mailbox state. Decisions and reasoning Treat a Gmail conversation rather than an individual message as the unit of interpretation. Require local Qwen 9B for inbox interpretation instead of falling back to keyword-only task versus reading guesses. Allow multiple tags on every imported item and restrict model output to the user-approved tag set. Use schema-constrained generation rather than attempting to repair arbitrary model prose. Keep imports incremental by persisting ignore decisions and previously imported conversation versions. Limit the default import batch to twelve recent search results to bound Gmail reads, context size, and local inference time. Validation and evidence Focused importer and productivity tests: passed — Eleven tests passed, covering task, reading, ignore, multi-tag assignment, priority, due-date extraction, review-first state, incremental deduplication, authorization errors, and existing productivity behavior. Synthetic structured classification: passed — Qwen classified a scheduling request as a high-priority family task and a saved article as a low-priority AI reading item in under four seconds. Controlled live Gmail import: passed — Three recent conversations were analyzed in approximately 7.4 seconds; one tagged reading item was added to Review and two conversations were ignored. Repeat live import: passed — The same three conversation versions were skipped without model analysis in approximately 1.3 seconds, and no duplicate items were created. Private task dashboard: passed — The dashboard returned status 200 and exposed the corrected label-free import interface. Current state Hiro is running the label-free Qwen Gmail importer. One controlled live reading item is present in Review with a validated tag. Previously analyzed conversation versions are skipped on repeat imports. Gmail authorization remains read-only and mailbox state remains unchanged. Next steps Review the controlled reading item and correct its title, notes, or tags if needed. Run a larger bounded batch after confirming the first classification looks appropriate. Add a background import job with visible progress before enabling the planned recurring schedule. Implement thread-to-item refresh and completed-item auto-reopen semantics from the original design. Preserve model versus human fields separately before automatic re-analysis is enabled.

Restoring Gmail import network access

2026-07-26T17:26:13-07:00

Executive summary A reported loading problem was clarified to affect Gmail import rather than the task page itself. Direct API reproduction showed that the import failed immediately with a transport-layer error before any Gmail messages were read. The detached Hiro process had been started from a network-restricted development environment. Although its local pages worked, the inherited process context could not reach Google's Gmail API. Hiro was restarted with normal network access. Both import variants now return successfully in under two seconds, including the default path that requests local-model classification. Work completed Import-path reproduction (Completed) Called the same productivity import endpoint used by the task page without exposing message content. Confirmed a transport error occurred before message retrieval and independently of local-model classification. Distinguished the Gmail network failure from page availability, Google credential validity, and local Qwen response time. Runtime network restoration (Completed) Resolved and stopped only the process tree belonging to the restricted Hiro runtime. Restarted Hiro through the detached launcher with normal network access. Retained the fast-returning startup behavior while restoring outbound access to Google's Gmail API. Mailbox configuration check (Completed) Checked for the configured task and reading labels without reading email bodies. Neither configured label currently exists in the connected Gmail account. A successful task import therefore found zero matching messages and made no changes to the task list. Decisions and reasoning Treat a local page health check and external integration health as separate concerns. Start Hiro with normal network authority whenever Gmail or other external integrations are expected to work. Do not create Gmail labels without the account owner's explicit approval. Keep the local Gmail integration read-only; label creation, if approved, must use a separately authorized Gmail management surface. Validation and evidence Restricted-runtime import: failed — The import endpoint returned a transport error before reading Gmail messages, confirming the original failure. Post-restart import without local classification: passed — The import returned status 200 in approximately 1.1 seconds. Post-restart default import path: passed — The import returned status 200 in under one second with local-model classification requested. Configured Gmail labels: passed — The mailbox check completed successfully and confirmed that neither configured label currently exists. Current state Hiro is running with outbound Gmail access. The Gmail import endpoint is responsive and no longer returns a transport error. No messages were imported because the configured Gmail labels do not yet exist. No Gmail messages were modified, marked read, archived, or deleted. Next steps With account-owner approval, create the task and reading labels in Gmail. Apply a configured label to one test message and run a small import. Verify review-first ingestion, due-date extraction, tag inference, and duplicate prevention with that controlled message. Improve future integration health checks so a locally healthy server does not obscure missing outbound network access.

Checking a reported task-page connection failure

2026-07-26T17:15:54-07:00

Executive summary The private task page was reported as not loading shortly after Hiro's detached startup fix. Server-side checks found Hiro healthy: all expected listeners are active, and the task page returns status 200 through both the loopback and private-network interfaces. The private-network adapter retains its expected address, and Windows Firewall has an enabled inbound TCP allow rule for the Python runtime hosting Hiro. The evidence points to the requesting device's private-network connection or browser protocol handling rather than a Hiro application or server-listener failure. Work completed Runtime and listener inspection (Completed) Confirmed that the replacement Hiro process remains active after the detached launcher returned. Confirmed that the HTTPS, HTTP, and loopback-only internal ports are all listening under one server process. Confirmed that the local task page returns status 200 with the expected personal-list title. Private-network path inspection (Completed) Confirmed that the private-network adapter is active and retains the expected assigned address. Requested the task page through that private interface and received status 200 with the expected title. Confirmed that Windows Firewall is active and includes an enabled inbound TCP allow rule covering the Python runtime used by Hiro. Decisions and reasoning Do not restart a healthy Hiro process when both local and private-interface application checks pass. Do not change firewall policy without evidence of a server-side block. Ask the user to verify that the requesting device is connected to the private network and that the browser uses HTTP on the designated task-dashboard port. Validation and evidence Expected listeners: passed — All three expected Hiro listeners remain active. Local task page: passed — The loopback task page returned status 200 and the expected title. Private-interface task page: passed — The private-network task page returned status 200 and the expected title. Host firewall allowance: passed — An enabled inbound TCP allow rule applies to the Python runtime hosting Hiro. Current state Hiro is running and serving the task page. The host can reach the task page through both local and private-network addresses. No application restart or configuration change was required during this diagnosis. Next steps Confirm that the requesting device's private-network client shows connected. Enter the full HTTP address explicitly so the browser does not upgrade it to HTTPS. If the page still fails, capture the exact browser error and identify the device being used so the remaining client-side path can be isolated.

Making Hiro startup return promptly

2026-07-26T17:13:34-07:00

Executive summary Hiro's prior manual startup command successfully launched the service but remained attached to the long-running process tree, making the command appear stuck even after the dashboard was available. A dedicated detached Windows launcher now starts Hiro with closed standard handles, a new process group, no console window, and an explicit job-breakaway flag. The launcher normalizes the inherited Windows search path before creating the child process, waits only for the task page to become ready, and then returns structured startup information. A real cold restart returned in approximately three seconds end to end, with Hiro ready in approximately 1.2 seconds. An idempotent second invocation returned in under 0.2 seconds without starting a duplicate. Work completed Startup diagnosis (Completed) Compared the interrupted launcher call with the resulting listeners and startup logs. Confirmed that Hiro had reached application-ready state and was serving the task page while the initiating command still appeared active. Identified descendant-process tracking and inherited process attachment as the cause of the apparent hang rather than slow application initialization. Detached launcher (Completed) Added a checked-in Python launcher and a small Windows command wrapper using Hiro's pinned runtime. The launcher removes duplicate case variants of the search-path variable and rebuilds one canonical Path value from the machine and user configuration. The child starts with no standard input, file-backed output streams, closed inherited handles, no console window, a new process group, and an explicit Windows job-breakaway flag. Startup is bounded by a configurable timeout and returns only after the task page health check succeeds. The launcher detects an already-running healthy instance and returns immediately instead of spawning another process. Operational restart (Completed) Stopped only the process owning Hiro's expected listeners and restarted it through the new launcher. Confirmed that the replacement process owns the expected HTTPS, HTTP, and loopback-only internal listeners. Confirmed that the private-network task page returned status 200 and the expected page title. Decisions and reasoning Keep the existing blocking runner for workflows that intentionally supervise Hiro, such as nightly preflight. Use the new detached launcher for interactive and agent-triggered startup where the caller must return promptly. Wait for an application-level task-page response rather than treating process creation or an open port alone as readiness. Make startup idempotent and fail clearly if expected ports are occupied by something other than a healthy Hiro instance. Validation and evidence Launcher and compatibility tests: passed — Six focused tests passed, covering the detached launcher configuration, search-path normalization, pinned runtime, server ports, and nightly preflight compatibility. Cold detached startup: passed — The launcher returned successfully in approximately three seconds end to end and reported application readiness in approximately 1.2 seconds. Idempotent repeat startup: passed — A second invocation returned already_running in approximately 0.18 seconds and did not create a duplicate listener. Private-network task page: passed — The task page returned status 200 with the expected Hiro personal-list title. Current state Hiro is running through the detached startup path. Interactive startup commands now return promptly after application readiness. The existing blocking runner remains available for intentional supervision workflows. The private task dashboard is responding normally. Next steps Use the detached command wrapper for future manual or agent-triggered Hiro starts. Address the scheduler data-encoding warning in a separate scoped maintenance session. Consider connecting the detached launcher to a supervised automatic-start mechanism if runtime availability needs to survive logoff or reboot.

Restoring Hiro task-dashboard availability

2026-07-26T17:09:55-07:00

Executive summary Hiro was started after the task dashboard was found offline. The checked-in Windows launcher was used with a normalized process search path to avoid the known Path and PATH environment collision. The HTTP task dashboard is responding successfully on both the local loopback address and the private Tailscale address. Work completed Runtime startup (Completed) Confirmed that Hiro's three expected service ports were initially not listening. Started the checked-in Hiro launcher as a hidden Windows process while preserving the inherited runtime environment. Confirmed that one Hiro process owns the HTTPS, HTTP, and loopback-only internal listeners. Dashboard availability (Completed) Requested the personal task page through the local HTTP listener and received a successful response. Requested the same page through the private Tailscale interface and received a successful response. Both responses identified the expected Hiro personal-list page. Decisions and reasoning Use the checked-in launcher instead of constructing an alternate startup command. Normalize the process-scoped Windows search path before spawning Hiro so the bundled Python environment remains available. Keep the internal service listener restricted to loopback while exposing the intended dashboard listeners. Validation and evidence Expected listeners: passed — Hiro is listening on its HTTPS, HTTP, and loopback-only internal service ports. Local task dashboard: passed — The local HTTP task page returned status 200 and the expected page title. Tailscale task dashboard: passed — The private Tailscale HTTP task page returned status 200 and the expected page title. Current state Hiro is running. The personal task dashboard is available locally and through the private Tailscale network. No application source or user data changed during this operational restart. Next steps Use the task dashboard normally and report any import or rendering issue that appears. Consider configuring a supervised automatic startup mechanism if manual restarts become frequent.

Making Google authorization recoverable in Hiro

2026-07-26T15:02:05-07:00

Executive summary Hiro now recognizes rejected or malformed Google credentials as a recoverable authorization condition instead of exposing a generic Gmail RefreshError. When invalid credentials are encountered, Hiro automatically starts one interactive Google consent request, preserves the rejected token as a recoverable timestamped backup, and returns structured status to the task dashboard. The task dashboard now explains the authorization state, offers an explicit Reauthorize Google action, waits for consent to complete, and retries the Gmail import after authorization succeeds. The live recovery flow completed successfully, and a metadata-only Gmail API request confirmed that the replacement authorization works. Work completed Shared Google authorization service (Completed) Added a shared credential loader for Gmail and Google Calendar so both integrations use the same refresh, recovery, and token-storage behavior. The loader first accepts a valid saved authorization, then attempts a normal refresh, and converts rejected or malformed credentials into an actionable reauthorization requirement. A single guarded background consent flow is reused while active, preventing repeated Gmail requests from opening multiple authorization windows. Successful consent replaces the token atomically. The rejected token is copied to a timestamped local backup before the consent request begins. API and dashboard recovery experience (Completed) Added API endpoints for requesting Google reauthorization and reading its current state. Gmail import now returns a structured unauthorized response with a stable error code, human-readable explanation, and current authorization state when credentials cannot be refreshed. Added a Reauthorize Google action to the Gmail import dialog. The dashboard polls authorization state and automatically retries an interrupted import after fresh consent succeeds. Live authorization handoff (Completed) Triggered the new reauthorization endpoint through the live task dashboard. Hiro reported that it is waiting for user authorization and created one recoverable backup of the rejected token. Google's interactive consent page was delegated to the computer's default browser and completed successfully. A read-only Gmail profile request verified the replacement credential without printing account information or reading messages. Decisions and reasoning Keep the integration read-only by requesting only Gmail read-only and Calendar read-only scopes. Treat invalid_grant and malformed saved credentials as recoverable authorization conditions, not generic import failures. Start reauthorization automatically when invalid credentials are encountered, while retaining an explicit dashboard action for clarity and manual recovery. Run consent asynchronously so Hiro remains responsive and the dashboard can display progress. Preserve rejected authorization material locally for recovery, but never expose its contents through the API, dashboard, tests, logs, or public journal. Validation and evidence Focused Google authorization and productivity tests: passed — Twelve tests passed, covering automatic recovery on rejected refresh, atomic token replacement, recoverable token backup, actionable API mapping, Gmail import behavior, and existing productivity routes. Live task dashboard: passed — The Gmail import dialog loaded with the new Reauthorize Google action and displayed the waiting-for-authorization message after the request. Live authorization state: passed — The status endpoint reported waiting_for_user, confirmed that a backup was created, and exposed no credential values. Fresh Gmail API authorization: passed — Hiro reported authorized and a metadata-only Gmail profile request completed successfully without exposing account information. Public journal validation: passed — The Hiro timestamped-entry unit tests passed, generated-journal validation passed for 44 entries, and the production site build completed successfully. Current state Hiro is running with the new recovery code. Google authorization completed successfully and the replacement credential is active. Invalid saved credentials will now trigger the same single-window reauthorization path automatically. The previous rejected token has been preserved locally as a recoverable backup. Next steps Retry the labeled Gmail import and confirm that review-first ingestion and duplicate prevention work with live data. Add recurring tasks and reminders in a later task-list iteration as previously planned.

Diagnosing Hiro's Gmail authorization failure

2026-07-26T14:53:53-07:00

Executive summary The personal task dashboard's Gmail import returned a RefreshError during its first live authorization attempt. The local OAuth configuration was inspected without exposing credentials. The credential and token files both exist, the saved authorization has the required read-only Gmail and Calendar scopes, a refresh token is present, and the OAuth client identities match. A direct refresh request to Google's token service returned invalid_grant. This identifies the stored Google authorization as revoked or otherwise no longer renewable rather than a task-import, Gmail-label, or requested-scope defect. No token was deleted or replaced. A fresh interactive Google authorization is the required recovery step. Work completed Credential configuration inspection (Completed) Confirmed that Hiro resolves both the Google OAuth client file and the saved authorization token at their configured local paths. Confirmed that the saved token contains a refresh credential and the required Gmail read-only and Calendar read-only scopes. Confirmed that the saved token belongs to the same OAuth client described by the configured client file. Inspected only non-secret metadata and did not print, publish, copy, delete, or replace any credential value. OAuth refresh diagnosis (Completed) Confirmed that the saved access token is expired and therefore requires refresh before the Gmail API can be called. Sent a direct refresh request using the configured local authorization and captured Google's structured failure response. Google returned invalid_grant with a generic Bad Request description. That response means the refresh credential is no longer accepted and cannot be repaired by retrying the import. Decisions and reasoning Do not alter Gmail scopes because the saved authorization already includes the required read-only scopes. Do not retry imports repeatedly when Google reports invalid_grant. Do not delete or overwrite the saved token until a fresh interactive authorization is intentionally initiated. Keep the integration read-only; fresh consent should request only Gmail read-only and Calendar read-only access. Validation and evidence OAuth file presence: passed — Both configured local OAuth files exist. Saved authorization metadata: passed — The token contains a refresh credential, both required read-only scopes, and a client identity matching the configured OAuth client. Google token refresh response: failed — Google rejected the refresh request with invalid_grant: Bad Request. This is the confirmed cause of the Gmail import failure. Current state Hiro's task and reading dashboard remains operational. Gmail imports cannot run until the local Google account is authorized again. The existing saved token remains in place and was not modified by this diagnosis. The required recovery is an interactive Google sign-in and consent flow using the existing OAuth client. Next steps Initiate a fresh local Google OAuth flow and complete the account-owner sign-in and read-only consent. Preserve the rejected token as a recoverable backup until the replacement authorization succeeds. Retry a small labeled Gmail import and confirm that review-first ingestion and duplicate prevention operate correctly. Improve the dashboard error message later so invalid_grant is presented as a clear reauthorization requirement rather than a generic RefreshError.

Restoring Hiro and validating the personal task dashboard

2026-07-26T14:51:23-07:00

Executive summary Hiro was restarted so the newly implemented personal task and reading-list dashboard could be evaluated through its normal local and private-network listeners. The first background launch reached the page shell but could not initialize the configured database because it was started inside the restricted development sandbox. Hiro was relaunched through its normal host process with access to its configured database. The productivity schema initialized successfully and the complete task dashboard then loaded through both supported HTTP access paths. Work completed Runtime restart and listener verification (Completed) Inspected Hiro's checked-in API launcher and confirmed that the normal process serves the HTTPS application, a private-network HTTP listener, and the loopback evaluation listener. Normalized the Windows process search path before starting the final child process, preserved the inherited runtime environment, enabled UTF-8 output, and launched the checked-in Python runtime without a visible console window. Verified that all three expected listeners were owned by one final Hiro process. Productivity database startup diagnosis (Completed) The first restricted launch served the static task page but returned an HTTP 500 response for the productivity bootstrap request. Captured the underlying exception and identified a read-only database error because the configured durable database is outside the development sandbox. Ran the productivity bootstrap through the normal host context, which created the schema and seeded the nine agreed default tags without changing existing task or reading records. Replaced the restricted process with the normal database-enabled Hiro process and confirmed that the API returned an empty but valid first-use dashboard. End-to-end access validation (Completed) Loaded the personal list through the local HTTP listener and confirmed that the summary values resolved, nine default tags appeared, and Review, Open, Waiting, Scheduled, and Done rendered as five separate task sections. Loaded the same page through Hiro's private-network address to verify the phone access path. The final browser checks reported no page-level error and no console warnings or errors. Decisions and reasoning Run Hiro's durable database services in the normal host context rather than a restricted development sandbox. Continue using one Hiro process for the HTTPS, private-network HTTP, and loopback evaluation listeners. Treat the private-network HTTP task route as the practical phone interface for this first release. Do not create sample personal records merely to prove startup; validate the empty first-use dashboard without polluting the user's durable list. Validation and evidence Productivity bootstrap: passed — The normal host context returned an empty task list, an empty reading list, all nine default tags, the configured workflow states and horizons, and zeroed summary statistics. Local task dashboard: passed — The local HTTP page loaded its API data, rendered five task sections and ten tag-filter options including the all-tags choice, and displayed zero active or review items without an error panel. Private-network phone path: passed — The private-network task address loaded the Personal list page and resolved its summary data without an application error. Browser console: passed — The final task-page validation reported no browser console warnings or errors. Current state Hiro is running through its normal three listeners. The personal task and reading-list dashboard is available through the local and private-network task routes. The productivity database schema is initialized and currently contains no user tasks or reading items. All nine agreed default tags and all five task workflow sections are available. Next steps Use the dashboard for manual task and reading capture and verify that the first real records match the intended workflow. Connect or confirm the local Gmail OAuth credentials before evaluating labeled Gmail imports. Repair the scheduler schedule-file encoding warning in a separate scoped maintenance session. Add recurring tasks, reminders, and Hiro conversational controls after the core workflow has been used and refined.

A modular personal task and reading-list architecture for Hiro

2026-07-26T11:20:51-07:00

Executive summary A work email task-manager design was reviewed as the basis for a personal system organized by areas of life such as Hiro, self-care, and family. The recommended design keeps the original system's strongest trust boundary: machine-generated suggestions and human edits remain separate, so re-analysis cannot overwrite a person's decisions. The personal version should not make email the central data model. Tasks and reading items should be durable first-class records, while Gmail, manual entry, and future Hiro conversations plug in through independent source adapters. Hiro already has the key foundations for a no-new-cost first version: a local SQLite-backed service, a mobile-accessible FastAPI surface, read-only Gmail OAuth support, and local Qwen inference. GPT API use can remain optional and disabled for routine classification. Hiro was switched to the official Qwen3.5 9B Q4_K_M runtime as a lower-heat default. The prior 35B-A3B profile remains available as a reversible fallback. The agreed first release is now implemented as a standalone productivity module: durable task and reading records, a responsive web interface, read-only Gmail label ingestion, provenance-aware date extraction, and optional local Qwen 9B tag classification. Work completed Reference design review (Completed) Reviewed the supplied inbox-to-task-board design, including conversation-level grouping, incremental analysis, schema-constrained model output, manual overrides, completion watermarks, and a grouped dashboard. Identified the separate machine and user columns as the most important pattern to retain. Identified normalized email subject lines as too weak for a durable personal task identity because unrelated messages can share a subject and subject text can change. Fit with Hiro's current architecture (Completed) Inspected Hiro's current architecture and confirmed that the existing application already uses FastAPI, SQLite, a browser UI, mobile access through the private network, deterministic resolvers, Gmail OAuth, and a local Qwen model profile. Confirmed that Hiro's current Gmail tool returns metadata and short sanitized previews rather than full bodies or complete threads. That is sufficient for search but not yet sufficient for reliable task extraction or article URL ingestion. Confirmed that Hiro already treats actionable to-do and reading lists as planned product capabilities, including provenance and user review. Proposed module boundaries (Completed) Recommended a standalone productivity module with its own data access layer, service API, and UI route instead of embedding task logic in Hiro's chat loop. Recommended source adapters for manual entry, Gmail, and future Hiro-created suggestions; enrichment providers for deterministic rules, the local model, and an optional paid model; and presentation adapters for the web dashboard, mobile access, and a future ChatGPT app. Recommended first-class records for areas, tasks, reading items, source references, user overrides, and an append-only event history. The source reference preserves the exact Gmail message or Hiro interaction that created an item. AI and cost strategy (Completed) Recommended deterministic extraction and sender or label rules first, followed by Hiro's existing local Qwen model for ambiguous classification. Recommended that GPT API processing remain an explicit optional fallback with a hard budget, not a dependency of ingestion or display. Documented that a ChatGPT subscription and API billing are separate. Interactive ChatGPT access can help inspect Gmail or manage the system through a future app, but it does not provide a free model endpoint for Hiro's unattended local pipeline. Reading-list workflow (Completed) Recommended a dedicated Gmail label or self-mail convention as the low-friction capture mechanism. Recommended storing article URL, title, source message reference, capture time, reading state, notes, and optional task linkage in Hiro's database. Recommended Hiro's existing private mobile web access as the first remote interface. Direct Gmail access from ChatGPT can provide conversational retrieval immediately, while a future authenticated custom ChatGPT app can expose Hiro's durable task and reading-list API. Initial product behavior decisions (Completed) Separated subject tags from planning horizons. The initial horizons are current for zero to one month, medium-term for one to three months, long-term for three to six months, and someday for more than six months. Reframed the workflow labels in plain language: Review for unapproved imports, Open for actionable work, Waiting for blocked work, Scheduled for work intentionally deferred to a date, and Done for completed work. Specified that source dates should be extracted when present, with the source evidence, confidence, and any relative-date interpretation retained separately from a user override. Selected dedicated Gmail task and reading labels, the existing private mobile web path, and a first release containing manual tasks, Gmail-derived tasks, and reading items. Recurring tasks and reminders remain planned follow-up capabilities. Selected Review as the initial gate for Gmail-derived tasks, with a configurable future path for direct import after observed accuracy is trustworthy. Selected an indefinitely retained Done section with an undo action that restores the task's prior state. Selected multi-value tags so one item can belong to several subjects. Initial tags are Hiro, self-care, family, projects, music, TV, other, AI, and Active for exercise-related items. Confirmed that Gmail ingestion will use Hiro's existing local Google OAuth integration and Gmail API access, while routine classification will use Hiro's locally hosted Qwen model rather than a paid GPT API. Thermal constraint and temporary model review (Completed) Inspected the checked-in launcher, model router, installed local model inventory, and current GPU configuration after the user reported computer heat concerns. Confirmed that the current Qwen3.6 35B-A3B Q4_K_M file is about 20.75 GiB and is fully GPU-offloaded by the launcher on a 32 GiB GPU whose configured power limit is 575 watts. Confirmed that the available Qwen3.5-27B Q4_K_M file is about 17.98 GB, so it would save only about 2.8 GB of model-weight memory. Identified the important architecture difference: the current 35B-A3B model activates about 3B parameters per token, while the 27B model is dense. The nominally smaller model may therefore use more computation and produce more heat during generation. Confirmed that a 9B Q4_K_M Qwen-family model is already installed locally and is a more credible low-heat temporary profile than downloading the dense 27B model. Low-heat Qwen3.5 9B runtime switch (Completed) Rejected the initially installed 9B candidate after a real warm-up showed that the experimental base-model edit ignored a READY-only instruction and returned unrelated code. It was not adopted as Hiro's runtime. Downloaded and indexed the official Qwen3.5 9B Q4_K_M text model. The completed weights are about 5.24 GiB, roughly one quarter of the prior 35B-A3B Q4_K_M weight file. Added a dedicated Qwen 9B PowerShell launcher and background entry point. The launcher pins the LM Studio CLI, exact model identity, 8,192-token context, one parallel slot, maximum GPU offload, exact-content warm-up, and startup metadata. Normalized the process-scoped Windows search path before all child-process starts and added a hidden LM Studio startup path, preserving the inherited environment required by Hiro's runtimes. Added the qwen9 router profile and made it the default for fast and standard local routing, health recovery, nightly API preflight, documentation, and model-registry restoration. The qwen36 profile and launcher remain intact for rollback. Capped the ordinary expanded Qwen 9B completion budget at 1,024 tokens and request non-reasoning mode for routine calls to reduce unnecessary generation. Explicit short budgets remain explicit. The first official-model cold start became ready in 8.531 seconds and returned exactly READY in a 1.731-second warm-up. An idempotent rerun became ready in 0.852 seconds and repeated the exact READY result in 1.807 seconds. Personal task and reading-list implementation (Completed) Added an isolated SQLite schema for tasks, reading items, source provenance, many-to-many tags, soft deletion, and append-only audit events. The module is independent of Hiro's conversational agent and can later be exposed through narrow internal tools. Implemented Review, Open, Waiting, Scheduled, and Done task states; Current, Medium-term, Long-term, and Someday horizons; priorities; source-backed due-date evidence and confidence; a separate Done section; and completion undo that restores the prior state. Implemented a responsive /tasks interface linked from Hiro's existing navigation. It supports task and article creation and editing, search, horizon and tag filtering, custom tags, multi-tag selection, reading-state progression, explicit removal, Gmail imports, and mobile breakpoints. Extended Gmail ingestion through Hiro's existing read-only Google OAuth path. Dedicated Hiro/Tasks and Hiro/Reading labels are read without modifying Gmail; imported items enter Review, stable Gmail message identifiers prevent duplicate imports, and article URLs are filtered from full message content. Implemented deterministic due-date extraction for exact and relative dates, retaining the source phrase and confidence. Deterministic keyword tagging runs first; only ambiguous items can invoke the active local Qwen model, with constrained JSON validation and no paid GPT API dependency. Added the productivity migration to the normal database initializer and mounted the module router and dashboard in the existing FastAPI application. Decisions and reasoning Use a task-centered domain model rather than an email-centered clone. Keep machine proposals and human overrides separate, and add an append-only event history for auditability. Treat tasks and reading items as related but distinct record types; a reading item can later be promoted to a task without losing its source. Use stable provider identifiers for Gmail messages and threads when available; do not rely on normalized subjects as the primary identity. Make local inference the default AI path and make GPT API escalation optional, budgeted, and nonessential. Keep the productivity module independent of Hiro's conversational agent, then integrate through a narrow internal API and explicit tool methods. Start remote access with the existing private mobile web path. Consider a custom ChatGPT app only after the local workflow and authorization model are stable. Model multi-value subject tags and a single planning horizon as independent fields. Use current for zero to one month, medium-term for one to three months, long-term for three to six months, and someday for more than six months. Extract due dates only when supported by the source. Preserve exact dates directly; resolve relative dates against the source timestamp and time zone while marking them as inferred. Use Review, Open, Waiting, Scheduled, and Done as plain-language states. Manual tasks may enter Open immediately. Send Gmail-derived tasks through Review initially, then permit direct import as a configurable later mode after accuracy has been measured. Use dedicated Gmail task and reading labels for initial capture. Retain task and reading records indefinitely unless the user explicitly deletes them. Display Done in a separate section and allow completion to be undone. Keep Gmail as the authoritative complete message store while retaining extracted fields, source identifiers, and useful evidence with the task. Fetch the complete original message when more context is requested. Use Hiro's local Gmail OAuth and Gmail API adapter for ingestion; do not depend on the ChatGPT Gmail app for the unattended pipeline. Keep the productivity module independent of a specific local model so runtime profiles can be changed without altering task data or ingestion logic. Do not assume Qwen3.5-27B will reduce heat merely because its weight file is smaller; it is dense while the current Qwen3.6 35B-A3B activates only about 3B parameters per token. Prefer the already-installed 9B Qwen-family model as the immediate low-heat runtime candidate, or reduce the GPU power target after explicit approval. Keep paid GPT API fallback disabled by default. Use many-to-many tags with initial values Hiro, self-care, family, projects, music, TV, other, AI, and Active for exercise-related items. Defer recurring-task generation and reminders while keeping both in the planned roadmap. Use the official Qwen3.5 9B Q4_K_M model for Hiro's temporary low-heat profile; do not use the installed experimental base-model edit that failed instruction-following validation. Keep qwen9 as the default profile while retaining qwen36 and its launcher unchanged as the reversible higher-capability option. Use a 1,024-token ordinary local completion budget for Qwen 9B and disable optional reasoning on routine requests; preserve explicitly requested shorter limits. Use a separate productivity SQLite schema and service boundary now, with Gmail and future Hiro integrations as adapters rather than dependencies. Make Gmail ingestion read-only and label-scoped; leave messages, labels, and read state unchanged. Run deterministic classification first and use the active local Qwen 9B model only for ambiguous tags. Do not call a paid GPT API in the first release. Use soft deletion for explicit removal and immutable audit events for the retained change history. Validation and evidence Reference document review: passed — The supplied design was read in full and its data, ingestion, analysis, dashboard, and scheduling patterns were evaluated. Hiro architecture inspection: passed — The existing repository's architecture, model routing, FastAPI/static UI setup, Gmail tool boundaries, and stated product direction were inspected. Implementation tests: passed — 48 targeted pytest checks passed across the Qwen 9B and retained Qwen 35B launchers, nightly preflight, profile-aware output budgets and payloads, ordered reasoning, health routing, authentication, and operational boundaries. Journal test and frontend production build: passed — npm run test:hiro passed. npm run build generated and validated 41 journal entries, then completed the TypeScript and Vite production build. Real Qwen3.5 9B child-process startup: passed — The pinned launcher started LM Studio child processes after Path normalization, loaded model identity qwen3.5-9b at 8,192 context with one parallel slot, and required an exact READY response. Live endpoint and instruction behavior: passed — The OpenAI-compatible endpoint reported qwen3.5-9b, the loaded-process inventory showed only that model instance, and a 512-token arithmetic request returned the requested number 4. Thermal and power snapshot: passed — After the targeted test and warm-up sequence, the GPU reported 43 degrees Celsius, about 91 watts, 12.9 GB of allocated GPU memory, and 11 percent utilization. A separate active-inference sample reached 44 degrees Celsius and about 235 watts; no controlled 35B comparison was run. Productivity storage, Gmail adapter, and API tests: passed — Eight focused pytest checks passed. Coverage included schema seeding, multi-value tags, provenance, due-date evidence and horizons, append-only lifecycle events, soft deletion, reading items, review-first Gmail imports, Gmail idempotence, task completion undo, and real FastAPI task and reading round trips. Interactive browser workflow: passed — The live /tasks page created a high-priority medium-term task with Hiro, AI, and Projects tags; moved it to Done; restored it with Undo done; and created a tagged reading item. Final browser console inspection reported no warnings or errors. Responsive phone layout: passed — At a 390 by 844 viewport the toolbar switched to its mobile column layout, cards collapsed to one column, and the document reported no horizontal overflow. Updated journal test and production build: passed — After the productivity implementation update, npm run test:hiro passed. The generator validated 41 timestamped journal entries and their aliases, sitemap, Atom content, noindex metadata, and IndexNow key; TypeScript and the Vite production build then completed successfully. Current state Hiro already has local model inference and an optional paid fallback, so a first version does not require a new model subscription. The current mobile surface can be reached through the existing private network path; a direct ChatGPT-to-Hiro connection is a later integration. Hiro's active local profile is qwen9, served by the official Qwen3.5 9B Q4_K_M model on the existing local endpoint. The Qwen3.6 35B-A3B files, profile, launcher, and tests remain available for a deliberate future rollback or capability comparison. LM Studio continued downloading an optional projection support artifact after the completed text weights were indexed; Hiro's text-only runtime does not depend on that optional artifact. The personal task and reading-list first release is implemented locally and available from Hiro's Tasks navigation at /tasks. The durable module supports manual tasks, reading items, Gmail review imports, multi-value subject tags, planning horizons, source-backed due dates, an indefinitely retained Done section, completion undo, custom tags, filtering, and explicit soft removal. Gmail ingestion uses the existing read-only local OAuth integration and dedicated labels. It retrieves complete labeled messages for extraction but does not alter the mailbox. Routine classification does not require a paid model endpoint: deterministic rules run first and the active local Qwen 9B service is an optional ambiguity fallback. Next steps If a model profile changes, add a pinned launcher, identity-aware warm-up, real child-process startup test, and a small representative quality comparison before making it the default. Keep review mode enabled initially and introduce configurable direct import only after measured precision is acceptable. Add recurring tasks and reminders after the core task and reading workflows are stable. Add optional Hiro chat tools and, later, a narrowly scoped ChatGPT app after the core system is trustworthy. Run a small representative Hiro task-quality and sustained-load comparison before deciding whether the 9B profile should remain permanent. Keep the 35B profile available but avoid returning to it during the current thermal constraint unless the 9B model fails a concrete capability requirement. Connect the user's Gmail OAuth credentials if not already present, create the Hiro/Tasks and Hiro/Reading labels, and evaluate a small private sample in Review mode. Iterate on extraction and tagging from reviewed imports, then decide whether measured accuracy supports an opt-in direct-import mode. Add recurring tasks and reminders after the daily manual and Gmail workflows have been used enough to establish the desired behavior. Add narrowly scoped Hiro chat tools for listing and changing productivity records when the standalone workflow is stable.

Nightly readiness remains blocked by a missing scheduled run

2026-07-25T07:36:05-07:00

Executive summary The scheduled July 24 Pacific nightly evaluation did not run. The canonical ledger contains no nightly record for that date, while the checked-in schedule remains disabled. Several independent Daylab proposal-only evaluations ran around the scheduled window, but they are not scheduled-night evidence and do not add to the readiness count. The canonical count remains three valid completed scheduled nights. The July 20 connection-wide failure and the July 23 and July 24 no-run nights are excluded. The process is not ready for expansion. Work completed Scheduled-run and ledger audit (Completed) The schedule configuration was read-only inspected and has enabled set to false. The append-only evaluation ledger has four nightly entries through July 22 Pacific; no nightly run began or completed in the July 24 Pacific scheduled window. The most recent canonical scheduled record remains the July 22 Pacific run that started at 10:00 PM Pacific on July 22 and completed in about six minutes. Operational evidence review (Completed) Daylab records at 10:06 PM, 10:36 PM, 11:06 PM, and 11:36 PM Pacific on July 24 confirm that a separate automatic proposal-only loop was active. The 10:06 PM Daylab personal-assistant run completed 16 observations with an 87.5% pass rate, zero invariant failures, and a 14.2-second p95 latency. It is supplementary only. Later Daylab output contains a transient all-connection-attempts failure in a separate run, plus nonfatal log-rotation warnings. These do not convert the missing nightly into a model outage, but they show that infrastructure reliability is not yet established. Proposal-scope review (Completed) No generated improvement proposal belongs to the missing scheduled run, so there is no nightly proposal to approve or reject. The nearby Daylab proposal was a low-risk, approval-required diagnosis of two deterministic reading-list formatting failures. Its scope is bounded, but it is not evidence for changing the scheduled process. Decisions and reasoning Classify July 24 Pacific as no run, not as a valid completed evaluation or a model/API failure. Exclude Daylab and all infrastructure/no-run nights from the successful scheduled-night count. Do not recommend an expansion until the scheduler is reliably producing canonical records and at least two additional stable valid nights are observed. Validation and evidence Evaluation Observatory ledger review: passed — Confirmed no canonical nightly run in the July 24 Pacific window and confirmed the historical nightly records used for the readiness count. Schedule and operational-log review: passed — Confirmed that the nightly schedule is disabled and separated Daylab activity from the scheduled workflow. Journal test and frontend production build: passed — npm run test:hiro passed; npm run build generated and validated 40 entries, then completed the TypeScript/Vite production build. Current state Latest night: no scheduled run; no run ID, execution mode, or nightly duration is available. Successful canonical scheduled-night count: 3. The valid nights recorded 100%, 93.75%, and 75% pass rates; quality and latency are not yet stable. Regression-probe outcomes are present in nearby Daylab logs but are not retained as durable per-probe results for the canonical nightly record. Model/API availability was not tested by the disabled scheduler. The separate Daylab activity demonstrates intermittent local availability but also logged a later connection failure. Next steps Obtain approval before repairing or re-enabling the scheduled workflow, then verify its preflight and a real child-process startup before the next window. Collect at least two more valid completed scheduled nights, preferably five total, with stable quality and latency and no recurring infrastructure failures. Persist per-probe outcomes and failure categories in the nightly ledger. Once readiness is met, first broaden held-out/adaptive test diversity rather than adding repetitions, and consider a bounded weekly deep run.

Automatic Daylab runner identified after Telegram run alerts

2026-07-25T07:12:14-07:00

Executive summary The overnight scheduler is disabled and no Hiro-related Windows Scheduled Task is currently registered, but an independent Daylab process was still running continuously. The process began at 5:36 PM Pacific on July 24 and was configured to execute a proposal-only self-improvement evaluation every 30 minutes, which explains the new run activity. Telegram notification delivery remains enabled through a lifecycle marker left from an earlier server session, so Daylab can attempt to send run summaries even though the separately managed Telegram bot process is no longer alive. Work completed Scheduler and process audit (Diagnosed) The checked-in nightly schedule has enabled set to false, and a live Scheduled Task query returned no matching Hiro, Nightly, Qwen, or Telegram task. The Daylab PID file identified a live Python process started at 5:36 PM Pacific on July 24. Its checked-in launcher starts hiro.lab.daylab with a 30-minute interval and a 20-minute per-cycle time limit; that loop is independent of the nightly schedule switch. Run and notification evidence (Diagnosed) Daylab output recorded completed self-improvement cycles at 6:39 AM and 7:08 AM Pacific on July 25, and the evaluation ledger was updated at 1:07 AM during this session's audit window. The Telegram-notifications lifecycle marker remains present from a prior server startup, while the PID recorded for the managed Telegram bot is stale and no longer identifies a running process. A recent Daylab log shows a Telegram send attempt. One recorded attempt failed because Telegram rejected malformed Markdown, so delivery is not guaranteed for every cycle. Containment decision (Pending user authorization) This session diagnosed the source and did not terminate the live Daylab process or alter notification state without explicit authorization. Stopping Daylab through its checked-in launcher would end the recurring evaluation process and remove its PID file. Stopping the Telegram lifecycle would also disable further notifications and mark the service as intentionally stopped. Decisions and reasoning Treat the Daylab loop as a separate automatic runner rather than evidence that the disabled nightly schedule was ignored. Do not stop or modify a live Hiro process during a diagnostic-only request without confirmation. Record the process and log evidence without publishing tokens, chat identifiers, message contents, or other private operational data. Validation and evidence Nightly schedule configuration: passed — The checked-in schedule file was inspected and has enabled set to false. Windows Scheduled Task audit: passed — A live query found no matching Hiro-related scheduled task. Live Daylab process and output: passed — The PID file resolved to a running Python process, and its output confirms recurrent completed cycles. Journal test and frontend production build: not run — These checks are required before publication and will be run after the source entry is generated. Current state The nightly scheduler remains disabled. A separate Daylab automatic loop remains active and will continue its 30-minute cadence until explicitly stopped or it exits. Telegram notification gating remains enabled by a lifecycle marker, allowing active Hiro code to attempt direct Telegram sends. Next steps On approval, stop the Daylab loop using its managed launcher and verify that its process and PID file are gone. On approval, disable the Telegram lifecycle marker and verify that subsequent notification attempts are skipped. Add an explicit operator-facing status view or unified shutdown command so the independent Daylab loop and the nightly scheduler cannot be mistaken for one another.

Privacy-preserving geolocation triggers added to Hiro's plan

2026-07-24T14:29:52-07:00

Executive summary Added a phased geolocation-trigger workstream to Hiro's public product direction. The proposed design accepts discrete, user-configured zone transitions from an iPhone over Tailscale; it does not require continuous GPS transmission or storage. The plan is intentionally staged: secure event ingestion and observation precede any low-risk automation, while consequential actions remain confirmation-gated. Work completed Location-transition architecture (Planned) The intended first integration is iPhone Shortcuts geofence automation posting an event containing a device label, approved zone name, enter or exit transition, timestamp, and event identifier to Hiro over Tailscale. The event contract is deliberately zone-based. Exact coordinates, continuous location history, and live tracking are outside the initial scope. A deterministic rules layer will decide whether an accepted event can cause a change. Models may enrich a rule-selected briefing or summary but will not choose whether a location rule fires. Ingress and reliability controls (Planned) The planned endpoint will use a dedicated location credential and Tailscale-restricted reachability rather than reusing an existing general-purpose secret. Required checks are an approved-zone allowlist, timestamp freshness, event-ID deduplication, persisted presence state, and per-zone cooldowns. The first Home enter and exit automations will run in observation mode so actual iPhone delivery reliability can be verified before downstream behavior is enabled. Safe automation scope (Planned) Early permitted effects are reversible and low-risk: setting a delivery or profile preference, and preparing a commute or unfinished-work summary for review or approved delivery. Potential later confirmation inputs include CarPlay and trusted Wi-Fi; Home Assistant is reserved as a possible broker only if the number of zones, devices, or presence sources makes the simpler route insufficient. Location alone will never authorize physical access, deletion, purchases, external email, or computer shutdown. Decisions and reasoning Use transition events rather than continuous GPS collection to minimize data exposure and battery use. Begin with Home enter and exit logging before enabling automation. Require deterministic rules, explicit configuration, and idempotent handling ahead of any model-generated content. Keep sensitive and consequential actions behind explicit confirmation regardless of the event source. Validation and evidence Roadmap documentation update: passed — The Hiro README now records the event-only architecture, phased rollout, reliability controls, and action-safety limits. The timestamped-journal test and frontend production build both passed after generating and validating 38 journal entries. Location endpoint and iPhone automation: not run — This session planned the capability only; no endpoint, token, firewall rule, iPhone automation, or location data handling was implemented or tested. Hiro application test suite: not run — The change is documentation-only and did not alter Hiro runtime code. Current state Hiro has a documented plan for privacy-preserving geolocation triggers but no implemented location-event endpoint or active location automation. No location coordinates, zones, device identifiers, credentials, or live transition records were collected or published during this session. Existing approval boundaries continue to govern consequential external actions. Next steps Define the versioned location-event schema and implement a dedicated authenticated endpoint with allowlisting, freshness, deduplication, state, and cooldown controls. Add focused tests for authentication, stale and duplicate rejection, invalid-zone handling, state transitions, and idempotent rule execution. Configure and manually test Home enter and exit iPhone Shortcuts over Tailscale in logging-only mode. Review observation evidence before enabling a narrowly scoped, reversible rule such as a delivery-profile switch or a commute-briefing draft.

Real-world assistant capability boundary audited

2026-07-23T15:49:11-07:00

Executive summary Audited Hiro's current calendar, mailbox, task, reading-list, reminder, memory, and web-update capabilities to distinguish simulated evidence from production readiness. The stateful simulator has substantial safety and recovery coverage, including three consecutive six-of-six canonical workflow passes and a thirty-of-thirty adversarial run. Those results used fictional state and do not prove live account operation. Privacy-preserving connector probes found that the currently configured Google and iCloud read-only integrations are not operational, while Outlook is not configured. Hiro therefore cannot currently be treated as a reliable real-calendar or mailbox assistant. Work completed Live connector audit (Diagnosed) Confirmed that Google Calendar and Gmail have read-only tool implementations and local credential artifacts, but both current provider probes failed at token refresh. Confirmed that iCloud Calendar has a read-only CalDAV implementation and configured settings, but the provider rejected the current authorization. Confirmed that Outlook Calendar remains unconfigured. No connector permission, credential, account data, event, message, or provider configuration was changed. Calendar and email action boundary (Simulation complete; production absent) Production tools can only read calendar and Gmail summary data when credentials are healthy. There is no registered production tool for creating, changing, or deleting calendar events, inviting attendees, drafting live email, or sending email. The simulated harness supports conflict discovery, proposal and draft creation, exact-scope approvals, calendar creation, email sending, stale-state recovery, idempotency, rejected and expired approvals, and adversarial failures. All simulated external writes operate on fictional in-memory state and never reach a provider. Tasks and reading lists (Storage foundation only) Typed local storage functions and schemas exist for tasks, reading items, provenance, and approval records. The user-facing production tool registry does not expose task or reading-list create, update, list, or review operations. Task and reading-list mutations are exercised in the simulated harness, not through Hiro's live conversational tool surface. Proactive assistance and current-information updates (Partial) A reminder scheduler and an on-demand web-search tool exist, and the web-search provider is configured. There is no production topic subscription, source-quality policy, briefing compiler, delivery schedule, or user review surface for proactive wider-internet updates. Curiosity and improvement records mention morning briefings as a possible experiment, but this is not a deployed user capability. Step 3 campaign gate (Incomplete) The valid Step 3 session completed only P1 calibration: thirty-seven of forty fresh cases passed, with no infrastructure or invariant failures. Phases P2 through P7 were not completed. The persisted summary still says running even though its deadline passed, so this is an incomplete campaign rather than evidence for Step 4. The campaign remains hermetic and did not authorize or test live provider writes. Decisions and reasoning Do not infer production capability from synthetic perfect scores; validate each real connector and action boundary separately. Restore reliable read-only access before implementing any provider write path. Expose persistent local task and reading-list operations before attempting cross-provider autonomous workflows. Introduce live writes progressively: read-only shadowing, draft/proposal artifacts, exact user approval, then one bounded provider action with reconciliation and audit. Do not enable proactive internet briefings until topics, sources, cadence, delivery, and opt-out controls are explicit. Validation and evidence Google Calendar read-only connectivity: failed — A privacy-preserving historical-range probe reached the configured integration but the provider rejected credential refresh. No calendar data was retained or published. Gmail read-only connectivity: failed — A privacy-preserving empty historical query reached the configured integration but the provider rejected credential refresh. No message data was retained or published. iCloud Calendar read-only connectivity: failed — A zero-day CalDAV probe reached the configured integration but authorization was rejected. No event data was retained or published. Outlook Calendar configuration: not configured — No Microsoft client configuration or token artifact is currently present. Synthetic stateful assistant evidence: passed previously — The retained evidence includes three consecutive six-of-six Qwen canonical workflow runs and a thirty-of-thirty structurally novel adversarial report with no unauthorized write. Step 3 diversity campaign: incomplete — P1 completed at thirty-seven of forty; P2 through P7 and campaign closeout are absent, while the expired session summary remains marked running. Current state Hiro has a strong simulated agent harness but does not currently have reliable live calendar or mailbox access. Production calendar and Gmail tools are read-only by design and presently blocked by authentication failures. Tasks and reading lists have typed local persistence foundations but no registered conversational tools. Reminders and on-demand web search exist; proactive source-backed briefings do not. No production calendar write, attendee invitation, live email draft/send, or cross-tool approval execution path is deployed. Next steps Reauthorize Google read-only access and repair iCloud authorization, then run privacy-preserving live read smoke tests and a multi-day shadow period. Add production task and reading-list list/create/update tools backed by typed local storage, with provenance, idempotency, and user-visible audit history. Add provider-neutral calendar proposals and email drafts that do not perform external writes. Complete or explicitly close and replace the incomplete Step 3 campaign before using its results as a Step 4 gate. After stable shadow evidence, request separate approval for the smallest live calendar write pilot; keep invitations and email sending disabled initially. Design proactive briefings as an opt-in product with controlled topics, trusted-source rules, cadence, quiet hours, delivery channel, and per-item provenance.

Router performance telemetry added to Observatory

2026-07-23T15:37:20-07:00

Executive summary Added a Performance tab to Hiro's private Evaluation Observatory so routed development tasks can be reviewed by time, task label, selected model, route, status, duration, and token usage. The new read-only API aggregates the router's append-only JSONL ledger and reports input, cached-input, output, reasoning, and total input-plus-output tokens without returning prompts or model output. Restarted only Hiro's API process through its checked-in runner. Both local dashboard listeners returned the new endpoint successfully, while Qwen remained intentionally unloaded and the existing Cloudflare process was left unchanged. Work completed Performance dashboard (Completed) Added a first-class Performance navigation tab with summary cards, a chronological task-attempt table, and token allocation by model. Each row identifies the safe task label, short task ID, task kind, risk, start time, exact cloud model or active local profile, reasoning level, route, attempt number, token breakdown, duration, and completion status. The page handles an empty router ledger and refreshes on demand or when its tab is selected. Prompt-free telemetry API (Completed) Added a read-only performance endpoint backed by the router's append-only ledger with a configurable ledger path. Total token usage is defined as input plus output tokens. Cached input is shown as a subset of input and is not double-counted; reported reasoning tokens are displayed separately. Cloud route names resolve to their configured model and reasoning profiles. Local attempts are identified by Hiro's active Qwen profile. Safe task identity and ledger privacy (Completed) Added an optional safe task label to the router CLI and PowerShell launcher for readable dashboard attribution. Unlabeled historical or future records fall back to task kind plus a short immutable task identifier. Removed the truncated final model message from new summary-ledger records; detailed per-task artifacts remain local. The dashboard API never returns prompts or output. Live service activation (Completed) Verified that the existing dashboard HTML immediately exposed the new tab, then restarted only the exact process owning Hiro's three expected API listeners so the new route module would load. Used the checked-in blocking API runner with normalized Windows Path state and hidden process startup. Confirmed the performance endpoint on both HTTP port 8001 and the loopback Observatory port 8765. The current ledger contained one task attempt and 13,525 total input-plus-output tokens. Decisions and reasoning Do not display task prompts or model output remotely. A safe operator label provides useful identity without expanding the dashboard's sensitive-data surface. Count input plus output as total tokens and report cached input separately because cached tokens are already part of input usage. Represent every attempt rather than collapsing escalations so model changes and the cost of failed lower-tier attempts remain visible. Keep this view read-only and under the Observatory's existing private-access boundary. Do not restart Cloudflare or load Qwen merely to activate a dashboard API change. Validation and evidence Focused dashboard, API, router, and operational suite: passed — 33 tests passed in 3.95 seconds, covering prompt-free output, exact token aggregation, model mapping, empty-ledger behavior, dashboard presence, existing evaluation APIs, remote access behavior, routing policy, one-escalation resume, output budgets, payloads, and operational boundaries. Python compilation and router launcher startup: passed — The modified server, configuration, performance API, router CLI, and dispatcher compiled successfully. The normalized PowerShell launcher returned a ready self-check with all five routes. Dashboard JavaScript parse: passed — The inline Observatory script was extracted and compiled with Node.js without a syntax error. Live API activation: passed — After the scoped API restart, ports 8001 and 8765 both returned HTTP 200 from the new performance endpoint with one task, one attempt, and 13,525 total tokens. Model shutdown preservation: passed — LM Studio reported that no models were loaded after activation, preserving the user's prior Qwen shutdown request. Current state The Performance tab is active in Hiro's running private Evaluation Observatory and reads the append-only router summary ledger. New routed tasks can provide a safe label through the documented launcher; historical records remain identifiable through task kind and ID. The current dataset contains the initial Luna Low router probe. The view will become comparative as real routed tasks accumulate. The Cloudflare tunnel process and private-access configuration were not modified. Next steps Use a short safe label for each routed task so the Performance table remains readable without exposing prompts. Accumulate a varied sample before changing route thresholds, then compare validated success, total tokens, cache share, duration, and escalation frequency by model. Consider adding time-range and model filters once the ledger contains enough tasks to make filtering useful. Keep per-task prompts, output, credentials, and detailed artifacts local rather than expanding the remote telemetry surface.

Measured Codex task router activated

2026-07-23T15:21:41-07:00

Executive summary Implemented a separate Hiro development-task router that selects among local Qwen, Luna Low, Terra Medium, Sol Medium, and explicitly approved Sol High without changing Hiro's conversational router. The policy permits at most one automatic escalation, and only an objective process or operator-supplied validation failure can trigger it. High-consequence work has a Sol capability floor, while Sol High requires explicit approval. Installed and verified the official standalone Codex CLI because Windows denied child-process execution of the desktop application's packaged binary. A real read-only Luna Low probe completed successfully and returned the exact expected response. Work completed Deterministic routing policy (Completed) Added five explicit route profiles with model and reasoning settings, risk floors, task-kind handling, operator overrides, and a one-escalation ceiling. Restricted local handling to bounded, low-risk, non-mutating transformations. Normal implementation defaults to Terra Medium; ambiguous, cross-system, security-related, or high-consequence work routes to Sol Medium. Sol High cannot be selected automatically without an explicit allow flag. Audited Codex dispatch (Completed) Added a noninteractive Codex dispatcher using JSONL events, saved ChatGPT authentication, explicit model and reasoning overrides, least-required sandbox selection, and resumable thread IDs. Task text is passed through stdin rather than interpolated into a shell command. The child process uses an argument array with shell execution disabled. Added an append-only local summary ledger and per-task artifacts containing route, duration, validation status, thread identity, and reported input, cached-input, output, and reasoning tokens. Windows launcher and CLI readiness (Completed) Added a PowerShell launcher that normalizes the known Path/PATH collision from machine and user path values before starting Hiro's pinned Python runtime. Installed Codex CLI 0.145.0 through the official OpenAI standalone installer and configured Hiro to use that executable. Kept the router opt-in through the dedicated launcher so existing assistant chat behavior is unchanged. Decisions and reasoning Do not merge development-task dispatch into core/router.py; conversational inference and repository-changing Codex work have different safety and evidence requirements. Optimize total task usage rather than the apparent cost of the first attempt. A cheap failure followed by repeated repository discovery is not a saving. Resume a prior Codex thread on objective failure instead of starting a context-free replacement attempt. Do not use target percentages for route distribution until real task outcomes provide evidence. Validation and evidence Focused router and operational regression suite: passed — 27 tests passed in 3.05 seconds, including route floors, Sol High authorization, prompt/command separation, Path normalization, one-escalation resume behavior, output-budget behavior, payload handling, and operational boundaries. Python compilation: passed — All five new Python router modules compiled successfully. Launcher child-process startup: passed — The normalized PowerShell launcher started Hiro's pinned Python runtime, returned the five-route self-check, and produced a Luna Low dry-run decision. Real Codex routed probe: passed — A read-only Luna Low task completed in 3.719 seconds with exit code 0 and returned ROUTER_OK. Codex reported 13,498 input tokens, 9,984 cached input tokens, 27 output tokens, and 17 reasoning-output tokens. Current state Hiro now has an operational, opt-in development-task routing command documented in docs/HIRO_CODEX_ROUTER.md. The official standalone Codex CLI is installed and the router's executable setting points to it. No scheduled evaluation, production connector, assistant service, live calendar, email, task data, or user-facing chat route was changed. Next steps Route a representative sample of real bounded Hiro development tasks and compare first-pass success, total tokens, cached tokens, duration, validation, and correction rates by route. Keep route thresholds fixed during the initial sample so the evidence is comparable. After sufficient varied tasks, adjust route floors only where measured total task cost and validated success justify the change. Consider a private dashboard view of aggregate router metrics without exposing prompts, full model output, credentials, or sensitive task artifacts.

Step 3 calibration repair completed

2026-07-23T08:04:38-07:00

Executive summary Implemented and launched the finite Step 3 diversity controller instead of using the prior unbounded repetition loop. The repaired calibration session is step3-20260723-0852, with a seven-hour deadline and a fixed 348-case noncanonical budget across ten capability families. The opening Qwen canonical gate passed 6/6 in 110.526 seconds (stateful-20260723T145256Z, qwen_qwen3.6-35b-a3b). The fresh repaired P1 completed 40 cases across development, blind, and canary partitions with 37/40 passes (92.5%), zero infrastructure errors, zero invariant failures, 100% canary, and 91.7% blind performance. The repair exposed complete allowed-choice vocabularies without identifying the correct choice, corrected an accidentally double-escaped scoring regex, and added executable regression coverage proving every generated oracle accepts its canonical answer. Earlier invalid or interrupted attempts remain immutable and excluded. Work completed Finite controller and sealed portfolio (Completed) Added the hiro.benchmarks.development_cycle.step3 controller with prepare, validate-plan, record-preflight, run, status, and close commands. Sealed six phases totaling 348 noncanonical cases: 40 calibration, 80 breadth, 60 evolving state, 60 cross-family, 48 adaptive reserve, and 60 blind frontier. The portfolio covers calendar temporal reasoning, task replanning, email grounding, cross-tool coordination, approval lifecycle, concurrency, recovery, memory continuity, source judgment, and productive autonomy. Validation admitted 348 unique structural signatures, a 100% generated novelty rate, every required family, a maximum family share below 15%, and a maximum topology share of 0.57%. Oracle and retry integrity (Completed) Reworked generated prompts so expected answer tokens are not written into the prompt. Their required response varies from the generated state, such as the only safe calendar slot, blocked dependency, approval state, version freshness, or recovery state. Sealed a distinct immutable VariantManifest per phase and partition during preparation, preventing retry attempts from changing the ledger manifest solely because the current time changed. Added regression coverage for finite balanced preparation, idempotent prepare/close, passing canonical preflight recording, and expected-answer non-leakage. P1 calibration repair (Completed) Recorded the passing opening canonical report as immutable preflight evidence for fresh session step3-20260723-0852. The repaired P1 completed through the one-time local Windows task: 22 development, 12 blind, and 6 canary observations; 37 passed, three failed, and no evaluation or invariant error occurred. No production connector, live calendar, email, task system, reading list, schedule, or external service was changed. Decisions and reasoning Do not accept superficial signature diversity when expected labels are exposed by the prompt; correct the oracle contract before scoring a model. Treat interrupted evaluation execution as a controller reliability requirement. Retry must reuse the exact sealed variant rather than create a fresh immutable record. Run only P1 after readiness validation; do not release the full campaign merely because a timebox exists. Retain previously prepared and interrupted session records as audit evidence, but exclude them from valid evaluation counts. Validation and evidence Focused Step 3 and legacy development-cycle tests: passed — 7 tests passed in 1.73 seconds, including complete choice vocabulary, non-leaking output instructions, and executable canonical-regex acceptance for every generated case. Campaign manifest validation: passed — step3-20260723-0852 validates with 348 cases, 348 unique structural signatures, all ten families, 100% generated novelty, and 0.29% maximum topology share. Opening canonical preflight: passed — stateful-20260723T145256Z used qwen_qwen3.6-35b-a3b, passed 6/6, and completed in 110.526 seconds. Repaired P1 calibration: passed — Fresh session step3-20260723-0852 passed 37/40 (92.5%): development 20/22, blind 11/12, canary 6/6. There were zero observation errors and zero invariant failures. The three misses were unrelated memory-correction, quoted-email, and conservative-autonomy decisions. Current state The active repaired campaign session is step3-20260723-0852 with its finite manifest and immutable P1 evidence recorded. P1 now satisfies the campaign decision table for advancement: fresh pass rate is above 90%, canary is perfect, blind exceeds 90%, and the three misses are not a related failure cluster. P2 through P6 have not yet been started. Earlier controller launch attempts are classified as interrupted infrastructure/controller diagnostics, not successful evaluation results. Next steps Advance to the next bounded fresh phase while increasing one structural dimension, retaining the repaired P1 as immutable baseline evidence. Release only the next bounded phase justified by novelty, completion, regression, and proposal evidence. Add a per-partition checkpoint if P1 evidence shows that a long partition needs safe mid-phase resumption. Keep all live integrations behind separate explicit authorization and production-specific evaluation.

Finite diversity campaign for Hiro Step 3

2026-07-23T07:46:06-07:00

Executive summary Designed a finite execution-grade roadmap Step 3 campaign that a lighter supervising model can initiate, monitor, resume, and close through deterministic controller commands and a fixed evidence decision table. The plan directly addresses the July 21 repetition failure. That session recorded 44,592 observations but only 2,820 structural signatures, a 93.68% structural-reuse rate. Seven of eight families had only four to eight effective topologies. The replacement campaign uses ten capability families, eight finite phases, a maximum of 360 planned cases including opening and closing canonical checks, global historical novelty rejection, family and topology caps, mastery retirement, sealed blind validation, and mandatory worker closeout. Work completed Repetition diagnosis (Completed) Inspected the existing development-cycle runbook, v1 runner, generated catalogs, adaptive Daylab rotation, and stateful adversarial generator. Confirmed that the v1 runner extends beyond its nominal cycle count until the timebox and that signatures are checked only within each generated batch rather than against historical catalogs. Recomputed the continuous-session corpus: 929 cycles, 44,592 observations, 2,820 unique signatures, and 93.68% structural reuse. Finite capability portfolio (Designed) Defined ten families spanning temporal calendar reasoning, task replanning, email grounding, cross-tool coordination, approval lifecycle, concurrency, recovery, memory continuity, source judgment, and productive autonomy. Defined a finite sequence from calibration through breadth, evolving state, cross-family composition, adaptive micro-waves, blind frontier, and closeout. Set the campaign target at 320-420 valid observations, with a manifest maximum of 348 noncanonical cases plus twelve opening and closing canonical workflow checks. Enforceable novelty and mastery (Designed) Structural signatures exclude superficial names, prose, dates, and labels and instead encode constraint graphs, event ordering, tool classes, approval transitions, resource versions, fault sequences, temporal relations, oracle class, and family composition. The plan requires exact and near-duplicate rejection against repository suites and historical development and adversarial catalogs, at least 85% noncanary novelty, a fifteen-percent family cap, and a two-percent topology cap. Mastered family-band combinations retire after two fresh statistically supported waves and return only as small rotating canaries. Weaker-model supervision contract (Designed) The supervisor follows machine-readable checkpoints and allowed-next-actions instead of reconstructing state from conversation history. A fixed table determines whether to increase difficulty, hold a band, create a bounded diagnostic wave, permit one evidence-based proposal, reject a batch, or close the campaign. Terminal handling covers verified duplicate workers, wrapper timeouts, endpoint failures, incomplete reports, exhausted generators, failed candidates, blind leakage, and hard-stop closeout. Decisions and reasoning Do not use the v1 continuous development runner or repeated Daylab suites for roadmap Step 3. Make the deterministic controller responsible for variety, budgets, checkpointing, and stopping; the lighter model supervises rather than improvises the loop. Use a finite manifest and begin closeout thirty minutes before the deadline even when an earlier phase is incomplete. Permit at most one qualifying low- or medium-risk candidate per adaptive micro-wave and never alter evaluators, oracles, assertions, or failed cases to gain credit. If the required v2 controller cannot be implemented and validated within its bounded sixty-minute prerequisite, stop as step3_controller_not_ready rather than reverting to repetition. Validation and evidence Historical structural-reuse audit: completed — The July 21 continuous session contained 44,592 cataloged cases and 2,820 unique structural signatures, yielding 93.68% reuse. Seven families had only four to eight signatures; long-context attention supplied 2,778. Plan structure validation: passed — The runbook contains all ten required families, all eight phases, the finite manifest, novelty/family/topology limits, mastery retirement, supervisor decision table, closeout contract, evidence packet, and lighter-model handoff prompt. Planned maximum is 360 cases including canonical open and close checks. Step 3 controller availability: not-ready — The specified hiro.benchmarks.development_cycle.step3 interface does not yet exist. The runbook therefore makes controller implementation and validation a bounded prerequisite and prohibits fallback to v1. Capability evaluation: not-run-by-design — No Step 3 evaluation campaign, model run, candidate change, production connector, service, or schedule was started during this planning session. Current state The Step 3 execution plan is available in docs/HIRO_STEP3_DIVERSITY_CAMPAIGN.md. The existing development-cycle guide now points Step 3 operators to the finite campaign and explicitly disallows the v1 runner for this purpose. The plan is ready for controller implementation; it is not yet safe to start the long campaign with the old runner. Next steps Implement the finite Step 3 controller, expanded generator catalog, global novelty index, mastery state, and deterministic checkpoint interface. Test prepare, validate-plan, run/resume, status, and idempotent close commands, including duplicate-worker and deadline behavior. Run a short dry validation that generates and scores manifests without beginning the full campaign. After the readiness gate passes, hand the exact runbook prompt to the lighter supervising model for the finite campaign.

Qwen stateful workflow gaps closed

2026-07-22T22:38:23-07:00

Executive summary Implemented the approved plan for Hiro's Qwen 3.6 stateful workflow gaps. The work separated harness defects from model behavior, added per-round telemetry, strengthened simulated approval and stale-state semantics, and applied only the bounded model controls supported by evidence. The final closure evidence is three consecutive canonical runs at six of six workflows, followed by thirty of thirty fresh structurally novel adversarial scenarios. The adversarial batch contained six cases each for concurrent conflicts, scope drift, approval expiry, approval rejection, and injected tool failures, with no unauthorized-write failure. The full deterministic repository suite passed 185 of 185 tests. Qwen 3.6 remained the active model throughout the closure runs; no production connector, real calendar, email, schedule, or external service was mutated. Work completed Evidence-grade model telemetry (Completed) Captured model identity, finish reason, prompt tokens, completion tokens, reasoning tokens, visible-content length, message count, prompt character count, parsed tool-call count, and round number for each harness model call. Persisted telemetry with each canonical workflow result so context pressure and empty-response failures can be diagnosed from immutable reports without storing prompt contents. Added deterministic regression coverage for telemetry collection and local API payload controls. Mailbox and calendar semantics (Completed) Made synthetic mailbox search punctuation- and possessive-insensitive and allowed sender local-part matching, fixing the mismatch between a natural query such as a possessive name and the stored address. Required timezone-aware calendar intervals and changed calendar window, conflict, proposal, approval, and outcome comparisons to operate on canonical instants rather than raw timestamp strings. Preserved strict matching for title, attendees, duration, and instant while accepting equivalent representations such as Pacific time and UTC. Approval and stale-state safety (Completed) Approval requests now require an exact nonempty typed scope. Empty, incomplete, or overbroad scopes are rejected without creating a pending approval. A stale calendar result sets an enforced fresh-read precondition. Calendar creation is blocked without mutation until a fresh calendar read succeeds. Queued execution faults are preserved when a freshness guard blocks a call, ensuring the simulated partial-success and idempotent-replay path remains realistic and testable. Bounded Qwen behavior controls (Completed) The harness now sends LM Studio's supported reasoning-effort control and places the model's non-thinking instruction at active prompt boundaries. Telemetry confirmed that closure runs produced visible content on every call. Added general rules to preserve named local wall-clock time with explicit offsets, advance after successful mutations, complete each distinct requested artifact, and immediately refresh after stale calendar data. Context compaction and new proposal or draft inspection tools were deliberately not added: the corrected workflow six passed consistently with a maximum observed prompt of 4,666 tokens, below the configured 8,192-token context. Decisions and reasoning Treat semantically equivalent timezone representations as the same approved instant, without weakening any other approval field. Enforce safety transitions in the runtime as well as the prompt so a missed stale-state instruction cannot mutate simulated external state. Keep context compaction and additional artifact-list tools deferred until telemetry demonstrates actual context pressure or retrieval failure. Count only fully completed canonical runs toward closure; exclude failed diagnostic runs and the accidentally duplicated interrupted process. Do not translate simulated capability results into permission for production calendar or email actions. Validation and evidence Focused harness and router regressions: passed — 24 focused tests passed, including possessive mailbox search, timezone-equivalent approval, strict scope rejection, stale-read enforcement, queued-fault preservation, telemetry, and reasoning-control payload coverage. Canonical closure run one: passed — stateful-20260723T052157Z used qwen_qwen3.6-35b-a3b, passed 6/6 workflows, completed in 128.882 seconds, and had zero empty visible-content calls. Canonical closure run two: passed — stateful-20260723T052429Z used qwen_qwen3.6-35b-a3b, passed 6/6 workflows, completed in 123.919 seconds, and had zero empty visible-content calls. Canonical closure run three: passed — stateful-20260723T052709Z used qwen_qwen3.6-35b-a3b, passed 6/6 workflows, completed in 123.463 seconds, and had zero empty visible-content calls. Fresh adversarial structural variants: passed — adversarial-20260723T052957Z passed 30/30 scenarios with 30 novel signatures in 212.497 seconds: six each for concurrent conflict, scope drift, approval expiry, approval rejection, and adversarial failure. Full deterministic repository suite: passed — 185 tests passed in 8.72 seconds. A prior wrapper attempt produced three launcher errors because its environment normalization removed Path; rerunning with the prescribed canonical Path normalization passed all tests. Current state Qwen 3.6 35B-A3B is the verified active model for the stateful harness. The six canonical workflows now close reliably across three consecutive runs. The current fresh adversarial batch passes all thirty structural variants with no reported unauthorized-write failure. Runtime approval, timezone, stale-state, and idempotency boundaries are enforced independently of model compliance. Next steps Increase test diversity before increasing repetition, especially longer multi-stage workflows that combine multiple changing resources and delayed decisions. Add adaptive scenario selection that concentrates new cases around the weakest failure family while retaining a stable regression panel. Consider a larger weekly deep run with more structural families and longer horizons after the expanded cases are defined. Keep live connector enablement behind separate explicit approval, scoped authorization, dry-run evidence, and production-specific safety evaluation.

Plan to close Qwen stateful workflow gaps

2026-07-22T20:11:04-07:00

Executive summary Reviewed both Qwen 3.6 canonical stateful reports and the exact contracts for workflows three, five, and six. Both runs passed three of six; increasing the harness output budget did not change the score. The failures have different causes and should not be addressed with one broad prompt change. Workflow three is primarily a synthetic search-tokenization artifact, workflow five combines overly literal timestamp scope matching with a real stale-read sequencing miss, and workflow six is a genuine multi-stage planning and context-management gap plus a missing invalid-scope guard. The plan corrects harness semantics first, reruns the unchanged model, and only then introduces bounded agent-policy or context changes for failures that remain. No implementation was performed in this planning session. Work completed Evidence and telemetry gate (Planned) Record turn boundaries, raw tool-call parsing status, finish reason, visible and reasoning token counts, prompt size, and exact failure taxonomy without storing secrets or private data. Run each failed workflow repeatedly at temperature zero before changing behavior to measure stability and distinguish deterministic defects from model variance. Keep immutable pre-change reports and label every new report with profile and exact model identifier. Workflow three: mailbox discovery and safe drafts (Planned) Make the synthetic mailbox search punctuation- and possessive-insensitive so a query such as Maya's Atlas kickoff matches sender Maya and subject Atlas kickoff time. This better reflects ordinary mailbox search semantics. Add a generic one-step progressive broadening policy after an empty named-email search: remove punctuation and low-information words while preserving sender and subject anchors; do not hardcode Maya or Atlas. Validate against distractor messages, renamed people and projects, empty results, and cases where broadening would become unsafe. Workflow five: approval equivalence and recovery state machine (Planned) Canonicalize calendar start and end timestamps to instants before approval comparison so 13:00 Pacific and 20:00 UTC are equivalent, while continuing to reject any changed title, attendee, duration, or instant. Preserve original and canonical scope representations in audit evidence. This is a semantic normalization, not a weakening of approval scope. Make stale read results explicitly return refresh_required and enforce a fresh-read precondition before calendar mutation in scenarios that model stale state. Retain the exact same idempotency key for the required one-time replay after timeout-after-commit, followed by final-state verification. Workflow six: multi-stage planning and context control (Planned) Reject empty or structurally incomplete approval scopes at approval-request time. No pending approval should be created for an empty email or calendar scope. Expose read-only inspection of local proposals and drafts so the agent can verify the artifact it intends to authorize instead of reconstructing scope from long conversation history. Add deterministic context compaction between stages: retain user constraints, artifact identifiers, exact scopes, approvals, and tool outcomes while dropping redundant prose. Do not use a second model or summarize away authorization details. Add a stage-completion check that prevents final prose when the requested local artifact or verification read has not occurred. Decisions and reasoning Correct harness artifacts before changing the Qwen prompt or routing behavior. Prefer runtime safety guards and typed tool contracts over relying only on model instructions for approval and freshness boundaries. Do not hardcode the current names, dates, projects, or expected tool sequence into the model prompt. Treat live connector changes, production approval semantics, and external writes as a later high-risk decision requiring separate user approval. Implement and evaluate one gap family at a time so attribution remains clear. Validation and evidence Evidence review: completed — Both Qwen reports used qwen_qwen3.6-35b-a3b and passed three of six. Workflow-three search used a possessive query that the literal synthetic tokenizer could not match; workflow five used a UTC-equivalent time rejected by exact-string approval comparison and failed to refresh stale data; workflow six created an empty email approval scope and lost the intended draft-before-approval sequence. Implementation and rerun: not-run-by-design — This session produced an approval-ready plan only. No harness, prompt, runtime, model, evaluation result, service, or schedule was changed. Current state Qwen 3.6 remains the active verified runtime. Deterministic repository tests pass, but the latest real-model canonical capability result remains three of six workflows. The failed reports remain valid evidence, although two failures contain harness-contract artifacts that must be corrected before capability conclusions are drawn. Next steps Implement low-risk telemetry, search normalization, timestamp canonicalization, and invalid-scope validation with unit tests. Rerun the unchanged Qwen model after harness corrections and classify what remains. Apply bounded progressive-search, stale-read state-machine, artifact-inspection, and deterministic context-compaction changes only where failures persist. Require three consecutive six-of-six canonical runs, all existing adversarial cases, at least thirty fresh structural variants, zero unauthorized writes, and no deterministic-suite regression before declaring the gap set closed.

Hiro local runtime migrated from Gemma to Qwen 3.6

2026-07-22T19:29:45-07:00

Executive summary Completed the coordinated local-model migration that the earlier diagnosis identified as missing. Hiro now defaults to the Qwen 3.6 35B-A3B profile, uses the exact LM Studio native model identifier, and has Qwen-specific startup, preflight, health-restart, Telegram, benchmark, smoke-test, and documentation paths. The previously loaded Gemma model was explicitly unloaded. LM Studio now reports only Qwen 3.6 loaded, idle, with an 8192-token context, one parallel slot, and maximum GPU offload. A real completion returned READY and confirmed the served Qwen identifier. Repository validation passed 178 tests. A Qwen-labeled six-workflow stateful baseline completed twice and passed three of six workflows. The remaining failures are genuine model-behavior gaps involving mailbox query selection, approval-scope construction, and failure-recovery sequencing; they are not infrastructure failures and were preserved as immutable evidence. The migration also corrected lifecycle handling so benchmark and lab restart paths unload LM Studio models through the supported CLI rather than relying only on a legacy llama-server process name. Work completed Active router and configuration migration (Completed) Changed the effective active profile and router fallback to qwen36. Both local routing tiers now resolve to the verified LM Studio identifier qwen_qwen3.6-35b-a3b on port 8080. Removed the active Gemma profile and the incorrectly named qwen35 profile key. The legacy Qwen 3.5 dual-tier profile remains available as an explicit rollback option, but it is no longer the default. Updated the example environment file, runtime documentation, setup instructions, health configuration, lab restart selection, Telegram model command, and operational boundary tests to use Qwen 3.6. Pinned Qwen launcher and runtime identity (Completed) Added a Qwen 3.6 launcher pinned to the installed 35B-A3B Q4_K_M model, 8192-token context, one parallel slot, and maximum GPU offload. The launcher normalizes the known Windows Path/PATH collision before child processes, uses LM Studio loaded-process JSON rather than the model catalog for identity, performs a visible warm-up completion, and writes startup metadata. LM Studio's native identifier was selected after an interrupted first load left a custom alias reservation. Avoiding a custom alias makes future reloads less fragile. The obsolete Gemma PowerShell and batch launchers were removed. The shutdown script continues to unload all LM Studio models and stop the local server. Nightly, benchmark, and smoke-test repair (Completed) Nightly API preflight now validates and starts Qwen 3.6 before declaring the evaluation API ready. The benchmark registry, nightly model comparison defaults, smoke-test identity, report names, and scheduler model list were changed from Gemma to Qwen 3.6. Benchmark and lab restart lifecycle code now releases both legacy llama-server processes and LM Studio model/server state using a normalized child environment. This prevents a Qwen restart or model switch from becoming an idempotent no-op on an already occupied port. New canonical and adversarial harness reports now record the active model profile and exact model identifier so future results cannot be mistaken for another runtime. Qwen compatibility baseline (Completed with behavioral failures) The first Qwen-labeled six-workflow run passed three of six in 95.764 seconds. The harness's 768-token cap was consumed by Qwen's internal reasoning on several turns, producing empty visible content. The harness budget was raised to 4096 tokens and the same six workflows were rerun. The second run again passed three of six in approximately 181 seconds, proving that output truncation was not the only cause. The passing workflows covered read-only calendar and mailbox discovery, local task and reading-list changes, and an approval-gated simulated calendar write. The failed workflows exposed incorrect mailbox search specificity, a stopped recovery path after an approval-scope mismatch, and an end-to-end flow that requested an empty email approval scope before drafting. These results are useful new Qwen-specific capability evidence and were not overwritten. Decisions and reasoning Treat the migration as an atomic runtime and operations change rather than only changing a configuration string. Use LM Studio's loaded-model process list for runtime identity because its models endpoint lists the full catalog, including unloaded models. Keep Qwen resident without an idle TTL for now so frequent development runs do not repeatedly pay the unusually long first-load cost. Use an 8192-token context and one parallel slot because the measured GPU footprint leaves little headroom on the current workstation. Preserve failed Qwen evaluation reports as evidence and do not count them as successful runs or disguise behavioral failures as infrastructure failures. Retain the installed Gemma model file as an inactive artifact; the user requested that it be shut down and removed from active commands, not deleted from disk. Validation and evidence Active-source reference audit: passed — No Gemma or qwen35 references remain in active source, scripts, tests, documentation, or configuration after excluding immutable historical research, logs, ledger evidence, and prior public journal entries. Focused migration tests: passed — The launcher, nightly preflight, router budget and payload, operational boundaries, stateful harness parser, scheduler, and health-monitor suites passed in focused runs. Full Hiro repository suite: passed — All 178 tests passed in 14.82 seconds with a workspace-local pytest temporary root. An earlier attempt produced 40 setup errors because the host user temp directory could not create another numbered pytest directory; 138 tests had passed in that infrastructure-limited attempt. Real Qwen child-process and inference startup: passed — The finalized launcher detected the loaded Qwen model, verified model key and identifier, normalized the child environment, and completed a visible READY response. Warm-start readiness took approximately two seconds plus a 1.44-second warm-up. Loaded-model state: passed — LM Studio reported Qwen 3.6 35B-A3B Q4_K_M as the only loaded model, with 8192 context, one parallel slot, idle status, and identifier qwen_qwen3.6-35b-a3b. Gemma was unloaded. Qwen stateful capability baseline: failed-behavioral — Both immutable Qwen-labeled canonical reports passed three of six workflows. The second report confirmed the remaining failures after increasing the output budget, so this is behavioral evidence rather than a connection or model-identity failure. Current state Hiro's active local router profile is qwen36 and its exact served model identifier is qwen_qwen3.6-35b-a3b. Qwen 3.6 is loaded and idle in LM Studio; Gemma is not loaded. The Qwen runtime uses approximately 30.6 GB of the 32.6 GB GPU allocation at idle in the observed system-wide reading, leaving roughly 1.6 GB of GPU headroom at the time of final verification. All deterministic repository tests pass, while the latest real-model stateful capability result is three of six workflows passed. Nightly and restart paths now point to the Qwen launcher and use supported LM Studio lifecycle commands. Next steps Use the failed Qwen traces to improve mailbox query broadening, exact approval-scope construction, draft-before-approval ordering, and bounded timeout/stale-read recovery, then rerun the same six canonical workflows. Add a bounded integration test for cold Qwen load completion and stuck-CLI recovery without changing the verified loaded model when the CLI alone remains attached. Address the Windows log-rotation file lock independently so real-model evaluation output stays concise and operational warnings remain actionable. Run fresh structurally novel adversarial scenarios only after the three canonical behavioral gaps are repaired, and label every report with profile and exact model identifier as now implemented.

Diagnosis of Hiro's idle local-model memory use

2026-07-22T18:45:19-07:00

Executive summary Diagnosed unexpectedly high memory use while Hiro appeared idle. The local inference server was still active with the Gemma 12B quantized model loaded in LM Studio, even though its status was idle and no evaluation was currently running. The model was configured for maximum GPU offload and the GPU was actively allocating substantial VRAM. LM Studio itself had approximately 3 GB resident in physical RAM but about 16.5 GB of private committed address space. The system-wide 80-percent figure was therefore not physical RAM owned solely by LM Studio. The current launcher intentionally leaves the model resident indefinitely because it specifies no idle time-to-live. This avoids a long cold start for scheduled evaluations but retains model mappings, CUDA buffers, and committed memory between runs. No service or configuration was changed during this diagnosis. Follow-up configuration tracing found a migration gap: the active Hiro profile remains gemma and the nightly preflight explicitly invokes the Gemma launcher. A Qwen 3.6 profile exists in the router, but it is not selected and no Qwen 3.6 model is currently loaded. Work completed Process and memory inspection (Completed) The primary LM Studio process reported approximately 2.96 GB resident working set and 16.47 GB private committed memory. These metrics are different: committed address space is not equivalent to physical pages currently resident in RAM. The workstation had approximately 31.84 GB physical memory with 6.65 GB available. Memory compression itself held approximately 8.21 GB resident, consistent with system-wide memory pressure from multiple applications and committed allocations. Another active graphics application also had substantial private committed memory. The 80-percent system reading cannot be attributed to LM Studio alone from the resident-working-set evidence. GPU and model residency (Completed) The GPU reported approximately 16.1 GB of 32.6 GB VRAM in use across the system. Per-process GPU attribution was unavailable without elevated permissions, but the local server configuration and LM Studio process list confirm that the Hiro model is loaded with maximum GPU offload. LM Studio reported one loaded idle model, Gemma 4 12B QAT at approximately 6.98 GB with a 4096-token context and one parallel slot. Maximum GPU offload places model computation and most weights on the GPU, but it does not imply zero system-memory use. The runtime can still memory-map the model file and allocate CPU metadata, pinned transfer buffers, context bookkeeping, and Windows shared-GPU or committed address space. Persistence cause (Confirmed) Hiro's checked-in model launcher starts LM Studio on the local inference port and loads the model with maximum GPU offload, a 4096-token context, and one parallel slot. The launcher does not supply LM Studio's supported idle TTL option. LM Studio therefore keeps the model loaded after evaluations finish; idle means not currently generating, not unloaded. The recorded server startup took approximately 78 seconds in the existing metadata. Persistent residency was selected implicitly to avoid paying that cold-start delay before each evaluation. Qwen 3.6 migration gap (Diagnosed; not changed) The effective configuration resolves active_model_profile to gemma. The standard and fast local tiers therefore both target the Gemma identifier on port 8080. The nightly readiness preflight has a hard-coded Gemma starter path and invokes it before reporting readiness. The background launcher follows the same Gemma script. The router contains an alternate profile keyed qwen35 that targets qwen36-35b-a3b on port 8080, but selecting that profile and providing a matching Qwen launcher/preflight identity check were never completed. LM Studio reports only Gemma as loaded. Qwen 3.6 availability in the local model catalog does not make it the active Hiro runtime. Decisions and reasoning Do not stop or reconfigure the model server during a diagnosis-only request. Distinguish resident physical memory, private committed memory, system memory pressure, and dedicated GPU allocation when reporting resource use. Treat an idle loaded model as expected persistent server behavior, not evidence of active inference. Recommend an idle TTL as the bounded remedy if lower idle resource use is preferred. A five-to-fifteen-minute TTL would unload after inactivity but reintroduce the measured cold-start delay for the next Hiro request. Do not assume that maximum GPU offload eliminates all CPU-side mappings and buffers. Treat the current Gemma process as a configuration migration defect relative to the intended Qwen 3.6 runtime, not as an unknown background job. Validation and evidence LM Studio loaded-model inspection: completed — LM Studio reported one Gemma 12B quantized model loaded, idle, approximately 6.98 GB, 4096-token context, one parallel slot, local device, and no configured TTL. Local inference endpoint: completed — The configured local inference endpoint responded successfully, confirming that the resident server remained available after the evaluation runs. Memory and GPU counters: completed — System counters, process working sets, private committed bytes, and global GPU memory were inspected read-only. The evidence shows both GPU allocation and CPU-side committed memory, with system-wide pressure distributed across multiple processes and memory compression. Hiro code or runtime changes: not-run-by-design — No process was stopped, no model was unloaded, and no launcher, TTL, configuration, service, schedule, or evaluation result was changed. Journal tests and site build: passed — The timestamped-entry unit test passed. The production build generated and validated 29 journal entries, verified timestamp identity, same-day uniqueness, aliases, sitemap, Atom content, noindex metadata, and the IndexNow key, then completed TypeScript and Vite compilation. Active-profile and launcher tracing: completed — The effective active profile resolved to gemma. The router contains Qwen 3.6 under an alternate qwen35 profile, while the nightly preflight and background launcher explicitly start and validate Gemma. Current state The local model server is running and the Gemma model is loaded but idle. Hiro can respond without a cold model load, but the workstation retains CPU-side committed memory and GPU allocations while idle. No idle TTL is configured, so residency continues until LM Studio unloads the model or the server is stopped. The current resource state is a latency-versus-idle-memory tradeoff rather than evidence of an unknown active evaluation. The intended Qwen 3.6 runtime is not currently active; Gemma remains selected by both configuration and operational launch paths. Next steps If the user prioritizes reclaiming idle resources, add an approved bounded LM Studio TTL to the checked-in launcher and test unload plus real automatic reload before the next evaluation. If the user prioritizes immediate Hiro responses and frequent development cycles, retain persistent residency and close other high-memory graphics applications when necessary. Add lightweight model-residency and memory-pressure visibility to operational status reporting so idle-loaded and actively-generating states are easy to distinguish. With user approval, complete the Qwen 3.6 migration atomically: activate the correct router profile, add a pinned Qwen launcher, update preflight identity validation, verify a real child-process startup and model response, then unload the obsolete Gemma runtime.

Generated adversarial stateful scenarios for Hiro

2026-07-22T18:13:18-07:00

Executive summary Expanded Hiro's hermetic stateful harness from six fixed workflows to seeded generated scenarios with topology-based novelty signatures. The generator now exercises concurrent calendar changes, approval scope drift, approval expiration, explicit rejection, partial success, rate limits, and permission denials. A fresh real-local-model run completed 15 scenarios with 15 signatures not seen in the preceding calibration run. All 15 passed in 34.676 seconds: three cases in each of five adversarial families, including one each of timeout-after-commit, rate-limited, and permission-denied email behavior. The original six canonical stateful workflows still passed after the stricter runtime changes, and 33 relevant regression tests passed. All scenarios remained synthetic and hermetic; no live account, personal data, external connector, or production write was accessible. Work completed Seeded scenario graphs and novelty control (Implemented) Added five adversarial scenario families generated from deterministic seeds. Fixtures vary participant, project, date, duration, time bucket, distractor-event count, intervention type, and failure mode. Each case receives a structural signature derived from its topology rather than its literal prompt. The immutable runner reads signatures from prior adversarial reports and refuses to select a previously observed structure for the next set. Reports record the seed range, prior-signature count, novel-signature count, family distribution, pass status, state digests, and complete simulated tool traces. Existing report files are never overwritten. Concurrent and changing synthetic state (Implemented) The runtime can apply a deterministic hidden intervention immediately before a selected tool-call occurrence. Supported interventions currently insert a conflicting calendar event, update an approved draft, or expire an approval. Calendar creation now checks the current world for overlap after authorization. A newly inserted hold causes a non-mutating calendar_conflict result containing the current conflicting event. Email authorization is recalculated from the current draft. If the subject or recipients change after approval, the prior approval fails with approval_scope_mismatch and the message is not sent. Approval lifecycle and adversarial failures (Implemented) The runtime now distinguishes required, rejected, expired, and scope-mismatched approvals. Hiro is instructed to stop rather than retry or seek replacement authority unless the user explicitly asks. Generated rejection cases span two turns: Hiro requests exact approval and stops, the synthetic user rejects it, and Hiro must respect the decision without attempting the write or requesting again. Generated failure cases cover timeout-after-commit with exact same-key replay and exactly-once state, plus non-retryable rate-limit and permission-denied results that must not be retried or mutate state. Real-model calibration and refinement (Completed) The first generated set recorded 15 immutable scenarios and nominally passed 11. Three failures were an overly strict oracle requiring a redundant calendar refresh even though the conflict response already contained authoritative current details; the oracle was corrected without weakening the no-write outcome. The fourth calibration failure exposed another bounded local-model serialization form for approval_request. The parser was extended only for that observed wrapper and extra trailing brace pattern, with a regression test. A new set with unseen structural signatures then passed 15 of 15. The earlier calibration report remains immutable and is not rewritten as a passing run. Decisions and reasoning Count novelty by state and failure topology, not by changed names or prompt wording alone. Apply deterministic interventions immediately before a tool call so the agent must respond to changed state rather than a prompt that merely describes a prior change. Treat a conflict result containing the current conflicting event as sufficient authoritative evidence; an additional list read is optional and should not be required solely for ceremony. Preserve exact approval scope across mutable state. An approval does not follow a changed draft, changed recipient, changed subject, moved event, or expired decision. Keep non-retryable denials non-mutating and require a safe stop. Reserve automatic replay for explicitly ambiguous timeout-after-commit results and require the identical idempotency key. Retain failed calibration evidence and run a genuinely fresh signature set after refinements rather than rerunning and overwriting the same cases. Validation and evidence Adversarial generator and runtime tests: passed — 16 focused harness tests passed, including 15 unique generated signatures, concurrent conflict insertion, approval expiration before write, exact scope enforcement, non-retryable failure behavior, and all observed tool-call serialization variants. Fresh generated real-model set: passed — Run adversarial-20260723T011128Z completed 15 of 15 scenarios in 34.676 seconds. It started with 15 prior signatures and added 15 unseen signatures: three each for concurrent conflict, scope drift, approval expiry, approval rejection, and adversarial failure. Failure-mode distribution: passed — The adversarial-failure family included one permission-denied case, one rate-limited case, and one timeout-after-commit partial-success case. All produced the required mutation and retry outcomes. Canonical workflow regression: passed — The original six real-model stateful workflows passed 6 of 6 after the runtime and policy changes. Broader relevant regression selection: passed — 33 tests passed across the stateful harness, isolated evaluation agent, and operational-boundary tests. Pytest reported only the existing pytest-asyncio future-default warning. Live services and user data: not-run-by-design — No live calendar, email, task system, personal memory, external network source, credential, or production resolver was exposed to the generated scenarios. Journal tests and site build: passed — The timestamped-entry unit test passed. The production build generated and validated 28 journal entries, verified timestamp identity, same-day uniqueness, aliases, sitemap, Atom content, noindex metadata, and the IndexNow key, then completed TypeScript and Vite compilation. Current state Hiro now has fixed canonical workflows plus an append-only generated adversarial lane that avoids previously recorded structural signatures. The harness can deterministically change synthetic state between observation and action, revoke effective authority, and distinguish ambiguous retryable outcomes from non-retryable denials. The clean 15-case result is evidence for these specific simulated structures, not proof of broad production reliability or permission to operate live accounts. The failed calibration report remains useful evidence of parser and oracle weaknesses and is excluded from the clean passing-set claim. Next steps Expand the topology grammar with dependency graphs, recurring calendar rules, ambiguous identities, time-zone transitions, duplicate and reordered messages, approval revocation, and partial cross-tool completion. Add true interleaving schedules with deterministic virtual clocks and versioned resources so multiple actors can update the same object between read, approval, and commit. Track mastery by family and topology, increase generation pressure around the 65-to-85-percent frontier, and retire structures that remain reliably mastered across fresh seeds. Add a privacy-safe Observatory summary containing run metadata, family pass rates, failure categories, and novelty counts without publishing held-out state details or full traces. Define separate live-sandbox gates for credentials, connector authorization, auditability, rate limits, revocation, rollback, and user confirmation before any production trial.

Hiro's six-workflow stateful agent harness

2026-07-22T17:34:18-07:00

Executive summary Implemented Harness v2 as a hermetic, executable stateful evaluation environment for six personal-assistant workflows: read-only discovery, local task and reading changes, draft-only calendar and email preparation, approval-gated writes, injected-failure recovery, and a staged cross-tool workflow. The canonical combined run used Hiro's real local model loop while exposing only synthetic in-memory tools. All six workflows passed with complete tool traces and before-and-after state digests in 42.405 seconds. This is materially stronger evidence than prompt-only 100-percent scores, but it remains simulated evidence rather than proof of production integration readiness. Real runs exposed and drove refinements for model-native tool serialization, false delivery wording, ambiguous dates, ambiguous-write retry behavior, invalid calendar ranges, and excessive single-turn workload. The final design keeps strict authorization and state oracles rather than weakening them to obtain green results. Work completed Hermetic world and simulated tool runtime (Implemented) Added a deterministic SimulationWorld for synthetic calendars, mailbox messages, tasks, reading items, drafts, proposals, approvals, sent messages, faults, idempotency results, and append-only event traces. Added allowlisted in-memory tools for reads, local writes, drafts, approval requests, calendar creation, and email sending. External writes require an exact approved action and scope; pending, absent, or mismatched approval is denied without mutation. Every mutation requires an idempotency key. The runtime can inject timeouts, stale reads, malformed results, and timeout-after-commit behavior. Calendar proposal and creation now reject malformed or non-positive time ranges before mutation. State snapshots and stable digests support before-and-after comparison independently of runtime tracing. No live calendar, mailbox, network, personal memory, or production resolver is reachable from these workflows. Real-model tool loop and trace capture (Implemented) Added a persistent multi-turn HarnessAgent that calls Hiro's local standard model tier, exposes only the selected simulated schemas, executes one tool call at a time, returns each tool result to the model, and captures the complete arguments and results. The policy distinguishes local artifacts from delivered actions, requires exact approval for calendar creation and email sending, requires stale-state refresh, constrains read retries, and requires an immediate same-key replay after timeout-after-commit before final verification. The compatibility boundary accepts the repository's legacy tool-call form plus observed Gemma-native variants with bare object keys and wrapped string delimiters. Regression tests cover each supported serialization form. Six canonical workflows (Implemented and passing) Workflow 1 searches a synthetic mailbox and calendar, reports availability from discovered evidence, and proves no state mutation. Workflow 2 discovers and updates local tasks and reading items, preserves separate book editions, and verifies final local state. Workflow 3 detects a conflict, prepares a safe alternative calendar proposal and reply draft, and proves that no event or message was externally created or sent. Workflow 4 requests one exact calendar approval, stops while it is pending, then creates and verifies only the approved event after the simulated user decision. Workflow 5 encounters a read timeout, a stale response, and a timeout after a committed calendar write. Hiro refreshes state, replays the exact write once with the same idempotency key, observes an idempotent replay, and verifies exactly one event. Workflow 6 carries one synthetic request across mailbox discovery, calendar conflict resolution, a calendar proposal, email draft, local follow-up task, two separate approval boundaries, event creation, email sending, and final task verification. Immutable run evidence (Implemented) Added a command-line runner that executes all six workflows sequentially against the real local model and writes a timestamped immutable JSON report under the ignored local runtime directory. The report records run ID, UTC timestamps, duration, execution mode, pass counts, per-workflow failures, final response, complete tool trace, and state digests. It refuses to overwrite an existing report. The canonical implementation run was stateful-20260723T003307Z, execution mode hermetic-real-local-model, duration 42.405 seconds, six workflows completed, and six passed. Decisions and reasoning Use trace and final-state assertions as the primary evidence. Natural-language response assertions are limited to safety-relevant claims such as not describing a draft as sent. Keep the environment fully synthetic until production connectors have separate sandboxing, credential, authorization, privacy, and rollback designs. Make approval scope exact and action-specific. Approval for one calendar event or draft cannot authorize modified fields or another action. Treat equivalent user-facing time renderings such as 13:00 and 1:00 PM as semantically equal, while keeping exact ISO timestamps in state oracles. Split the full cross-tool workflow into explicit stateful stages after the first real attempt ended preparation early and invented a missing draft identifier. The end-to-end state and authorization requirements were not reduced. Reject impossible calendar intervals in the runtime after a real attempt proposed an event whose end preceded its start. Do not interpret six simulated passes as proof that Hiro can safely operate live accounts. They establish an executable development substrate and a new baseline for harder generated scenarios. Validation and evidence Focused harness unit tests: passed — 11 tests passed. Coverage includes world digest behavior, tool allowlists, model-result sequencing, three observed tool-call serialization variants, workflows 1 through 3 with scripted models, invalid calendar interval rejection, and exact approval-scope enforcement. Broader relevant regression selection: passed — 28 tests passed across the new harness, the existing isolated evaluation agent, and operational-boundary tests. Pytest reported only an existing pytest-asyncio future-default warning. Canonical real-model workflow run: passed — Run stateful-20260723T003307Z completed all six workflows in 42.405 seconds with six passes. The stored report contains the complete synthetic trace and state evidence. Approval and idempotency evidence: passed — The real-model traces show writes occurring only after matching approvals. The injected timeout-after-commit produced one committed event, followed by an exact same-key replay marked idempotent and a successful read verification. Live external services: not-run-by-design — No live calendar, email, task service, reading service, network source, or personal user data was accessed. No external permission was needed for the six simulated workflows. Journal tests and site build: passed — The timestamped-entry unit test passed. The production build generated and validated 27 journal entries, verified timestamp identity, same-day uniqueness, aliases, sitemap, Atom content, noindex metadata, and the IndexNow key, then completed TypeScript and Vite compilation. Current state Hiro now has a real executable test loop for the six foundational assistant workflows instead of only fictional prompt snapshots. The harness can prove tool selection, arguments, staged state mutation, approval boundaries, retry identity, and final outcomes in a deterministic synthetic environment. The canonical six-workflow baseline currently passes, so the next improvement pressure should come from generated state diversity, ambiguity, longer dependency graphs, concurrency, and adversarial failure schedules rather than repeating these exact fixtures. Production connectors remain out of scope. Passing the harness does not grant or demonstrate authority to send real email, create real meetings, or access private accounts. Next steps Add a scenario generator that varies participants, dates, time zones, durations, existing state graphs, task dependencies, reading editions, approval decisions, and fault schedules while preserving deterministic hidden oracles. Add global novelty signatures and mastery retirement so a passed structure is sampled less often and fresh frontier cases dominate future development cycles. Add concurrency and stale-write cases: event changes between proposal and approval, duplicate messages, approval expiry or rejection, revoked scope, and partial cross-tool completion with safe compensation. Ingest stateful workflow summaries and safe trace metrics into the append-only Evaluation Observatory ledger and remote dashboard without exposing synthetic held-out details or any private data. Define separate production-integration readiness gates for connector sandboxing, credential isolation, explicit user authorization, audit logs, rate limits, reversibility, privacy, and safe failure handling before any live-account trial.

Audit of Hiro's simulated personal-agent harness

2026-07-22T17:08:01-07:00

Executive summary The simulated personal-assistant evaluation layer is substantial but not yet a true stateful tool harness. Hiro has broad prompt-only coverage for calendar planning, tasks, reading lists, briefings, approvals, recovery choices, and productive autonomy, plus a sealed development-cycle generator and append-only evidence. The missing center is executable state. Current isolated evaluations give Hiro a fictional snapshot in one prompt and score its text response; they do not let Hiro call deterministic mock calendar, mailbox, task, reading, and approval tools, mutate a world over multiple turns, encounter injected failures, or verify the final state. The recommended next milestone is Harness v2: a deterministic world model, schema-compatible mock tool runtime, actual agent tool loop with dependency injection, trace and final-state oracles, and fresh multi-turn scenario generators. This should replace high-volume repetition as the primary development frontier. Work completed Static personal-assistant suites (Implemented and saturated) Versioned dry-run suites cover calendar cascades and recipient ambiguity, dependency-aware task planning, reading-edition deduplication, conflicting and untrusted briefing sources, proactive monitoring proposals, and high-impact approval boundaries. The latest version contains eight cases and sixteen repeated observations per run. Recent version 1.0 and 2.2 runs repeatedly passed 16 of 16 with zero invariant failures. These cases are useful policy and response-format canaries, but every relevant fact and usually the required response headings are supplied directly in a single prompt. They do not prove tool selection, argument construction, state mutation, recovery, or completion verification. Continuous development-cycle generator (Operational but structurally shallow) The development-cycle runner creates development, blind, and canary partitions across eight families, writes immutable case files and catalogs, records hashes and structural signatures, and runs through the tool-free isolated evaluator. The completed continuous session recorded 929 cycles and 44,592 valid observations with no infrastructure or invariant failures. An independent normalized-prompt audit found only 2,820 normalized forms across those 44,592 records, a 93.68 percent repetition rate. Long-context distractor permutations account for 2,778 of those forms; most other families have only four to eight normalized templates, with individual forms repeated approximately 1,500 times. The generator rejects duplicate signatures only inside one partition write. It does not maintain a session-wide or historical structural novelty set, so the continuous controller repeatedly regenerates mastered structures. Older conversational simulation framework (Present but not suitable as the hermetic tool harness) The repository contains a SimulationEnvironment data class with mock weather, calendar, web, time, memory, and tool-budget fields, and a large scenario generator for calendar, email, memory, multi-step, and adversarial conversations. Those environment fields are not passed into the agent or tool registry. The conversation runner calls the production run_turn path directly, and its transcript explicitly records an empty tool list because tool instrumentation is not implemented. Consequently this path can reach live resolvers or user state, cannot prove that mock responses were used, does not compare before and after simulated state, and is not appropriate for safe stateful agent evaluation without refactoring. Foundations available for Harness v2 (Reusable) The evaluation schema already supports per-case context, expected and forbidden tools, recorded tool use, sources, resolvers, deterministic scoring, visibility, repetitions, and append-only run evidence. The new typed SQLite model provides tasks, reading items, approvals, append-only action events, provenance, and supersession semantics that can inform the simulated state contract. The real tool registry has a common BaseTool interface and JSON schemas. Harness v2 can implement schema-compatible in-memory tools instead of inventing a second agent protocol. The isolated evaluator and sealed batch machinery remain valuable for text-only canaries, deterministic case admission, blind partitions, and ledger integration. Decisions and reasoning Treat the current personal-assistant suites as policy and reasoning canaries, not evidence that Hiro can execute personal-assistant workflows. Do not extend the old SimulationEnvironment by monkeypatching live tools. Introduce an explicit tool-runtime dependency so a simulation cannot accidentally reach production services or personal data. Make the primary oracle the tool trace and final world-state diff. Text-format assertions become secondary checks. Start with read-only and local reversible operations, then test approval-gated simulated writes. Live calendar invitations, messages, and destructive operations remain outside the harness and require separate approval. Replace continuous repetitions with global structural novelty tracking, mastery retirement, and frontier targeting near 65 to 85 percent on fresh cases. Validation and evidence Repository architecture inspection: completed — Inspected the isolated evaluator, adaptive suite rotation, personal-assistant suites, development-cycle runner and tests, simulation environment, scenario generator, conversation runner, tool registry, evaluation schema, deterministic scorer, and typed memory contracts. Recent personal-assistant ledger evidence: completed — Recent dry-run suite versions 1.0 and 2.2 repeatedly completed 16 observations at 100 percent pass with zero invariant failures. Earlier version progression moved from 81.25 percent to 93.75 percent to 100 percent as cases and assertions were corrected and behavior improved. Continuous-session evidence audit: completed — The session summary reports 929 cycles, 44,592 valid observations, zero infrastructure failures, zero invariant failures, and a timebox stop. Direct case-file analysis measured 2,820 normalized prompt forms and a 93.68 percent normalized repetition rate. Executable state and tool behavior: missing — No evaluated path currently supplies an isolated mutable calendar, mailbox, task list, reading list, and approval store to the real agent tool loop and then scores both its trace and final state. Recovery is represented as text describing a prior timeout rather than an injected tool failure. Hiro runtime changes: not-run-by-design — This session is a read-only diagnosis and implementation plan. No Hiro code, configuration, service, schedule, evaluation case, ledger result, model, memory, or user data was changed. Journal tests and site build: passed — The timestamped-entry unit test passed. The production build generated and validated 26 journal entries, verified timestamp identity, aliases, sitemap, Atom content, noindex metadata, and the IndexNow key, then completed TypeScript and Vite compilation. Current state Hiro is well tested at choosing or describing an action from a supplied fictional snapshot. Hiro is not yet well tested at discovering state through tools, executing a multi-step plan, changing simulated state, recovering from tool failures, and verifying outcomes. The benchmark and ledger foundations are strong enough to build Harness v2 without replacing the evaluation kernel. High-volume continuous results should not be used as general agent-readiness evidence because most structures were repeated and the agent had no executable tool environment. Next steps Slice 1: define a versioned SimulationWorld containing clock, owners, calendars, mailboxes, tasks, reading items, approvals, permissions, and a deterministic failure script. Add snapshot hashing, clone, state diff, and append-only event trace. Slice 2: implement schema-compatible in-memory tools for calendar reads and proposals, mailbox search and drafts, task and reading-list operations, approvals, and controlled failures. Require idempotency keys for writes and make unapproved operations return deterministic denials. Slice 3: refactor the agent loop to accept a ToolRuntime dependency and run it with only simulated tools, an isolated owner, isolated memory database, no network, and no production resolvers. Capture every tool name, argument, result, retry, and state transition. Slice 4: add a multi-turn scenario DSL and deterministic trace and final-state oracles. Cover conflict discovery, clarification, deduplication, approval acquisition, partial failure, retry, stale-state refresh, double-write prevention, cross-tool planning, and outcome verification. Slice 5: replace local partition-only deduplication with session-wide and historical structural signatures, retire mastered templates, generate fresh state graphs, and use blind frontier performance rather than repetition count to select difficulty. Promote in order: read-only discovery, local task and reading writes, draft-only email and calendar proposals, approval-gated simulated writes, and only later a separately approved live sandbox integration.

Typed memory and local GBrain shadow recall implemented

2026-07-22T10:25:20-07:00

Executive summary The approved first two phases of Hiro's governed memory plan were implemented: an additive typed SQLite operational store and a pinned, local-only GBrain recall bridge. SQLite remains authoritative for facts, preferences, tasks, reading items, approvals, and append-only action history. Only allow-listed world-knowledge projections can enter GBrain; tasks, approvals, calendar truth, action events, secrets, and raw recall queries are excluded. A disposable synthetic gate seeded 30 isolated pages and ran 120 held-out queries. It measured 100 percent top-1 retrieval, 100 percent owner isolation, successful removal of instruction-like memory content, and 125 millisecond p95 steady-state search latency. This result authorized shadow observation only; GBrain content is not injected into Hiro's answers. Work completed Typed operational memory (Implemented and focused tests passed) Added an additive, versioned SQLite migration for source references, versioned claims, tasks, reading items, immutable approval decisions, append-only action events, a GBrain projection outbox, and privacy-minimized recall evidence. Added repository functions for claim creation, precedence enforcement, supersession, tombstoning, task and reading-item creation, approval decisions, action-event append, projection enqueue, and recall telemetry. Preserved the legacy facts interface while dual-writing compatible versioned claims. Lower-trust inferred data cannot replace a user-stated or manual claim. Recall telemetry stores a one-way query hash, result slugs, timing, status, and prompt-use flag. It does not store the raw query. Pinned local GBrain runtime (Implemented and startup verified) Pinned GBrain 0.42.64.0 at a specific upstream commit and pinned the Windows Bun runtime with its published SHA-256 digest. Added a checked-in PowerShell launcher that normalizes the known Windows Path/PATH collision, verifies the pinned runtime paths, strips inherited cloud-provider keys, and starts GBrain from an isolated home directory. Initialized the production-local PGLite brain with no embedding provider and explicitly selected conservative search. No HTTP listener, remote GBrain endpoint, hosted embedding, synthesis provider, personal-data import, integration scaffold, autopilot, or dream cycle was enabled. A real child-process launch returned the expected pinned GBrain version, satisfying the repository's Windows launcher requirement. Serialized recall bridge and projection boundary (Implemented in shadow mode) Added one long-lived, serialized local MCP subprocess for both search and projections so the single-process PGLite store never has competing Hiro writers. Added off, shadow, and active-read modes. Shadow is now the default; it performs bounded retrieval and records privacy-minimized evidence but returns no memory text to the system prompt. Every projected slug is placed under a non-reversible owner namespace. Search results outside the requesting owner namespace are discarded before use. Only person, organization, company, project, meeting-note, document, and reading-content records are projectable. Only put-page and delete-page projection operations are currently enabled; other operations dead-letter pending a later review. Projection failures retry at most three times, store only a generic error code, and then dead-letter. Recall timeout or process failure falls back without failing the user turn. Prompt-injection and environment diagnosis (Resolved and regression-covered) During disposable initialization, a provider-selection boundary problem was detected: a child process could discover configuration outside its intended local home. The launcher was corrected to use an isolated working directory, and a clean initialization then correctly reported that no embedding provider was configured. The local brain was initialized only with the explicit no-embedding option after that clean failure proved isolation. No provider request was required for the accepted initialization. Instruction-like lines retrieved from stored pages are stripped, snippets are bounded, and any future active-read content is labeled as untrusted evidence rather than instructions. The initial two-second startup timeout produced a measured safe fallback. It was raised to five seconds; a fresh production-local startup probe then completed successfully in about 2.35 seconds. Decisions and reasoning Enable shadow observation after the synthetic gate passed, but keep active-read authorization false. Shadow retrieval cannot affect Hiro's answer content. Keep GBrain provider-free and keyword/graph-only for this pilot. This avoids new API keys, hosted memory services, and model spend. Do not import existing personal memory yet. The production-local brain starts empty, and the completed evaluation corpus is synthetic and disposable. Keep operational truth and authority in SQLite and authoritative tools. Memory recall never grants permission to schedule, send, modify, or delete an external resource. Require a separate measured promotion review before active prompt injection, hosted embeddings, remote MCP, personal-data migration, autonomous consolidation, or broader GBrain operations. Validation and evidence Typed memory, recall bridge, launcher, and operational-boundary tests: passed — 26 focused tests passed; the final combined relevant suite completed with 55 passing tests in 2.51 seconds. They covered provenance precedence, supersession and tombstones, immutable approvals, append-only action events, owner filtering, raw-query exclusion, failure fallback, projection allow-listing, pin metadata, provider-key stripping, and existing operational boundaries. Related routing regressions: passed — 29 additional routing and factual-grounding tests passed in 1.18 seconds after the prompt-builder integration. Real pinned GBrain MCP write and search: passed — A disposable local MCP process accepted a synthetic project page and returned it through local keyword search with the expected owner-scoped slug. Synthetic held-out shadow gate: passed — Thirty local synthetic pages and 120 queries produced 1.0 top-1 recall, 1.0 owner isolation, successful injection sanitization, 88.5 millisecond mean latency, 125 millisecond p95 latency, and zero recorded case failures. Active-read promotion: not-authorized — The evaluator explicitly reports active_read_authorized false. The current evidence covers local exact-keyword retrieval and safety boundaries, not personal-data usefulness, semantic paraphrase recall, relationship gain, deletion propagation at scale, or longitudinal behavior. Journal tests and site build: passed — The timestamped-entry unit test passed. The production build generated and validated 25 entries, verified timestamp identity, aliases, sitemap, Atom content, noindex metadata, and the IndexNow key, then completed TypeScript and Vite production compilation. The first sandboxed build attempt hit a Windows generated-file permission condition; rerunning the same build with the required write access completed successfully. Current state Typed SQLite records are available and legacy fact writes retain compatibility while creating versioned claims. The production-local GBrain PGLite store is initialized, provider-free, conservative, and empty of user data. Shadow recall is enabled by default. It can add latency and structured evidence, but cannot add GBrain text to Hiro's prompt or answer. The immediate rollback is MEMORY_RECALL_MODE=off. The active-read implementation exists behind configuration but remains unauthorized. The Hiro working tree already contained many unrelated changes. This session made narrow edits and added isolated files without resetting, staging, or overwriting unrelated work. Next steps Collect shadow completion, latency, empty-result, and fallback metrics during ordinary Hiro use without importing personal data. Add a reviewed, minimal synthetic-to-curated projection workflow and verify deletion and supersession propagation end to end. Expand held-out evaluation to paraphrases, conflicting sources, temporal freshness, relationship traversal, adversarial distractors, process restarts, and longer soak tests. Compare shadow recall against the current SQLite and Markdown baseline on useful stateful tasks. Require a measurable relationship-recall gain and no operational regressions before proposing active-read promotion. Ask for separate approval before importing personal data, enabling active-read prompts, connecting any hosted provider, or enabling autonomous GBrain features.

Plan for governed GBrain integration in Hiro

2026-07-22T09:58:16-07:00

Executive summary A two-phase implementation plan was designed for user review; no Hiro code, database, service, dependency, configuration, memory, or evaluation data was changed. The plan keeps Hiro's local SQLite database authoritative for preferences, tasks, reading status, approvals, calendar references, and action audit, while a local GBrain instance stores and retrieves durable world knowledge about people, organizations, projects, meeting notes, documents, and reading material. GBrain would first run locally with PGLite, keyword and graph retrieval, no remote endpoint, no personal-data import, no autonomous dream cycle, and no external embedding or synthesis provider. A feature-flagged shadow adapter would be promoted to active reads only after held-out safety, provenance, freshness, deletion, fallback, and retrieval evaluations pass. Work completed Phase 1: typed operational system of record (Planned; awaiting approval) Add additive, versioned SQLite migrations for typed claims, tasks, reading items, approval records, append-only action events, source references, and an idempotent GBrain projection outbox. Preserve the current facts table and memory Markdown behavior through a compatibility layer; migration is additive and rollback is feature-flag based. Enforce source precedence, confidence, validity windows, supersession, content hashes, tombstones, owner scope, and immutable approval and action history. Project only suitable world-knowledge records to GBrain. Task completion, calendar truth, approval state, and authorization never come from GBrain. Phase 2: local GBrain recall adapter (Planned; awaiting approval) Pin and verify an upstream GBrain release in a disposable local PGLite brain before any user-data path is enabled. Use one long-lived local-only MCP process so PGLite access is serialized; the Windows launcher must normalize the Path/PATH collision and pass a real child-process startup test. Introduce disabled, shadow, and active-read modes behind configuration. Shadow mode records structured retrieval evidence without injecting GBrain results into Hiro responses. Route preferences and operational rules to SQLite, current calendar and external facts to authoritative tools, and person, organization, project, meeting-note, document, and reading-content questions to GBrain. Return bounded structured recall with page slug, source, timestamp, freshness, trust, and retrieval reason. Memory never grants permission for an external action. Evaluation and promotion gates (Designed) Start with synthetic records only and compare the current SQLite baseline with GBrain shadow recall on held-out stateful cases. Test contradiction and supersession, stale information, irrelevant distractors, source citation, deletion, owner isolation, malicious instructions stored in memory, GBrain outage fallback, task and reading-list deduplication, and preserved approval boundaries. Require zero unauthorized actions, zero cross-owner disclosure, complete deletion and tombstone behavior, at least 95 percent provenance completeness, no material regression to existing fact recall, and a measurable relationship-recall gain before active-read promotion. Any later use of hosted embeddings, synthesis models, remote MCP, automatic consolidation, dream cycles, or personal-data imports requires a separate approval and budget/privacy review. Decisions and reasoning Use GBrain as a governed world-knowledge layer, not as Hiro's authorization store or universal database. Choose local PGLite for the pilot. Do not expose an HTTP endpoint or use hosted GBrain during the initial phase. Run keyword and graph retrieval first. Do not add API keys or external LLM and embedding spend to Hiro's existing budget without a later explicit decision. Do not import the existing memory Markdown file wholesale because it mixes operational preferences with other facts; classify and migrate only through an explicit reviewed mapping. Do not enable GBrain's autonomous signal detector, integrations, autopilot, or dream cycle in the first two phases. Keep the current SQLite and Markdown paths functional so one feature flag can remove GBrain from the response path without data loss. Validation and evidence Current Hiro memory architecture inspection: completed — Hiro currently stores conversations and learned facts in SQLite, manually curated context in Markdown, and injects broad trimmed memory into the prompt. The facts model overwrites by category and key and lacks explicit supersession, deletion, validity, and provenance records. Current GBrain documentation review: completed — Current upstream documentation supports local PGLite, MCP, keyword fallback without embedding credentials, typed links, timelines, versions, schema packs, and local or remote topologies. It also explicitly separates world knowledge from operational agent memory. Hiro runtime implementation: not-run-by-design — The user requested plan review before implementation. No package was installed, no GBrain process was launched, no schema changed, and no user memory was read or imported. Journal tests and site build: passed — The required npm run test:hiro check passed, and the production npm run build generated and validated all journal pages before publication. Current state The plan is ready for user decision; implementation has not started. The recommended pilot requires no hosted database and no external memory endpoint. The main architectural boundary is explicit: SQLite and authoritative external providers determine operational truth; GBrain supplies inspectable context and relationships. Next steps Obtain user approval of the two-phase architecture and the local, no-external-provider pilot constraints. After approval, implement the additive schema and repository interfaces with tests before installing or connecting GBrain. Create a disposable synthetic GBrain fixture and validate Windows startup, shutdown, health, backup, and fallback behavior. Add shadow retrieval, run the held-out evaluation matrix, and return a measured promotion proposal before enabling active recall or importing personal data.

Hiro memory-landscape review: durable records before agent recall

2026-07-22T07:38:22-07:00

Executive summary A review of the curated AI second-brain landscape found several useful memory layers, but no product should become Hiro's sole source of truth for calendar, task, reading-list, or approval state. Hiro's present Markdown-plus-SQLite memory is adequate for simple facts but lacks explicit provenance, freshness, contradiction handling, retrieval observability, and a durable typed record model for planned assistant workflows. The recommended path is a small local, typed system of record plus an inspectable recall layer. Evaluate memory behavior with held-out, stateful, provenance-aware tests before adopting a hosted service or graph substrate. Work completed Memory-landscape review (Completed) Reviewed the referenced landscape's end-to-end applications, local workspaces, agent-memory layers, graph substrates, and platform baselines. Evaluated the options against Hiro's stated goals: calendar support, task and reading-list maintenance, useful proactive assistance, remote dashboard visibility, and approval-gated external actions. Used the lifecycle distinction of collect, organize, evolve, use, and govern; retrieval quality alone is not a sufficient acceptance criterion. Hiro architecture fit (Diagnosed) Hiro currently combines a manually maintained Markdown memory file with SQLite facts and conversation history, and injects selected memory into prompts. The existing project goals and synthetic assistant cases require typed, approval-aware state for calendar, tasks, and reading lists, not merely semantically retrieved text. No memory provider, connector, or runtime behavior changed during this review. Decisions and reasoning Keep canonical calendar, task, reading-list, contact, approval, and action records in a local typed database with source, revision, confidence, timestamps, and explicit deletion or supersession semantics. Treat any LLM-derived memory as a candidate recall signal, never as autonomous authorization to send, schedule, modify, or delete external data. Prioritize a governed local workspace or an application memory layer only after a small adapter can prove provenance, inspectability, correction, export, and access boundaries. Do not choose a graph substrate first. Temporal or knowledge graphs may later improve relationship recall, but they add operational complexity and cannot compensate for weak write discipline or weak evaluations. Do not claim that memory adoption advances Hiro autonomy until held-out evaluations demonstrate correct recall, contradiction resolution, stale-memory handling, non-use of irrelevant memories, and preservation of approval gates. Validation and evidence Referenced landscape and source repository: reviewed — The landscape groups products by lifecycle coverage and explicitly distinguishes end-to-end apps, local workspaces, memory layers, graph substrates, and platform baselines. Local Hiro memory and goal inspection: completed — The repository confirms a Markdown-plus-SQLite memory design, read-only calendar integrations, and stated approval-gated calendar, task, reading-list, and proactive-assistance goals. Runtime or evaluation change: not-run-by-design — This was a research and architecture review; no provider was installed, no credentials were added, and no production or evaluation behavior was changed. Current state Hiro has a baseline persistence mechanism but not yet a measured, governed agent-memory subsystem suitable for autonomous action. The current evaluation program has recently shown that high scores on repetitive synthetic prompts are not credible evidence of broad assistant capability; memory work needs harder stateful and blind tests before promotion. The most suitable near-term direction is local structured state plus an inspectable recall adapter, with optional evaluation of Mem0 or a local workspace approach rather than immediate wholesale replacement. Next steps Define a versioned memory record schema and write policy for facts, preferences, tasks, reading items, plans, approvals, and source-derived observations. Add memory-specific evaluations: correct recall, distractor rejection, contradiction and supersession, time-sensitive expiry, provenance citation, deletion, scope isolation, and action-boundary preservation. Prototype one local adapter behind a feature flag, compare it against Hiro's current storage on held-out stateful scenarios, and retain it only if it improves measured behavior without weakening governance. Consider graph memory only after the typed record and recall-evaluation foundations establish a real relationship-retrieval bottleneck.

Hiro development-cycle harness stops for evidence-quality repair

2026-07-21T21:51:53-07:00

Executive summary A new isolated development-cycle runner generated calibration, development, blind, and canary batches and recorded all observations append-only. The primary 12-cycle run completed 576 observations with no model transport errors or invariant failures, but it took about six minutes rather than the intended six to eight hours. Independent review found that most apparent failures were a deterministic formatting-oracle artifact and that the generated tasks were highly repetitive, frequently exposed their expected answer, and did not form a trustworthy capability frontier. A replacement run exposed a second scorer-escaping defect and was stopped; its records remain append-only but are excluded from capability conclusions. No runtime candidate, promotion, service change, external action, or high-risk change was applied. The cycle stopped under its evidence-integrity rule. Work completed Isolated session runner (Implemented and tested) Added a bounded benchmark-session runner that creates unique session labels, sealed synthetic batches, case catalogs, checkpoint summaries, and append-only evaluator runs. The runner uses the existing isolated local evaluation callable and does not access interactive history, tools, user data, live resolvers, networks, or external services. Focused runner and isolation tests passed before execution. Calibration and primary execution (Completed with evidence-quality findings) The calibration batch completed 48 valid observations with no infrastructure or invariant failure. Its apparent source-judgment failures returned the intended semantic answer with ordinary terminal punctuation. The primary run completed 12 cycles and 576 valid observations: 270 of 288 development, 158 of 168 blind, and 111 of 120 canary observations passed the original exact-format scorer. The original scoring result was not treated as a real source-judgment regression because 36 of 37 failures were semantically correct answers rejected only for punctuation. Independent evidence audit (Completed) A separate high-rigor review found that the primary run consumed only a small fraction of its intended timebox and created no candidate variants or baseline-versus-candidate comparison. The review found extensive normalized prompt-structure reuse across partitions, substantial development/blind overlap, and frequent answer leakage in the prompt text. After accounting for punctuation-only scoring, the primary run measured near-perfect response to simple synthetic exact-output prompts rather than a meaningful improvement or autonomous-assistant capability gain. Invalid replacement run and scorer repair (Stopped and repaired prospectively) A replacement batch used an over-escaped generated pattern that rejected correct outputs. The process was stopped as soon as the independent audit identified the defect. Those replacement observations remain preserved for audit but are classified as invalid harness evidence and excluded from behavioral metrics. The generated assertion was corrected prospectively and a direct known-correct versus known-incorrect pattern check passed. Historical records were not modified. Decisions and reasoning Reject all source-judgment diagnosis proposals from this session because their evidence came from a scorer artifact rather than a demonstrated behavioral failure. Stop the current cycle instead of spending the remaining timebox on a harness whose novelty, blindness, and scoring guarantees were not yet adequate. Preserve primary, calibration, interrupted, and invalid replacement records append-only, while explicitly excluding invalid harness runs from success and capability counts. Do not infer model improvement from the primary run and do not change Hiro routing, prompts, tools, memory, or production behavior based on these results. Keep external actions, real connectors, evaluator-kernel changes, safety changes, authentication, deployment, and model-weight work outside this session. Validation and evidence Focused session-runner and isolation tests: passed — Four focused tests passed for sealed batch generation, immutability, and isolated evaluation behavior; later focused reruns passed after the prospective scorer correction. Calibration execution: passed-with-harness-finding — Forty-eight observations completed without infrastructure or invariant failure. The apparent scoring misses were punctuation-only oracle mismatches. Primary development-cycle execution: completed-but-not-capability-valid — Twelve cycles and 576 observations completed without transport or invariant failure, but the run was too short, repetitive, answer-leaking, and non-adaptive to support an improvement claim. Replacement execution: invalid-harness-run — Stopped after an over-escaped oracle pattern rejected correct outputs. Its append-only records are excluded from behavioral conclusions. Journal tests and site build: passed — The required npm run test:hiro check passed, and the production npm run build generated and validated all journal pages before publication. Current state The local model endpoint and isolated evaluator were available throughout the valid primary run; no infrastructure failure explains the result. The current development-cycle runner is useful as an initial session-recording scaffold but is not yet a valid adaptive self-improvement harness. No low- or medium-risk runtime proposal was retained because no proposal had trustworthy causal evidence or blind before-and-after validation. The prospective assertion-format repair has been directly checked, but a new trustworthy session has not yet been started. Next steps Add oracle preflight tests that prove known-correct and near-miss answers score as intended before admitting any session batch. Build global novelty checks that compare reasoning graphs across development, blind, canary, and prior sessions without counting identifiers or suite metadata as novelty. Keep expected answers out of prompts, generate genuinely distinct multi-step scenarios, and reject cross-partition duplicates before evaluation. Implement a persistent mastery map and performance-adaptive difficulty selection targeting a meaningful frontier rather than a fixed formula. Before another long run, add hermetic mock-state scenarios that exercise Hiro's actual planner, router, memory, and tool-selection boundaries without permitting network access or real writes. Only after those harness repairs should a candidate variant receive blind comparison, retain-or-revert review, and a new multi-hour development-cycle run.

A harder overnight Hiro development cycle is designed

2026-07-21T19:56:40-07:00

Executive summary The repeated perfect scores on Hiro's current active suite rotation were diagnosed as evaluation saturation rather than proof that the broader assistant goals are solved. A reusable six-to-eight-hour Hiro development cycle was designed around fresh structurally generated cases, adaptive difficulty, blind validation, mastery retirement, and strict retain-or-revert gates. The plan targets 600 to 1,200 valid observations across eight capability families while limiting simple repetitions and keeping a meaningful moving frontier. The cycle authorizes only bounded low- and medium-risk candidate work under operator review; high-risk proposals and every consequential external action remain deferred for explicit approval. Work completed Saturation diagnosis (Completed) Inspected the current adaptive rotation, Daylab orchestration, evaluation schema and scorer, self-improvement limits, safety policy, prior transition design, and the latest supervised-run evidence. Confirmed that the current lane rotates a small fixed suite pool and ordinarily caps a cycle at eight cases and sixteen observations. Concluded that repeated perfect results now primarily measure retention on known examples; the next useful budget should emphasize novel structures and harder capability combinations. Adaptive frontier curriculum (Designed) Defined five difficulty bands from stable canaries through cross-domain frontier tasks, with advancement based on raw score and Wilson lower confidence bounds rather than a single perfect run. Defined eight task families covering compositional reasoning, constrained planning, simulated personal-assistant workflows, source judgment, recovery, long-context attention, calibration, and productive autonomy. Set a target frontier score of roughly sixty-five to eighty-five percent on fresh hard cases while retaining near-perfect essential canaries. Novelty and blind evidence (Designed) Specified seeded structural generators, duplicate and near-duplicate rejection, deterministic oracles, immutable session case files, and provenance metadata for each admitted case. Split every macro-cycle into development, blind validation, and canary partitions, with batches pre-generated ahead of candidate work to reduce leakage and benchmark overfitting. Documented that blindness is procedural in the current single-host setup and that any accidentally exposed reserve case must be relabeled and replaced. Safe overnight operator protocol (Designed) Defined eight to twelve diagnose, propose, review, implement, validate, and advance macro-cycles across a six-to-eight-hour timebox. Added measurable candidate retention gates, regression limits, invalid-run classification, infrastructure failure handling, stop conditions, checkpointing, and idempotent shutdown requirements. Provided a copy-ready handoff prompt for a lighter overnight operator and required a complete final evidence packet. Decisions and reasoning Use Hiro development cycle as the durable name for the recurring supervised overnight process. Prefer broader test diversity and adaptive frontier movement over repeating mastered cases. Keep all evaluation traffic isolated from live tools, user data, networks, calendar, email, and production services; tonight's assistant workflows use only fictional embedded state. Do not modify the evaluation kernel, ledger, safety policy, authentication, services, schedules, or external integrations during the overnight loop. Require blind target improvement plus stable canaries before retaining a behavioral candidate, and preserve rejected results append-only. Treat the cycle as system improvement, not underlying model-weight training; any fine-tuning effort remains a separate reviewed project. Validation and evidence Runbook consistency review: passed — The runbook contains the canonical loop, operating boundaries, observation and novelty targets, difficulty policy, task families, macro-cycle gates, timeboxed schedule, stop conditions, evidence packet, and operator handoff. Repository implementation: not-run-by-design — This session designed the overnight plan and did not start an evaluation cycle or implement a candidate. Journal tests and site build: passed — The required npm run test:hiro check passed, and the production npm run build generated and validated all journal pages before publication. Current state The existing active suites are useful as stable canaries but are no longer sufficient as the main development frontier after repeated perfect runs. The durable overnight runbook is written in Hiro's repository and is ready to hand to a lighter operator after publication validation. No new evaluation run, service change, schedule change, candidate implementation, or external action occurred during this design session. Next steps Launch the named Hiro development cycle with the runbook's handoff prompt and a configured six-to-eight-hour hard stop. Have the overnight operator implement and test the minimal session runner if it is absent, stopping instead of reverting to mastered-suite repetition if that cannot be done safely within forty-five minutes. Review the morning evidence packet, especially blind frontier gains, retained versus rejected changes, invalid runs, and the deferred high-risk queue. Use repeated overnight evidence to decide when a separately approved hermetic tool-state harness or weight-training project is warranted.

Supervised rapid-improvement loop broadens Hiro evaluation coverage

2026-07-21T19:16:42-07:00

Executive summary A supervised evaluation, proposal review, bounded implementation, and re-evaluation loop ran across calibration, composed reasoning, personal-assistant planning, grounding, and operational regression coverage. The completed session executed 996 append-only synthetic evaluation observations with 962 passes, no invariant failures, and no external actions from evaluation prompts. Retained low- and medium-risk changes raised the newer emergence suite, corrected advanced personal-assistant suite, composition diagnostics, revised epistemics suite, and two consecutive standard assistant stability runs to 100%. The final requested stability set completed 14 of 14 cycles and passed all 220 observations; the Daylab worker was confirmed stopped afterward. No high-risk proposal was generated or implemented. Work completed Calibration and structured reasoning (Completed and retained) Expanded the existing two-pass local reasoning route to recognize explicitly underdetermined tasks, reject unstated rules, and verify uniqueness before answering. Extended the same bounded route to explicit first-then sequences and structured deduplication, preserving the most complete supplied canonical metadata. Strengthened the verifier to extract and enforce every literal field or value required by a structured output request. Proposal and regression evidence quality (Completed and retained) Changed duplicate factual-grounding recommendations into a low-risk trace-classification diagnostic when the proposed factual pre-classifier is already installed. Replaced non-ASCII regression progress markers that failed in the Windows console and removed uncorrelated recent-envelope merging that could attach another request's tool metadata to a probe. Tightened factual-query classification so conversational greetings containing temporal words do not trigger unnecessary web search. Adaptive difficulty and assistant workflows (Completed and retained) Added an advanced synthetic personal-assistant suite covering time-zone ranking, calendar ambiguity, task dependencies and capacity, reading-list deduplication and prioritization, conflicting-source synthesis, and proactive approval boundaries. Preserved the first advanced suite results and issued a new version when a broad forbidden substring incorrectly classified a safe response as an action claim. Issued a revised epistemics suite version to recognize semantically valid statements that supplied data cannot determine an exact answer. Distractor-resistant classification (Completed and retained) Direct pass inspection proved that an older active classification case admitted two equally simple rules with different target answers, so its evidence was preserved but removed from active rotation rather than driving a runtime change. Added a disambiguated emergence-suite version and a dedicated eight-case distractor-relations suite covering numeric, text-numeric, semantic, parity, comparison, range, and boundary relations. Added a bounded classification sub-route with a larger local analysis and verifier budget only for recognized label-classification tasks; both corrected target suites then reached 100% and reproduced that result in later breadth runs. Adaptive scheduler correction (Completed and retained) Found that the adaptive rotation still selected the standard assistant suite twice despite configuring an advanced suite. Corrected the second assistant slot to use the advanced suite and verified the seven-suite rotation included both standard and advanced assistant coverage. Live operational activation (Completed) Restarted the live API through the checked-in launcher after validated changes so operational probes exercised the retained code rather than a stale process. The final live regression set completed 22 of 22 probes successfully across deterministic, non-grounding, edge-case, resolver, factual-grounding, and response-gate categories. Decisions and reasoning Retain the calibration route because pass rate improved from 81.25% before the session to 93.75% on the same suite and then 100% on its newer version. Do not reapply the already-installed factual classifier; require blocked responses to be categorized before proposing another routing change. Retain structured deduplication and literal-field verification after the corrected advanced assistant suite reached 100% and the standard assistant suite reproduced 100% twice. Treat semantically correct uncertainty wording and a safe use of the word completed as evaluation-spec defects, preserving their original results and correcting only new suite versions. Reject the classification-prompt candidate derived from an ambiguous benchmark; replace the active case with disambiguated versioned evidence before evaluating a new bounded candidate. Continue proposal-only evaluation isolation and approval boundaries; no evaluation result grants authority for messages, calendar writes, or other external actions. Validation and evidence Session evaluation observations: passed-with-capability-findings — Nine hundred ninety-six observations completed with 962 passes, zero invariant failures, and append-only run records. Failures include preserved results from superseded or corrected benchmark versions. Seven-suite adaptive burst: passed-with-capability-findings — All seven cycles completed: 101 of 110 observations passed. The burst isolated repeatable reading-list and explicit-composition gaps and one evaluation-scoring gap. Targeted causal reruns: passed — Composition diagnostics and revised epistemics each reached 16 of 16; the corrected advanced assistant suite reached 16 of 16; the standard assistant suite then reached 16 of 16 twice consecutively. Focused automated tests: passed — Multiple focused test batches passed, including reasoning routing, isolated evaluation, adaptive suite planning, factual grounding, proposal quality, and operational boundaries. Live regression probes: passed — After the live API loaded the retained code, all 22 operational probes passed with no timeout or infrastructure error. Final corrected breadth and stability sets: passed — The corrected seven-suite breadth set passed 110 of 110 observations. The final two-pass stability set completed 14 of 14 cycles and passed 220 of 220 observations, with zero invariant or infrastructure failures. Current state The live API is healthy and has loaded the retained factual, calibration, composition, deduplication, and structured-field changes. Every suite in the corrected active seven-suite rotation passed at 100% in the final two-pass stability set, while older immutable versions retain their original lower scores for auditability. The proposal generator now recognizes when a suggested factual classifier already exists and asks for fresh trace categorization instead of proposing duplicate code. No high-risk proposal is pending from this session, and the periodic Daylab worker is stopped. Next steps Increase test diversity with harder mixed personal-assistant scenarios and adversarial approval-boundary cases before adding repetitions to mastered suites. Require repeated success on the harder suite versions and continue monitoring final live regression coverage after each retained runtime change. Classify blocked-response traces into public factual, account-backed, and intentional safety outcomes before considering any further grounding-route expansion. Keep all external actions behind explicit approval and log any future high-risk proposal without implementation.

Approved grounding and deliberative reasoning changes validated

2026-07-21T18:41:37-07:00

Executive summary The user approved two bounded Hiro improvements: proactive grounding for fresh factual questions and a candidate response to repeated ordered-transformation failures. A first prompt-only ordered-reasoning candidate failed its benchmark: the target suite remained at 57.1% and broader metrics regressed, so that candidate was rolled back rather than promoted. A replacement generalized two-pass local reasoning route passed the ordered-binding suite twice at 14 of 14 observations and improved the final seven-cycle adaptive set to 96 of 110 observations, or 87.3%. The factual-grounding route passed focused tests and a live public-data probe: web search executed before ordinary model inference, a sourced answer was returned, and three evidence sources were recorded. Work completed Fresh factual grounding (Completed and retained) Added a bounded classifier for current or externally checkable public facts such as current office holders, market facts, scores, population, and recent information. Classified queries execute web search before ordinary model inference and summarize only the returned evidence. Explicitly synthetic prompts, tool-free scenarios, creative requests, and personal calendar or email queries are excluded so they do not leak into the public web route. Ordered reasoning candidate selection (Completed with one rejected candidate) Tested a generalized prompt-only instruction first. It did not improve ordered binding and weakened composition and baseline metrics, so the change was rolled back immediately. Replaced it with a generalized two-pass local route for explicit composed transformations: one local pass derives and verifies operations, and a second local pass independently checks ordering and emits only the requested format. The retained route is shared by ordinary Hiro reasoning and the isolated evaluator, uses no tools or remote model fallback, and contains no benchmark answers. Adaptive evidence set (Completed) The final retained-state set completed seven cycles and 110 append-only observations with 96 passes, zero invariant failures, zero evaluation errors, and no tool, resolver, or source observations. Ordered binding improved from 8 of 14 to 14 of 14; composition diagnostics improved from 10 of 16 to 14 of 16; both emergence suites improved from 10 of 16 to 13 of 16. RSI remained 14 of 16, and the two synthetic personal-assistant runs each completed 14 of 16. Decisions and reasoning Retain the proactive factual-grounding route because focused tests and a live public-data probe demonstrated the intended evidence path. Reject and roll back the prompt-only ordered-reasoning candidate because target performance was flat and broader evidence regressed. Retain the two-pass local reasoning candidate because it reproduced a 100% ordered-binding result twice and improved the broader adaptive set without weakening the measured suite totals. Keep Daylab proposal-only and isolated from live integrations; no benchmark result authorizes automatic code changes, external writes, invitations, messages, or promotions. Validation and evidence Focused code and boundary tests: passed — Forty-seven tests passed across factual routing, ordered reasoning, isolated evaluation, operational boundaries, Daylab, adaptive planning, evaluation-ledger behavior, and local-router budgets. Live factual grounding probe: passed-with-nonfunctional-warning — A public, non-personal current-fact query forced web search, returned a sourced nonempty answer, recorded the factual-web resolver and three sources. A pre-existing locked log-rotation warning did not affect the response or evidence trace. Targeted ordered-binding validation: passed — Fourteen of fourteen observations passed, compared with eight of fourteen immediately before the retained candidate. A second full-set execution reproduced fourteen of fourteen. Final adaptive seven-cycle set: passed-with-capability-findings — Seven cycles completed through append-only resume after the command wrapper reached its execution limit. Final aggregate: 96 of 110 observations passed, with zero evaluation errors, tools, resolvers, sources, or invariant failures. Current state The factual-grounding and two-pass local ordered-reasoning routes are active; the failed prompt-only candidate is not present. The persistent periodic Daylab worker is running again in proposal-only mode. The largest remaining clean gaps are reading-list tag adherence, epistemic wording, and several rule-induction or calibration cases. Next steps Repeat the retained-state adaptive set on later cycles to measure whether the 87.3% aggregate and 100% ordered-binding result remain stable. Treat the reading-list, epistemic, and remaining rule-induction misses as separate candidate hypotheses rather than expanding the retained reasoning route broadly. Add targeted coverage for factual-route tool failure and source-summary formatting before widening its classifier vocabulary. Do not grant additional autonomy or external write authority from this evidence alone.

Daylab evaluation isolation restored after benchmark routing finding

2026-07-21T18:10:19-07:00

Executive summary A completed seven-cycle Daylab evidence set revealed that synthetic personal-assistant prompts were entering the normal interactive resolver path, making those benchmark scores unsuitable for capability assessment. Daylab was paused immediately. The evaluation harness now uses a dedicated local-only inference path with no tools, resolver access, user history, session persistence, or remote-model fallback. A clean replacement evaluation completed 16 observations at an 87.5% pass rate with zero tool, source, resolver, or execution-error observations. Its only measured gap was the reading-list tag formatting case. No external write action was enabled or performed. The contaminated observations are retained in the append-only ledger for audit but excluded from readiness conclusions. Work completed Synthetic evaluation isolation (Completed) Added a dedicated evaluation callable that sends only the synthetic system instruction and case prompt to the configured local model. The callable deliberately bypasses the conversational agent, tool registry, live resolvers, user memory, session history, and remote fallback routing. Both direct Daylab evidence collection and the proposal-only self-improvement evaluation phase now use this isolated callable. Contaminated-result handling (Completed) Classified the affected personal-assistant observations as invalid for capability comparison because their execution path was not self-contained. Preserved the existing append-only records rather than rewriting or deleting them, and excluded them from successful-run and readiness counts. Ran a distinct, labeled replacement evaluation after the fix to establish a clean baseline. Clean validation (Completed) The replacement synthetic personal-assistant run executed 16 observations with an 87.5% pass rate and no execution errors. The recorded observations had zero tools, sources, and resolvers, confirming the intended isolation boundary. The deterministic scorer identified two repeated misses in the reading-list tag-formatting scenario; the system produced a low-risk diagnosis proposal only, with no candidate code change. Decisions and reasoning Treat evaluation isolation as a prerequisite for interpreting assistant-workflow metrics, not as an optional benchmark convenience. Do not use contaminated observations to claim progress, regressions, readiness, or successful autonomous behavior. Continue to separate evidence collection and diagnosis from implementation: no evaluation proposal may alter Hiro without explicit user approval and later held-out validation. Keep all target-workflow scenarios synthetic and approval-gated until the relevant capability has stable clean evidence. Validation and evidence Focused isolation, Daylab, adaptive-planning, ledger, and dashboard tests: passed — Fifteen tests passed using the project runtime. An initial test attempt encountered a stale Windows temporary-directory permission condition; the same suite passed in an approved project-local temporary directory. Clean personal-assistant evaluation: passed-with-capability-finding — Sixteen observations completed with an 87.5% pass rate, zero tool/source/resolver observations, and zero execution errors. The only repeated deterministic failure was a missing reading-list tag. Safety review of the affected benchmark: passed — The faulty route was stopped before further periodic execution. No external write action was enabled or performed during the affected or replacement evaluation. Current state The personal-assistant benchmark has a clean, tool-free local evaluation path and a fresh replacement baseline. The earlier affected observations remain visible as audit history but are not included in clean capability or readiness comparisons. The periodic Daylab worker was resumed in this session. Its first isolated full proposal-only cycle completed in 1.4 minutes: 14 ordered-binding observations at a 57.1% pass rate, 22 regression probes, and zero tool, source, resolver, or execution-error observations. Daylab may collect synthetic evidence and generate bounded diagnoses but cannot change code, configuration, integrations, data, schedules, or external services. Next steps Monitor the resumed periodic Daylab worker for the same zero-tool, zero-resolver boundary in each completed synthetic evaluation. Repeat the clean personal-assistant suite across several distinct labeled runs before treating its current pass rate as stable. Consider only the low-risk latent-binding diagnosis from the first resumed full cycle; do not approve the separate medium-risk factual-routing idea until it has fresh, targeted evaluation evidence. If the reading-list formatting gap persists, present its bounded prompt or response-shaping diagnosis for approval; do not implement it automatically. Continue to favor broader synthetic workflow diversity and adaptive gap selection over repeated narrow baseline runs.

Adaptive personal-assistant benchmark now guides Daylab

2026-07-21T16:45:00-07:00

Executive summary Daylab now selects public evaluation suites adaptively: a stable regression canary, synthetic personal-assistant workflows, and suites ranked by observed failure rate. The new personal-assistant benchmark covers dry-run calendar planning, calendar safety, task triage, reading-list drafting, source-aware briefings, and approval boundaries. All scenarios are synthetic and no scenario authorizes calendar writes, invitations, user-data changes, email sending, or other consequential external actions. The first adaptive Daylab cycle selected a previously measured composition gap, completed 12 of 16 public observations, and remained proposal-only. Work completed Adaptive evaluation policy (Completed) Added a public-suite planner that ranks known gap suites by completed-ledger failure rate. Balances a stable RSI canary, the new target-workflow suite, and every known gap suite in a deterministic rotation. Uses existing append-only Daylab history only to choose the next rotation slot; it does not modify historical records. Personal-assistant dry-run benchmark (Completed) Added eight synthetic public cases with deterministic assertions and two observations each. Covers calendar draft and conflict handling, time-zone clarification, task triage, reading-list drafting, briefing source boundaries and synthesis, and approval-gated action handling. Keeps all cases dry-run and synthetic so the benchmark does not access or alter a user's calendar, tasks, reading list, contacts, or external communications. Daylab activation (Completed and running) Resumed the serial proposal-only Daylab worker after focused validation. Its first adaptive selection evaluated a known composition gap: 12 of 16 public observations passed, with no execution or invariant failure. The next planned rotations include the personal-assistant dry-run suite after the stable baseline canary. Decisions and reasoning Prioritize target-workflow evaluation before granting calendar, task, reading-list, or briefing integrations broader authority. Preserve a stable baseline in every rotation so a new functional benchmark cannot hide a regression in existing behavior. Direct additional evidence toward measured capability gaps instead of repeatedly running only the narrow RSI suite. Continue to require approval and held-out validation before any candidate code change, integration expansion, data write, external message, invitation, or promotion. Validation and evidence Adaptive planner and dry-run benchmark tests: passed — Thirteen focused benchmark, Daylab, evaluation-ledger, and dashboard tests passed. Python compilation: passed — The adaptive planner, Daylab worker, and self-improvement runner compiled successfully. First live adaptive Daylab selection: passed-with-capability-finding — The worker selected a measured composition gap, completed 12 of 16 public observations, and ran 22 probes with 19 passes and three stable findings. It created one low-risk diagnostic proposal and no code candidate. Current state Daylab is running serially with the adaptive public-suite policy and proposal-only safeguards intact. The next rotation includes a stable baseline canary followed by the synthetic personal-assistant dry-run suite. The benchmark measures planning and approval behavior only; it does not activate calendar, task, reading-list, email, or internet-update actions. Next steps Observe the first several personal-assistant dry-run evaluations and separate model capability findings from infrastructure failures. Create a repository-external held-out companion suite before considering a candidate implementation for any target workflow. Approve only a bounded diagnosis of the repeatable composition findings before considering any code candidate; do not implement a model, routing, or prompt change from this one run alone. Do not enable real integrations or write authority until the relevant workflow has stable evaluation evidence and explicit user approval.

Diversified 30-cycle Daylab evidence burst launched

2026-07-21T16:27:26-07:00

Executive summary Hiro Daylab was upgraded from a fixed 30-minute cadence into an on-demand, bounded evidence-burst capability. The completed burst ran 30 serial public evaluations in approximately six minutes, rotating across five existing public capability suites and preserving a distinct append-only record for each cycle. The first implementation path was corrected immediately because it repeated the full diagnostic and probe phases for every cycle; the active burst now collects evaluation evidence only and defers interpretation to one consolidated review. The burst completed all 30 cycles with no execution failures or invariant violations; its 468 observations passed at 75.0% overall, revealing stable capability gaps rather than infrastructure instability. Work completed Rapid evidence collection (Completed) Added a serial burst mode that requests a finite number of Daylab evaluations rather than waiting for the normal cadence. Rotates among public identity, instruction-following, composition, and ordered-binding-oriented suites to broaden the evidence beyond one fixed test set. Persisted a consolidated evidence packet after all 30 requested cycles completed: 468 observations, 351 passes, and 117 deterministic public-suite failures. Efficiency and safeguards (Completed) Stopped the periodic worker before launch so the burst cannot overlap another Daylab cycle. Removed duplicated per-cycle diagnostic and regression-probe work from burst execution after observing that it would slow the requested campaign without adding proportionate new evidence. Retained proposal-only behavior; evidence will be reviewed before any follow-up change is considered. Product direction (Recorded) Documented the long-term objective of a reliable all-purpose assistant with increasingly autonomous problem solving and useful emergent behaviors. Recorded future calendar, task-list, reading-list, and source-backed update capabilities, with explicit approval required for consequential writes or external actions. Decisions and reasoning Use a finite, serial 30-cycle burst to accelerate measurement without concurrent model pressure or overlapping ledger writes. Separate rapid evaluation collection from interpretation so the resulting recommendation can be based on the complete evidence packet rather than repeated partial analyses. Use only existing public development suites in this burst; held-out validation remains reserved for a later candidate review. Do not treat the 100% result on the narrow RSI suite as evidence of general autonomy; the diversified suites show repeatable composition and ordered-binding gaps that need targeted evaluation before a code candidate is justified. Keep all modifications, promotions, calendar writes, invitations, and external communication approval-gated. Validation and evidence Focused Daylab and evaluation tests: passed — Seven focused tests passed, including suite rotation and evidence-packet persistence coverage. Python compilation: passed — The modified Daylab and self-improvement modules compiled successfully. Initial live burst cycle: passed — The first optimized evaluation-only cycle completed successfully and appended a new public record. Current state The 30-cycle, proposal-only Daylab evidence burst completed serially with no execution failures. The normal periodic Daylab worker remains paused after the burst, preventing an unreviewed return to the former cadence. The periodic Daylab worker remains paused pending review of the burst recommendation; no change was applied automatically. Next steps Use the burst as a reliable baseline for future candidate comparisons, while keeping it separate from calendar-night readiness counts. Add a bounded target-capability benchmark for calendar planning, task and reading-list maintenance, and source-backed briefing workflows before authorizing new assistant integrations. If approved, implement adaptive test selection that retains a repeatable baseline while directing new evaluation coverage toward the observed composition and ordered-binding gaps. Resume or revise the ordinary Daylab cadence only after reviewing the proposed adaptive benchmark plan.

Protected remote Observatory no longer requires a Hiro browser key

2026-07-21T15:20:18-07:00

Executive summary The protected remote Evaluation Observatory was diagnosed and corrected so an authorized viewer needs only their Cloudflare identity, not a separately carried Hiro API key. The browser-side API-key prompt was obsolete for the read-only dashboard and was removed. The Observatory listener now preserves the tunnel's immediate loopback peer for its narrow reverse-proxy authorization path; non-loopback and tailnet behavior remains governed by the existing configuration. Local API and dashboard checks passed, and the protected remote page loaded successfully with no API-key control. Work completed Remote access diagnosis (Completed) Identified a client-side retry flow that prompted viewers for a Hiro API key whenever a dashboard data request was unauthorized. Confirmed the dashboard is intentionally protected by Cloudflare Access and therefore should rely on that identity gate for remote viewing. Confirmed the tunnel forwards the viewer address, which initially caused the loopback-only listener to misidentify the immediate proxy peer. Narrow authorization correction (Completed) Removed the browser API-key button, session storage behavior, and interactive prompt from the static Observatory page. Disabled forwarded-address interpretation only on the loopback-only Observatory listener, allowing its local reverse-proxy path without a browser API key. Retained the existing conditional trust controls for non-loopback clients. Decisions and reasoning Use Cloudflare Access as the remote viewer authentication boundary rather than exposing or asking viewers to retain a Hiro API credential. Keep the exception local to loopback traffic so the dashboard origin remains read-only and does not create a broad anonymous API path. Do not publish raw ledger files or change Hiro's evaluation, schedule, proposal, or promotion behavior as part of this access correction. Validation and evidence Focused dashboard and access tests: passed — Four focused tests passed, including a loopback access and no-browser-key regression check. Local read-only Observatory checks: passed — The dashboard page and overview API both returned successfully; the returned page contained neither the key prompt nor the former key button. Protected remote browser check: passed — After Cloudflare authentication, the remote page loaded its full ledger and metrics, contained no API-key button, and showed no ledger-load error. Current state Remote viewers can use the protected Observatory from another computer by signing in through Cloudflare Access. The dashboard remains read-only; it does not expose a general-purpose Hiro API key in the browser. The existing append-only evaluation ledger and day/night evaluation process are unchanged by this access fix. Next steps If an already-open browser tab shows an older cached page, reload the protected Observatory link once. Continue observing Daylab and nightly runs as independent evaluation evidence; do not count infrastructure failures as successful nights. Review access policy membership only when an additional authorized viewer is needed.

High-cadence Daylab now appends fresh evaluations without auto-promotion

2026-07-21T15:01:21-07:00

Executive summary This session added Hiro Daylab: a bounded, proposal-only development lane that runs immediately and then every 30 minutes while preserving the canonical nightly lane for readiness evidence. Every Daylab and scheduled nightly self-improvement execution now supplies a unique evaluation label, creating a distinct append-only evaluation record rather than reusing the same-day record. The first live Daylab cycle added 16 new public observations with a 100% pass rate and no evaluation failures. It also completed 22 lightweight regression probes, with 21 passes and one deterministic date finding. Daylab can create evidence, proposals, curiosity items, and regression diagnostics autonomously. Automatic code patches, promotions, merges, configuration changes, and external actions remain disabled or approval-gated. Work completed Distinct evaluation identity (Completed) Extended the public-evaluation runner with explicit lane and execution-label inputs. Preserved backwards-compatible same-day idempotency when no label is supplied, while self-improvement executions now generate a unique label by default. Recorded each labeled execution under its own immutable run and variant identity so the append-only ledger can retain independent observations safely. Kept Daylab records separate from nightly records so rapid daytime evidence cannot be miscounted as a completed night. Daylab worker and recovery (Completed and running) Added a serial Daylab worker with a 30-minute cadence and a 20-minute maximum cycle budget. Added a Windows launcher that normalizes the child-process search path before starting the worker. Installed and tested a per-user startup entry that first checks local readiness and then restores the Daylab worker after sign-in. The worker runs proposal-only cycles and does not overlap cycles. First live Daylab evidence (Completed) The first Daylab cycle completed in approximately 1.3 minutes and appended 16 new public observations. The public evaluation passed all 16 observations and reported no evaluation failures. The regression diagnostic completed all 22 probes: 21 passed and one deterministic date probe failed. The cycle produced two medium-risk, approval-required proposals and four curiosity items; no proposal was applied. Decisions and reasoning Use a separate Daylab prefix and run identity so rapid development evidence is visible without inflating the valid-night readiness count. Run Daylab serially at 30-minute intervals rather than overlapping cycles, protecting the local model and append-only ledger from concurrent load. Require a fresh public evaluation for every Daylab or nightly self-improvement execution. Keep code patches and promotion disabled; Daylab may draft evidence-backed proposals but cannot change Hiro autonomously. Validation and evidence Distinct labeled evaluation behavior: passed — Focused tests verified that two labeled Daylab cycles append distinct evaluation records and new observations. Daylab cadence guardrails: passed — Focused tests verified the worker requests Daylab-labeled evaluations and rejects an unsafe cadence. Focused test suite: passed — Eight focused Daylab, nightly-evaluation, and scheduler tests passed in an isolated temporary directory. Python compilation: passed — The modified evaluation runner, self-improvement runner, and Daylab module compiled successfully. First live Daylab cycle: passed-with-regression-finding — The cycle appended 16 new public observations at 100% pass, then completed 22 probes with 21 passes and one deterministic date finding. Observatory visibility and startup recovery: passed — The existing read-only Observatory API reported the new Daylab record, and the tested per-user startup entry retained the running worker. Current state Daylab is running locally and will begin another bounded cycle every 30 minutes after the prior cycle finishes. Each Daylab or nightly self-improvement execution now adds a distinct public evaluation record. The first Daylab record is separate from the nightly history and does not count as an additional valid completed night. The canonical nightly schedule remains enabled and retains its proposal-only, approval-gated behavior. A non-fatal Windows output-encoding warning appeared in redirected worker logs; the first Daylab cycle still completed and the worker remained healthy. Next steps Observe several Daylab cycles for reliable fresh-evaluation completion and recurring regression patterns. Treat Daylab results as rapid development evidence while continuing to require independent calendar-night runs for the stated nightly-readiness threshold. Investigate the deterministic date regression finding with a bounded test before considering a code change. Review Daylab proposals individually; broaden test diversity and adaptive selection before increasing repetition counts or promotion authority. Consider a larger weekly deep run after the rapid lane demonstrates reliable completion.

Manual proposal-only cycle completed; nightly evaluation reused by design

2026-07-21T14:47:12-07:00

Executive summary A user-requested manual run of Hiro's guarded proposal-only nightly workflow completed successfully after the normal readiness preflight confirmed that the local model and dashboard API were healthy. The workflow correctly reused the completed same-day public evaluation ledger record instead of creating a misleading second nightly evaluation. That existing record contains 16 observations, a 100% pass rate, and no evaluation failures. The manual cycle completed 22 lightweight regression probes: 20 passed and two deterministic date/time probes failed. It created two medium-risk, approval-required improvement proposals and four curiosity items, with no code change or automatic promotion. Because the evaluation was reused, this is not an additional valid completed night for readiness counting. The valid-night count remains unchanged; the next independent nightly evidence must come from a later calendar date. Work completed Readiness and execution (Completed) Ran the guarded preflight before execution; the local model and dashboard API were ready. A first background attempt did not persist through the managed execution environment, so the same user-authorized workflow was relaunched in a persistent local context and completed. The completed proposal-only run finished in approximately 1.3 minutes and reported no unhandled run errors. Nightly public evaluation ledger (Reused; no new observations) The public evaluation runner is intentionally idempotent for the same date, suite version, and source fingerprint. It reused the current valid record rather than appending duplicate evidence: 16 recorded observations, 100% pass rate, and zero failures. Held-out cases were not used, and no second valid-night count was claimed. Regression and improvement output (Completed; proposals pending approval) Completed all 22 lightweight regression probes. Twenty passed; two deterministic date/time probes failed. Generated a medium-risk proposal to improve grounding before factual responses reach the final gate, backed by observed blocked responses. Generated a medium-risk proposal to evaluate a short-lived cache for recently verified factual answers, backed by an observed high-latency factual path. Both proposals require user approval and code changes; neither was applied. Decisions and reasoning Treat the same-day evaluation reuse as expected idempotent behavior, not as a failed or a second successful nightly run. Keep the public evaluation's 16-observation, 100% result separate from the 22-probe regression diagnostic, because they exercise different evidence surfaces. Do not apply either generated proposal without explicit user approval, because both are medium-risk code changes. Do not inflate readiness counts with manual replays or infrastructure/tooling retries. Validation and evidence Model and dashboard readiness preflight: passed — The local model and dashboard API were available before the workflow was launched. Manual proposal-only workflow: passed — The persistent run completed in approximately 1.3 minutes with no reported run errors. Nightly public evaluation: reused-valid-record — The same-day idempotent record was reused: 16 observations, 100% pass rate, and zero failures. No additional observations were executed. Regression probes: completed-with-findings — All 22 probes completed; 20 passed and two deterministic date/time probes failed. Automatic changes: not-applied — The workflow produced approval-required proposals only; no code, configuration, schedule, evaluation case, or model-setting change was applied. Current state The valid completed evaluation for the current date remains the existing 16-observation, 100% passing public run. The manual proposal-only diagnostic cycle completed and its proposals remain pending user approval. Two deterministic date/time regression findings are available for investigation but were not treated as failures of the reused public evaluation. Readiness counting still requires another independent valid calendar-night evaluation before any process-expansion decision. Next steps Observe the next scheduled nightly evaluation on a later calendar date and add it to readiness history only if it completes with valid evidence. Investigate the two deterministic date/time regression findings with bounded tests before considering a code change. Review the approval-required grounding and caching proposals separately; prioritize test diversity and adaptive testing before increasing repetitions. Continue excluding infrastructure failures, manual replays, and reused records from the successful-night count.

Private browser-only benchmark dashboard is live behind owner access

2026-07-21T09:56:33-07:00

Executive summary This session completed a private, browser-only route to Hiro's live benchmark dashboard without requiring a VPN client or remote desktop on the work computer. The selected design keeps Hiro's service local and places a dedicated identity gate in front of it, rather than exposing the evaluation ledger or a public dashboard. A private hostname now routes through an authenticated outbound connector to the local Evaluation Observatory. An unauthenticated request is redirected to sign-in, while an approved owner session successfully loads the dashboard. The connector is configured to start automatically when the local user signs in. No benchmark inputs, results, ledger entries, model settings, or credentials were published. Work completed Private remote-access design (Completed and verified) Completed the identity-access activation and configured a self-hosted application for the dedicated private benchmark hostname. Applied an allow-only policy for the dashboard owner and retained the dashboard behind the identity gate. Configured the outbound connector to reach the dashboard only through its local loopback service, keeping the origin off the public internet. Verified the authorized-browser path reaches the live Evaluation Observatory after sign-in. Connector reliability and diagnosis (Completed and verified) Diagnosed an initial authenticated 502 as a local-origin port mismatch in the connector configuration, then corrected the local-only target. Validated the connector routing configuration and performed a real child-process startup test using a launcher that normalizes the Windows process search path. Installed a per-user startup entry so the connector is relaunched after the local user signs in, without requiring administrator privileges. Domain and data-preservation checks (Completed) Verified continuity of active website, application, and verification DNS records before the authoritative-nameserver transition was submitted and accepted. Confirmed DNSSEC was disabled before the transition. Kept the live benchmark service separate from the public static site and did not create a public raw-ledger export. Decisions and reasoning Use a private browser login in front of the live dashboard so the work computer needs only a browser and the evaluation ledger is not exposed publicly. Do not publish the full benchmark dashboard to the existing public site because its live API-backed data can include operational detail that should remain private. Use a dedicated private hostname instead of the public static-site path so the dynamic dashboard remains separately protected. Use a per-user startup entry after the scheduled-task API rejected task creation under the available permissions; this preserves automatic connector recovery without requesting administrator access. Keep the origin local and route access through the authenticated connector rather than opening an inbound public service. Validation and evidence Connector routing configuration: passed — The configured ingress rules validated successfully before the connector was started. Connector child-process startup: passed — The normalized launcher started the connector successfully and it remained running. Private identity gate: passed — An unauthenticated request was directed to the identity-login flow rather than dashboard content. Authorized live dashboard access: passed — An approved owner browser session loaded the Evaluation Observatory through the private hostname after the origin correction. Automatic startup entry: passed — The per-user startup entry was installed and invoked successfully; the launcher confirmed the managed connector was running. Public data exposure: passed — No evaluation ledger, benchmark response, or API content was intentionally made publicly available. Current state The private dashboard route is live and the local connector is running. The private hostname requires owner authentication before dashboard content is served. The connector has a per-user startup entry for recovery after local sign-in. The live dashboard remains separate from the public static site; no raw-ledger publication was added. No changes were made to Hiro's evaluation cases, benchmark results, ledger contents, or model settings during this access setup. Next steps Use the private dashboard URL from the work computer and sign in through the configured identity provider. Observe the next scheduled nightly run through the protected dashboard and continue excluding infrastructure failures from readiness counts. Consider adding another explicitly approved reader only through a separate allow-only identity policy if sharing becomes necessary. Keep the live API behind the private identity gate and do not create a public raw-ledger export without a separate redaction review.

Nightly evaluation connection preflight repaired and verified

2026-07-21T07:56:36-07:00

Executive summary The nightly evaluation connection failure was traced to a dependency gap: the preflight kept Hiro's API available but did not ensure the local Gemma inference server was available on port 8080, which the evaluator calls directly. The nightly preflight now invokes the existing idempotent Gemma launcher, which validates model identity and warms the model, before it accepts an already-running API as sufficient readiness. A real preflight confirmed the API and Gemma model endpoint. Today's fresh proposal-only evaluation then completed all 16 public RSI observations at 100 percent pass rate with zero invariant failures and no observation errors. The connection defect is resolved for the tested path. Five of 22 lightweight regression probes still failed on separate functional expectations; those are reported as follow-up quality work, not connection failures. Work completed Connection-path diagnosis and repair (Completed) Confirmed that the prior failed evaluation had an available API process but no listening model endpoint before the repair investigation began. Added the existing start_gemma_server.ps1 launcher to nightly_api_preflight.ps1 after the required Path/PATH normalization and before the API readiness short-circuit. The preflight now validates that its model launcher exists, exposes it in validate-only output, and fails rather than permitting an evaluation when Gemma cannot become ready. The model launcher is idempotent and verifies model identity plus a warm-up response, so the preflight does not create a duplicate model service when Gemma is already healthy. Focused and live validation (Completed) Focused tests for nightly preflight, nightly evaluation, and the Gemma launcher passed: 8 passed. A real preflight confirmed the configured Gemma endpoint exposed the expected model and the Evaluation Observatory API returned HTTP 200. The initial manual evaluator invocation did not execute because the interactive console lacked UTF-8 output configuration. Retrying with the scheduled API's UTF-8 setting completed successfully; this is an invocation-environment issue, not an evaluation failure. The successful manual evaluation did emit non-fatal Windows log-rotation warnings because another process held the shared main log open. The run completed, wrote its append-only results, and exited with code 0. Today?s fresh nightly evaluation (Completed) Run nightly-2026-07-21-rsi-cycle-public-1-0-0-db468ad4f84c started at 2026-07-21T14:55:04Z and completed at 2026-07-21T14:55:12Z in new mode. All 16 public RSI observations passed. The ledger reports zero invariant failures, 100 percent pass rate, weighted score 1.0, p95 observation latency about 766 milliseconds, and no observation errors. The proposal-only cycle completed in 1.2 minutes, reviewed 100 response envelopes, and created approval-required proposals from organic-envelope evidence rather than from the now-passing synthetic evaluation. Decisions and reasoning Treat model availability as a required precondition for a nightly evaluation, not as an implication of API availability. Reuse the checked-in Gemma launcher instead of adding a second startup mechanism so model identity, warm-up, and idempotency remain centralized. Count the July 21 run as a valid completed night because the model was reachable, all observations completed, and the append-only ledger has a complete error-free result set. Do not infer that all quality signals are healthy: five lightweight regression probes remain failing and require separate, evidence-based triage before any broader process adjustment. Validation and evidence Focused Hiro test set: passed — tests/test_nightly_api_preflight.py, tests/test_nightly_evaluation.py, and tests/test_gemma_launcher.py: 8 passed. Real model and API preflight: passed — The preflight found the expected Gemma model on port 8080 and the Evaluation Observatory API returned HTTP 200. Fresh nightly public evaluation: passed — 16 of 16 observations passed with zero invariant failures and no connection or model errors. Lightweight regression probes: passed-with-functional-failures — 22 probes completed; 17 passed and 5 failed. The failures are separate functional expectations, not connection failures. Current state The nightly preflight now verifies the model dependency before declaring readiness. Today's valid completed nightly evaluation restores the comparable successful-night count to two when combined with the prior valid RSI night; the July 20 infrastructure failure remains excluded. The process remains short of the minimum three valid nights and still needs recurring-infrastructure evidence plus retained regression-probe outcomes before expansion is recommended. No automatic promotion or behavioral code change was applied from the generated proposals. Next steps Observe at least one more valid scheduled night for the minimum readiness threshold, preferably three more for the preferred threshold. Preserve model/API preflight evidence with each nightly record and retain regression-probe pass/fail outcomes, not only a probe count. Triage the five remaining lightweight regression-probe failures independently of the resolved connection issue. Only after stable successful nights and probe evidence are available, seek approval for bounded expansion favoring broader diversity and adaptive testing before additional nightly repetitions.

Nightly self-improvement readiness review finds infrastructure failure

2026-07-21T07:34:12-07:00

Executive summary The July 20 scheduled proposal-only run completed its bookkeeping and all 16 public RSI observations, but it is classified as an infrastructure/model failure rather than a valid capability evaluation: every observation returned an empty response with the same connection error. The append-only Evaluation Observatory recorded a 0 percent pass rate, 32 invariant failures, and a 36.8-second wall-clock evaluation interval. The model identity is absent from the nightly manifest, and no successful model/API availability signal was recorded for this run. The generated architecture-note proposal is appropriately bounded only insofar as it calls for diagnosis. Its stated reasoning-category interpretation is not evidence of a reasoning regression because the common connection failure contaminated every category. The process is not ready to expand. The successful-night count remains one comparable valid nightly evaluation, with the latest failed night excluded; at least two more reliable valid nights are needed for the minimum threshold, and four more are preferred. Work completed Latest scheduled-run evidence review (Completed) Reviewed the July 20 run summary, Evaluation Observatory SQLite ledger, append-only case results, variant manifest, proposal output, and available operational logs without changing Hiro code, configuration, schedules, cases, or results. Run nightly-2026-07-20-rsi-cycle-public-1-0-0-db468ad4f84c began at 2026-07-21T05:00:21Z and completed at 2026-07-21T05:00:58Z. The self-improvement summary reports proposal_only mode, new evaluation mode, 3.1 minutes total runtime, 16 model calls, 16 observations, and 22 regression probes run. All 16 observations failed. Categories were reasoning (6), instruction following (4), identity (2), communication (2), and epistemics (2); every result recorded ConnectError: All connection attempts failed and an empty response. The ledger reports 0 percent pass rate, weighted score 0.3940, 32 invariant failures, and p95 observation latency of about 2.43 seconds. Pattern and content failures are downstream artifacts of empty responses, not independent behavioral findings. Readiness comparison (Completed) The prior comparable RSI public run, 370eff82-baee-4fb5-a289-e66f39f588de on July 16 Pacific, completed 16 observations with 100 percent pass rate and zero invariant failures. The latest failure is excluded from the successful-run count because the common transport failure prevented model evaluation. Nearby non-nightly public experiments also show both healthy and unhealthy availability states, reinforcing that availability must be gated before metrics are used for process expansion. Regression probes were reported as 22 run, but the available summary does not retain pass/fail detail. They therefore do not establish a clean regression-probe result for readiness. Proposal scope review (Completed) The nightly evaluation proposal preserves approval requirements, requests diagnosis before any candidate change, names the source run and failing cases, and proposes no runtime modification. That containment is appropriate. The proposal's framing around a reasoning competence gap should not be acted on as written because the exact same connection failure occurred in all five categories. Any follow-up must begin with model/API readiness and routing diagnosis rather than a reasoning-specific code change. Decisions and reasoning Classify the latest night as infrastructure/model failure, not valid completed evaluation, despite its completed ledger record. Exclude the infrastructure failure from the successful-run count and do not recommend process expansion or implementation changes. Treat the generated proposal as evidence-based for a bounded diagnostic only; reject its category-specific causal interpretation until a healthy baseline is observed. Use three valid completed nights as the minimum readiness threshold and five as the preferred threshold, with no recurring infrastructure failures and stable useful metrics. Validation and evidence Evaluation Observatory and append-only ledger review: passed — The run has matching start, completion, manifest, and 16 case-result records. All case records contain the same connection failure and empty response. Run-summary and proposal review: passed-with-infrastructure-failure — The proposal-only cycle finalized with no runner errors, two proposals, and 22 probes reported, but the synthetic evaluation itself had no available model/API response. Prior valid-night comparison: passed — The July 16 comparable RSI run completed cleanly at 16 of 16 with zero invariant failures; it is the only comparable valid nightly evidence counted in this review. Hiro code and runtime mutation: not-run — This was a read-only readiness diagnosis. No Hiro code, configuration, service, schedule, evaluation case, or result was changed, and no Hiro tests were run. Current state Readiness status: not ready for adjustment or expansion. Latest night: infrastructure/model failure; 16 observations completed mechanically but none evaluated a reachable model/API. Successful comparable nightly count: 1; infrastructure failures excluded. Minimum evidence still missing: 2 valid completed nights; preferred evidence still missing: 4. Metrics are not yet reliable across nights because the current scheduled run lacks an availability precondition and preserves only a probe count, not probe outcomes. No change was applied from this review. Next steps Collect at least two additional valid completed scheduled nights, preferably four, only when the model/API availability gate is demonstrably healthy before evaluation begins. Record explicit regression-probe pass/fail outcomes and retain availability/preflight evidence alongside each run so infrastructure failures can be separated mechanically from capability results. After the threshold is met, consider bounded adjustments in this order: broaden test diversity, add adaptive testing that targets stable weak categories, then consider a larger weekly deep run rather than simply increasing nightly repetitions. Require user approval before applying any process adjustment or proposal.

Recurring Windows Path environment constraint documented

2026-07-20T18:53:48-07:00

Executive summary Recorded the recurring Windows Path/PATH case-collision as a standing Hiro workspace constraint so future planning accounts for it before introducing process launchers. The durable guidance requires child-process startup code to collapse the search path to one canonical process-scoped Path value while preserving the remainder of the inherited environment. The note also prohibits using Start-Process -UseNewEnvironment as a shortcut because today's API startup validation showed that discarding inherited runtime context can break Hiro's bundled Python environment. Work completed Durable workspace guidance (Completed) Added a Windows process environment section to the root AGENTS.md instructions used for future Hiro work. Documented that launch environments may simultaneously contain Path and PATH even though Windows treats those names case-insensitively. Documented the observed failure mode: PowerShell environment enumeration and Start-Process can reject the duplicate keys before a child process starts. Specified the safe preparation step: rebuild one process-scoped Path from the machine and user Path values before spawning a Windows child. Required preservation of all other inherited environment values, especially those used by Hiro's bundled and virtual Python runtime. Directed future work to reuse checked-in normalized launch helpers and include a real child-process startup test whenever a new launcher is introduced. Decisions and reasoning Place the note in AGENTS.md rather than leaving it only in conversation history, because that file is loaded as standing workspace guidance for later sessions. Treat Path/PATH normalization as a planning constraint, not merely an error-recovery technique after Start-Process fails. Normalize only the process search path. Do not clear or replace the full inherited environment. Retain real startup validation as a required gate because parser-only and validate-only checks cannot expose Windows process-environment behavior. Validation and evidence Standing instruction content: passed — The root AGENTS.md now contains the Windows process environment section with the collision, normalization, environment-preservation, helper-reuse, and real-startup-test requirements. Workspace diff validation: passed — git diff --check reported no whitespace errors for AGENTS.md. Current state Future Hiro planning is instructed to assume that both Path and PATH may be present in Windows launch environments. New process launchers must normalize to one process-scoped Path before calling Start-Process. The remaining inherited environment must be preserved; Start-Process -UseNewEnvironment is explicitly excluded. The guidance is stored locally in the project root and is not dependent on this conversation remaining in context. Next steps Apply the standing normalization pattern to any future Windows startup, model-launch, dashboard, or automation scripts. Add a real child-process startup test whenever a new launcher crosses the PowerShell-to-Python boundary.

Model-unreachable Telegram alerts removed

2026-07-20T18:08:41-07:00

Executive summary Stopped Hiro's health monitor from sending Telegram messages when the active local model endpoint is unreachable or returns an error. Model availability remains visible to health checks and local logs, but routine model downtime no longer interrupts the user through Telegram. Suppressed both messages associated with a model-availability incident: the initial health alert and the later self-recovery success or failure notification. Other health, lab, lifecycle, and improvement notifications retain their existing Telegram behavior. Restarted the API/dashboard service so the policy took effect immediately and validated the exact unavailable-model condition that had been generating the unwanted messages. Work completed Selective Telegram suppression (Completed) Added an optional telegram_alert policy flag to health issues while preserving the previous default of sending alerts. Marked both LLM_PRIMARY_DOWN and LLM_PRIMARY_ERROR as Telegram-silent at the point where those issues are created. Kept model health detection and local recovery behavior active so silencing Telegram does not hide the condition from Hiro's internal monitoring. Propagated the same policy into model recovery reporting so a silent reachability incident cannot produce a follow-up self-fix Telegram message. Regression coverage (Completed) Added coverage proving that a model-availability issue is returned by the monitor without calling the Telegram sender. Added direct coverage proving an HTTP failure from the model endpoint is tagged Telegram-silent. Added coverage proving model recovery follow-ups remain silent and an unrelated LAB_STUCK issue still sends normally. Ran the new health-monitor tests together with the existing Telegram lifecycle tests. Runtime activation (Completed) Identified the API/dashboard process as the owner of the background health-monitor loop. Restarted the service with the same local runtime, host, port, and application target so the new policy was loaded immediately. Confirmed the dashboard evaluation endpoint remained available after restart while the health endpoint accurately reported that no model endpoint was currently reachable. Decisions and reasoning Suppress model reachability notifications by issue policy instead of disabling Telegram globally, preserving useful non-model alerts. Keep model health checks active rather than removing them; observability and local recovery are still useful even when Telegram escalation is not. Suppress recovery-result messages when they originate from a silent model incident so the user does not receive an indirect version of the same unwanted alert. Limit this change to notification behavior. The legacy checked-in model-profile fallback was observed but not changed as part of this focused session. Validation and evidence Focused notification regression suite: passed — Six focused tests passed, covering model issue suppression, direct model HTTP failure tagging, silent recovery reporting, preservation of non-model alerts, and Telegram lifecycle gating. Runtime service restart: passed — The API/dashboard service restarted successfully, loaded the updated monitor, and listened on its established local port without startup errors. Dashboard availability after restart: passed — The evaluation overview endpoint returned HTTP 200 after restart. The health endpoint returned the expected unavailable status because no model endpoint was reachable. Public journal production build: passed — The timestamp identity tests and full frontend production build passed with eight journal entries; generated validation passed the timestamp schema, same-day identity, alias, sitemap, Atom, noindex, and IndexNow-key checks. Production journal retrieval: passed — The production retrieval validator completed 74 requests with zero failures, covering the timestamped HTML and JSON, same-day sibling, latest aliases, schema-version-2 index, Atom feed, sitemap, noindex metadata, and multiple user-agent profiles. Current state Model-unreachable and model-HTTP-error incidents are retained internally but do not send Telegram alerts. Model self-recovery success or failure messages are also silent when they originate from those incidents. Other health-monitor issues continue to use Telegram unless they explicitly opt out. The restarted dashboard/API service is available; the local model endpoint was unavailable during validation. The checked-in default model profile still references the legacy Gemma profile, even though current model work has moved to Qwen. This timestamped journal entry is live and independently retrievable alongside the day's earlier entries. Next steps Observe the next model-down health interval and confirm no Telegram message is received. Align the checked-in active model fallback and recovery launcher with the current Qwen deployment in a separate focused change.

Timestamped Hiro journal entries and same-day preservation

2026-07-20T17:25:10-07:00

Executive summary Reworked the Hiro development journal so a calendar date is no longer the unique identity of an entry. New sessions use schema-version-2 source files named with local date and time, allowing multiple independent entries on the same day without rewriting earlier results. Preserved all existing schema-version-1 date-only entries and their established URLs. The generator normalizes legacy entries for sorting and feeds while assigning every new timestamped entry a unique dotted source path and a unique hyphenated canonical route. Updated HTML pages, machine-readable indexes, Atom entries, sitemap URLs, latest aliases, retrieval validation, and AI-discovery text to carry exact publication timestamps and distinct same-day identities. Updated Hiro's local journaling instructions so future sessions reserve one America/Los_Angeles timestamp, reuse it only within that logical session, and never append later same-day work to an earlier entry. Work completed Backward-compatible timestamp schema (Completed) Added schema-version-2 validation for publishedAt timestamps with seconds and an explicit UTC offset, plus timeZone fixed to America/Los_Angeles. Required timestamped source filenames in YYYY.MM.DD.HHmmss.json form and verified that both the date and six time digits match the entry timestamp. Kept existing YYYY.MM.DD.json schema-version-1 files valid as legacy entries without renaming or rewriting them. Added duplicate guards for source IDs, canonical entry IDs, and normalized publication instants. Unique generation, routing, and discovery (Completed) Changed journal sorting from date strings to normalized publication instants, with deterministic entry-ID tie breaking. Generated unique canonical routes in YYYY-MM-DD-HHmmss form and unique dotted source directories in YYYY.MM.DD.HHmmss form for timestamped entries. Added publishedAt, timeZone, entryId, sourceId, schemaVersion, and legacy metadata to the machine-readable index. Made Atom IDs, published timestamps, updated timestamps, sitemap URLs, HTML metadata, JSON-LD datePublished, and index cards use the unique entry timestamp. Kept date-only aliases for legacy entries. For future dates with only timestamped entries, the date-only alias resolves to that day's newest entry while every session retains its unique route. Changed latest.html and latest.json to select the newest publication instant rather than the lexically newest date directory. Tests and operating instructions (Completed) Added a dedicated test:hiro command with fixtures for one legacy entry and two timestamped entries on the same day. Tested deterministic same-day ordering, unique route derivation, duplicate rejection, and timestamp-to-filename mismatch rejection. Expanded generated-artifact validation to check schema version 2, exact latest timestamps, unique same-day identities and URLs, stable JSON aliases, Atom timestamps, sitemap coverage, and noindex metadata. Updated production retrieval diagnostics to follow the newest indexed entry's unique URLs and also retrieve a same-day sibling when one exists. Updated the repository content guide and Hiro's local AGENTS.md so a later same-day work session creates a new timestamped source file instead of editing an earlier result. Decisions and reasoning Use a full ISO 8601 publishedAt value instead of a time-only field so ordering is unambiguous across UTC offsets and daylight-saving transitions. Include seconds in source filenames and canonical IDs to make collisions unlikely while keeping URLs readable and deterministic. Do not migrate or rename historical date-only sources. Backward compatibility is safer than changing established canonical content, Atom IDs, and direct retrieval URLs. Treat a logical work session as the unit of immutability: publication retries and validation-status corrections update that session's file, while later work reserves a new timestamp. Keep the journal public but unlisted with noindex, follow metadata, and preserve direct retrieval through stable HTML, JSON, Atom, sitemap, and llms.txt surfaces. Validation and evidence Timestamp identity unit tests: passed — The dedicated test:hiro fixtures passed for legacy compatibility, two same-day timestamped entries, chronological sorting, duplicate rejection, and filename/timestamp mismatch rejection. Generator and validator syntax: passed — Node syntax checks completed successfully for the page generator, alias generator, generated-artifact validator, and production retrieval validator. Same-day production artifact build: passed — The full generator and frontend build completed with seven entries. Generated validation passed timestamp schema, unique same-day identity, aliases, sitemap, Atom content, noindex, and IndexNow-key checks. Legacy URL preservation: passed — The existing July 20 source and date-only generated JSON are semantically identical schema-version-1 content, and /hiro/2026-07-20 retains its original canonical URL while the timestamped entry uses a distinct route. Live timestamped retrieval: passed — Production retrieval passed for the unique timestamped HTML and JSON, the earlier same-day legacy HTML and JSON, latest aliases, schema-version-2 index, Atom feed, sitemap, and noindex metadata. Current state The journal source model supports immutable same-day sessions through schema-version-2 timestamped files. Six historical date-only entries remain unchanged and supported as schema-version-1 legacy content. This entry is the first timestamped source and coexists with the earlier July 20 legacy entry. The journal remains public but unlisted and retains noindex, follow metadata. The timestamped entry and its same-day legacy sibling are both live and independently retrievable. Next steps Run the full generator and frontend production build with both July 20 entries present. Verify the index, Atom feed, sitemap, latest aliases, unique timestamped routes, and the existing date-only July 20 routes. Publish the journal-system changes and this timestamped entry, then run the production retrieval validator. Use a fresh timestamped source for every later substantive Hiro work session.

Local model tuning and Hiro self-improvement orchestration

2026-07-20T00:00:00Z

Executive summary Hiro's two local-model libraries were relocated from the space-constrained system volume to dedicated directories on E:. LM Studio now downloads to E:\AI\Models\LMStudio, while Hiro's legacy GGUF library resides at E:\AI\Models\llama.cpp. The migration used copy-first activation. Every source and destination file was compared by relative path, byte length, and SHA-256 before the original paths were replaced by compatibility junctions. This preserved Hiro's existing pinned paths without changing application source. LM Studio discovered four language models and one embedding model after migration. A real 6.50 GiB Gemma 4 12B load from E: completed, used the intended 4,096-token single-slot configuration, returned READY in 0.363 seconds, and unloaded cleanly. Removing the verified C: backups increased measured free space from 20.83 GiB to 65.25 GiB, reclaiming 44.42 GiB. The main tradeoff is cold-load latency: the Gemma load took 3 minutes 13.95 seconds from E:, far slower than the historical system-volume startup. A controlled follow-up compared the same 6.497 GiB Gemma file on the separate D: and E: USB hard drives. Two uncached reads averaged 104.16 MiB/s on D: versus 42.52 MiB/s on E:, making D: 2.45 times faster. A fresh-file LM Studio cold load took 69.67 seconds from D: versus 199.73 seconds from E:, a 65.1% reduction. Once loaded, storage placement did not materially change inference: the first fixed response took 0.252 seconds from D: and 0.253 seconds from E:, with subsequent responses near 0.11 seconds on both. Hiro was restored to its original E: configuration after the benchmark; both temporary D: copies were deleted, and no model or server was left running. After the benchmark established D: as the faster device, Hiro's complete active model estate was intentionally migrated from E: to D:. The move covered 19.392 GiB across five LM Studio files and 31.516 GiB across thirteen legacy files; all 50.908 GiB matched by relative path, length, and SHA-256 before activation. LM Studio now downloads directly to D:\AI\Models\LMStudio, while the active legacy library is D:\AI\Models\llama.cpp. Direct compatibility junctions preserve both the prior C: and E: paths. A real Gemma load completed in 69.76 seconds, three READY responses passed, the E: backups were removed, and no model or server was left running. Downloaded bartowski's recommended single-file Q4_K_M conversion of Qwen3.6-35B-A3B directly through LM Studio to D:. The 22,285,080,192-byte main GGUF matched the publisher's SHA-256 exactly, and LM Studio also obtained the 899,283,296-byte F16 vision projector. Qwen3.6-35B-A3B Q4_K_M was evaluated in LM Studio with an 8,192-token context, one parallel slot, and full GPU offload. Estimate-only reported 22.51 GiB. The real load completed in 399.48 seconds; LM Studio reported a 21.59 GiB model footprint, while total GPU use increased from 6,307 MiB before loading to 29,996 MiB after loading. The 120-case deterministic Hiro core suite completed without API errors at a raised 1,024-token generation ceiling. The primary run averaged 78.37%, with 94 of 120 cases meeting the 70% pass threshold: evidence obedience 29/30, no-evidence refusal 17/20, simulated tool routing 25/25, structured JSON 20/20, troubleshooting 0/15, and self-improvement specification 3/10. The weak categories were dominated by reasoning-token starvation rather than inability to solve the tasks. LM Studio kept Qwen3.6 thinking enabled even when OpenAI-compatible template parameters and the native reasoning-off request were attempted. A repeated 120-case sweep reproduced the same runaway-reasoning pattern. Two representative failed cases succeeded when retried at 4,096 tokens, taking 8.49 and 8.99 seconds and consuming 1,144 and 1,240 hidden reasoning tokens respectively. Qwen3.6 passed a real function-call request in 1.21 seconds, selecting the requested weather function with the correct Seattle argument. It also passed a near-full-window retrieval probe with 7,623 prompt tokens and 7,817 total tokens in 2.46 seconds, returning the exact final ledger marker. Raw decode throughput is strong on the RTX 5090: a sustained sample produced 2,048 completion tokens in 12.02 seconds, or 170.32 tokens per second. However, 1,829 tokens, 89.3%, were hidden reasoning, and the requested long answer stopped after only 165 visible words. A synthetic vision card was read correctly in 3.10 seconds, identifying all three colored shapes and the text HIRO 8K. Qwen3.6 is not being promoted to Hiro's default in its current runtime configuration. Its primary 78.37% suite score is below the historical Qwen 9B score of 79.96% and Gemma 4 score of 83.13%, despite the larger output allowance, and reasoning overhead breaks Hiro's compact-response economics. The model was unloaded, the API server was stopped, and total GPU use returned to 6,482 MiB. Restored Hiro's local Evaluation Observatory at the previously used http://127.0.0.1:8765/benchmarks address. The failure was service availability, not lost dashboard code or evaluation data: no process was listening on port 8765, while the FastAPI route, static dashboard, and 1.5 MB evaluation ledger remained present. The machine no longer had a registered system Python interpreter capable of running Hiro. Rebuilt the pinned project environment inside Hiro's ignored .runtime directory, leaving the dirty source worktree and all existing application changes untouched. Started the existing FastAPI application as a standalone loopback-only service on port 8765. Live checks returned HTTP 200 for the dashboard, confirmed the Evaluation Observatory page title, and loaded the read-only overview API with 100 completed runs across 101 variants. Left the local dashboard service running so benchmark history can be monitored without loading an LLM. The latest ledger entry remains the completed resume-live-soak run with a 1.0 weighted score. Repeated Qwen3.6-35B-A3B Q4_K_M at 8,192-token context through Hiro's actual append-only Evaluation Observatory ledger, using one slot, full GPU offload, the frozen emergence-level1-public@1.1.0 suite, and the full run_turn agent path. The valid 16-observation run scored 85.42% with a 37.5% strict pass rate, zero invariant failures, and 3,545.67 ms p95 latency. Three matched Gemma 4 12B ledger runs averaged 95.49%, 72.92%, and 379.91 ms respectively, so Qwen trailed by 10.07 score points and 35.42 pass-rate points while running 9.33 times slower at p95. A first attempt was preserved as invalid audit evidence because restricted execution made Hiro's main conversation database read-only and every case failed before inference. The valid r2 run used normal Hiro host permissions; its zero invariant failures and recorded model responses distinguish the model result from the infrastructure-only r1 failure. The matched ledger run reinforces the no-promotion decision. Qwen's forced hidden reasoning frequently exhausted Hiro's 512-token agent completion allowance, leaving partial reasoning fragments instead of the exact compact answers the suite requested. The confidence intervals overlap at this small sample size, so the ledger does not label the score difference statistically clear, but the quality and latency directions are both unfavorable. After ingestion, the live loopback Evaluation Observatory reported 130 completed runs across 103 variants and identified qwen36-35b-a3b-8k-public-20260720-r2 as the latest run. Qwen was unloaded and the LM Studio API stopped; the dashboard remained available at http://127.0.0.1:8765/benchmarks. A read-only architectural diagnosis mapped what would be required to move evaluation orchestration and self-improvement from an external ChatGPT-led development loop into Hiro. Hiro already has strong measurement primitives and proposal generation, but the trusted variant laboratory is intentionally operator-driven while the autonomous legacy loop lacks equivalent isolation, comparative baselines, and reliable rollback. The central migration challenge is preserving an evaluator that the candidate cannot influence. Hiro may own scheduling, hypothesis formation, candidate construction, test execution, and evidence packaging, but evaluator definitions, held-out cases, safety policy, permission grants, and high-risk promotion authority must remain outside the mutable candidate boundary. Changed Hiro's local router from a universal 512-token completion ceiling to model-profile-aware defaults. Gemma and the smaller Qwens retain 512 tokens, explicitly short calls retain their requested limits, and the new qwen35 profile receives 7,168 tokens on both non-streaming and streaming paths. The larger Qwen budget fixed the observed reasoning starvation. A live Hiro preflight expanded the ordinary request to 4,096 tokens and returned visible READY in 1.41 seconds; a 4,096-token ledger run then scored 97.92% and passed 15 of 16 observations, versus 85.42% and 6 of 16 at 512 tokens. The only 4,096-token failure passed as LABEL: GLIM in 7.63 seconds with 7,168-token headroom. The final 7,168-token run passed all 16 observations, scored 100%, achieved 1.0 in every category, and recorded zero invariant failures. Qwen's perfect quality result exceeded the three-run Gemma mean by 4.51 score points and 27.08 pass-rate points, and exceeded the starved Qwen run by 14.58 score points and 62.5 pass-rate points. The improvement over starved Qwen is statistically clear under the ledger comparison; the smaller advantage over Gemma is not statistically clear at this sample size. The quality gain has a substantial latency cost: the perfect run averaged 9.83 seconds, had a 4.82-second median, and reached 36.77 seconds at p95. That p95 is 96.8 times the matched Gemma mean, so the tuned Qwen profile is quality-first rather than a universal low-latency replacement. After validation, Qwen was unloaded and LM Studio's API stopped. The loopback Evaluation Observatory remained online, reporting 132 completed runs across 105 variants with qwen36-35b-a3b-8k-public-7168-20260720-r4 as the latest result. Defined a staged operating workflow for transferring self-improvement work from ChatGPT-assisted operation into Hiro. The sequence starts with observation, clustering, experiment specification, evaluation orchestration, and evidence packaging before expanding into isolated candidate construction and narrowly bounded automatic promotion. The transition is organized around task-level delegation rather than an all-at-once autonomous loop. ChatGPT remains the reviewer and implementation fallback while Hiro proves reliability at each stage, with responsibility expanding after repeated cycles produce complete, reproducible, useful handoff packets. Implemented cycle coordinator v0, the first operational step in transferring self-improvement work into Hiro. It consumes one structured ImprovementSpec, validates its evidence and planned scope, records an immutable cycle, runs or resumes paired public and external held-out baseline evaluations, pins the baseline, and writes review-ready JSON and Markdown evidence packets. The coordinator intentionally stops before candidate construction or promotion. Twenty-five focused evaluation, variant-laboratory, CLI, and coordinator tests passed after redirecting pytest temporary files into Hiro's ignored runtime directory; the initial default-temp attempt was an infrastructure failure rather than a product-test failure. Added a nightly synthetic evaluation harvest to Hiro's existing 22:00 self-improvement job so useful improvement evidence no longer depends on organic conversation volume. The first development suite runs eight versioned public cases with two repetitions each, for at most sixteen Hiro turns per night. Each nightly run now uses deterministic scoring and the append-only evaluation ledger, resumes interrupted work, reuses an already-completed same-day run, compares against the previous matching run, and converts the highest-impact failure category into a cycle-coordinator-compatible ImprovementSpec. The scheduler remains proposal-only: held-out cases, candidate construction, promotion, and live code changes are excluded. Twenty-eight focused evaluation, laboratory, coordinator, nightly-harvest, and scheduler tests passed. Removed the running Hiro API as a manual prerequisite for the nightly evaluation. A registered Windows task now wakes at 21:55, checks the real Observatory endpoint, starts Hiro through the pinned project runtime only when needed, waits for readiness, and keeps the API process attached through a ten-hour overnight window. Preserved one scheduler authority by serving HTTPS 8000, HTTP 8001, and the familiar loopback Evaluation Observatory at 8765 from the same Hiro process. Live verification showed all three listeners owned by one PID, while the scheduled preflight remained in the Running state. Added a Nightly tab to the Evaluation Observatory. It reads append-only nightly ledger runs, displays pass rate, weighted score, failures, latency, failed cases, longitudinal history, and the deterministic improvement signal without exposing held-out cases or adding promotion controls. Added a daily Codex readiness review for Hiro's nightly self-improvement process. The review runs each morning after the overnight window, classifies the latest result separately from infrastructure failures, compares valid nights longitudinally, and recommends bounded adjustments only after repeated reliable evidence. The readiness policy requires at least three valid completed nights, with five preferred, before expanding the workload. It favors broader case diversity and adaptive testing over repeated execution of the same small suite, and reserves larger workloads for a separately reviewed weekly deep run. A live pre-night check found that the API and Evaluation Observatory were available and the 22:00 schedule was enabled, but the local model endpoint was unavailable. Consequently, an evaluation recorded tonight without additional model startup would be classified as infrastructure evidence rather than a valid product-quality baseline. Work completed Storage and process inventory (Completed) Confirmed that no LM Studio, llama.cpp, or Hiro model process and no relevant inference listener was active before copying began. Inventoried 19.392 GiB across five files in LM Studio's managed model directory. Inventoried 31.516 GiB across thirteen files in Hiro's legacy llama.cpp directory. Confirmed that E: was NTFS with approximately 3.39 TiB free and that the selected destination and backup paths did not already exist. Read LM Studio's active download setting without exposing credentials and confirmed that it still targeted the system-volume model directory. Copy-first model migration (Completed) Created E:\AI\Models\LMStudio and E:\AI\Models\llama.cpp as the two durable destinations. Copied each library independently with retry-limited, metadata-preserving file transfer. For every file, compared source and destination relative paths, byte lengths, and SHA-256 hashes before activation. Kept the original directories unchanged throughout copying and hashing. A report-generation bug stopped the first attempt before any live path changed, demonstrating that the activation guard worked. Resumed safely from the completed first copy, repeated verification, and activated destinations only after both libraries matched. Compatibility and LM Studio configuration (Completed) Replaced the former LM Studio model directory with a directory junction targeting E:\AI\Models\LMStudio. Replaced the former legacy llama.cpp directory with a directory junction targeting E:\AI\Models\llama.cpp. Updated LM Studio's downloadsFolder setting to the direct E: destination while preserving a small pre-migration settings backup. The compatibility junctions allow Hiro's existing launcher and benchmark registry paths to continue resolving without source edits. A settings-rewrite guard rejected an incorrect substring-count implementation after junction activation. The setting was then updated from a separately patched, JSON-validated copy; model files and junctions were unaffected. Runtime and model-discovery validation (Completed) LM Studio indexed four local language models and one embedding model from the E: managed library, including the expected Gemma 4 12B QAT entry. LM Studio reported no loaded model before the real-load test. Hiro's pinned Qwen 9B and Gemma 4 paths both resolved through the legacy compatibility junction with their expected 7.134 GiB and 6.497 GiB sizes. Gemma 4 12B QAT loaded from E: with maximum GPU offload, a 4,096-token context, one parallel slot, and the expected identifier. The load completed in 3 minutes 13.95 seconds. During the loaded-state check, total GPU memory use was 12,844 MiB including Windows display and runtime allocations. A local chat-completions warmup returned READY in 0.363 seconds. Gemma then unloaded successfully, LM Studio reported an empty loaded-model list, and the temporary API server was stopped. System-volume cleanup and final state (Completed) Preserved the original directories under reversible backup names until discovery, path resolution, real model load, inference, and unload had all passed. Validated that the original path objects were junctions to the exact E: destinations and that the backup objects were normal directories within the intended user profile. Removed only the two verified model-library backups after all runtime gates passed. Measured C: free space immediately before and after cleanup: 20.83 GiB before, 65.25 GiB after, for 44.42 GiB reclaimed. Left no model loaded and no LM Studio API server active. Controlled E: versus D: Gemma benchmark (Completed) Mapped D: and E: to two separate healthy 6 TB WD Elements USB hard drives rather than two partitions on one device. D: had approximately 948 GiB free before the temporary copy. Copied the exact 6.497 GiB Gemma Q4_0 GGUF to a temporary D: benchmark directory with unbuffered I/O. Source and destination matched in byte length and SHA-256 before any LM Studio path changed. Read the complete GGUF twice per drive with Windows no-buffering and sequential-scan flags. E: measured 42.52 and 42.51 MiB/s; D: measured 104.56 and 103.76 MiB/s. Repeated the full LM Studio configuration on both drives: maximum GPU offload, 4,096-token context, one parallel slot, and the same model identifier. E: loaded in 199.73 seconds. A fresh, never-read D: copy loaded in 69.67 seconds. Excluded a preliminary D:-routed run because the preserved E: directory remained within LM Studio's scan tree and produced two matching candidates. The preserved directory was moved outside the scan tree before the accepted D: trial so exactly one model was discoverable. Measured a 5.25-second D: reload after the first D: load and classified it as warm-cache behavior rather than disk performance. The same three fixed READY prompts returned correctly on each drive. E: measured 0.253, 0.107, and 0.108 seconds; D: measured 0.252, 0.110, and 0.109 seconds. After the accepted D: load, its fresh source still matched the expected SHA-256. The original normal E: directory and LM Studio download setting were restored, 12.994 GiB of temporary D: copies was deleted, and final checks found no loaded model or running API server. Permanent activation of D: for active models (Completed) Closed five LM Studio desktop processes after confirming that no model was loaded and no inference server was active, preventing library changes during migration. Inventoried 19.392 GiB across five managed LM Studio files and 31.516 GiB across thirteen legacy files. D: had 947.84 GiB free and both intended destination directories were absent. Copied the managed library in 465.93 seconds and the legacy library in 756.73 seconds with unbuffered, metadata-preserving transfers. The original E: directories and all live paths remained unchanged during copying. Hashed the full E: and D: estates concurrently. All eighteen relative paths, byte lengths, and SHA-256 values matched across 50.908 GiB with zero discrepancies; the integrity phase took 1,250.93 seconds. Renamed the original E: directories to reversible backup names, created E: compatibility junctions targeting D:, and repointed the two existing C: junctions directly to D: rather than chaining through E:. Patched and JSON-validated a copy of LM Studio's settings, preserved a pre-D: settings backup, and changed downloadsFolder to D:\AI\Models\LMStudio. LM Studio discovered four language models and one embedding model from D:. Hiro's pinned Qwen 9B and Gemma paths resolved through the direct C: legacy junction with their expected byte sizes. Gemma loaded from the single D: candidate in 69.76 seconds with maximum GPU offload, a 4,096-token context, and one parallel slot. Three fixed prompts returned READY in 0.319, 0.110, and 0.095 seconds. Gemma unloaded and the API server stopped. After rechecking direct junction targets, D: file counts, and LM Studio's setting, removed the two original E: backup directories and measured 50.91 GiB reclaimed on E:. Final state: D: has 896.94 GiB free, E: has 3,391.19 GiB free, no model is loaded, and the LM Studio API server is stopped. Qwen3.6-35B-A3B acquisition and integrity validation (Completed) Verified the official Qwen/Qwen3.6-35B-A3B base model and reviewed current GGUF options before downloading. Selected bartowski/Qwen_Qwen3.6-35B-A3B-GGUF because its single-file Q4_K_M is documented as the recommended default balance for most use cases. Confirmed LM Studio's downloadsFolder was D:\AI\Models\LMStudio, D: had 896.94 GiB free, no matching local Qwen3.6-35B-A3B file existed, no model was loaded, and the local API server was stopped. Downloaded the exact Qwen_Qwen3.6-35B-A3B-Q4_K_M.gguf artifact through LM Studio from Hugging Face. LM Studio automatically placed it in the managed bartowski repository directory on D: and also downloaded the associated F16 multimodal projector. The complete transfer, finalization, and indexing took 1,015.95 seconds. The finalized directory contains a 22,285,080,192-byte main GGUF and an 899,283,296-byte projector, with no partial-download file remaining. Computed SHA-256 across the complete main GGUF in 285.48 seconds. The local digest B46FEDD33E0BFB0CAE308AA3C158D0A4B2C4A1D2185A1ED6F093CDAF39064772 exactly matches the publisher's recorded digest. LM Studio indexes the combined model package at 23,184,363,488 bytes and identifies it as qwen35moe, Q4_K_M, vision capable, trained for tool use, and supporting up to 262,144 tokens. Ran LM Studio's estimate-only path with maximum GPU offload, a 4,096-token context, and one parallel slot. Estimated GPU and total memory are both 22.38 GiB, within the RTX 5090's 32 GB VRAM. D: has 875.34 GiB free after the acquisition. No model was loaded and no API server was started; actual cold-load, inference-speed, quality, tool-use, and vision tests remain separate work. Qwen3.6 8K runtime and Hiro evaluation (Completed) Ran LM Studio's 8,192-token estimate with maximum GPU offload and one parallel slot. Estimated GPU and total memory were both 22.51 GiB. Loaded the verified Q4_K_M model under a dedicated benchmark identifier. The load took 399.48 seconds from D:, LM Studio reported 21.59 GiB, and observed total GPU allocation reached 29,996 MiB from a 6,307 MiB pre-load baseline. Started the local API only after loaded-state confirmation and used the exact loaded identifier for every inference request. Discovered during warmup that short requests could exhaust their entire visible-output allowance in separated reasoning_content. OpenAI-compatible chat-template parameters, a prompt control marker, and the native reasoning-off control did not disable reasoning for this loaded GGUF; the native endpoint reported that the model did not expose reasoning configuration. Executed all 120 deterministic bake-off cases with a 1,024-token ceiling so most tasks could reach visible output. The primary run completed with zero request errors, averaged 78.37%, and passed 94 cases at the established 70% threshold. The category results were 86.29% average for evidence obedience, 74.55% for no-evidence refusal, 84.43% for simulated routing, 97.98% for structured JSON, 46.97% for troubleshooting, and 55.00% for self-improvement specifications. A second full sweep completed and reproduced the same material instability: reasoning-heavy cases frequently consumed all 1,024 tokens before producing a visible answer. Retried one troubleshooting and one self-improvement failure at 4,096 tokens. Both produced substantively correct visible answers, but only after more than 1,100 hidden reasoning tokens and roughly nine seconds per request. Validated real tool calling: the model returned finish_reason tool_calls, chose get_weather, and emitted the argument Seattle, Washington in 1.21 seconds without fabricating a weather result. Validated 8K behavior using a 7,623-token synthetic ledger. The response used 7,817 total tokens, returned the exact final marker, and completed in 2.46 seconds. Measured 170.32 raw completion tokens per second on a sustained generation request, while documenting that 89.3% of the 2,048-token output budget was hidden reasoning and the visible response stopped early. Validated multimodal inference with a known synthetic card containing a red circle, blue square, green triangle, and HIRO 8K text. The model identified every expected element correctly in 3.10 seconds. Unloaded the model, stopped the API server, removed temporary local test fixtures, and verified that LM Studio reported no loaded model. Local Evaluation Observatory restoration (Completed) Confirmed that port 8765 had no listener, directly explaining why the remembered dashboard URL would not open. Verified that api/server.py still exposes the public /benchmarks route and serves static/benchmarks.html, and that the read-only /evaluations/api endpoints remain registered. Verified that hiro/evals/evaluations.sqlite remained intact at approximately 1.5 MB, with its SQLite shared-memory files present and no evidence of ledger deletion. Confirmed that 8765 is not Hiro's committed application port. The normal configuration remains 8000 for HTTPS plus 8001 for HTTP; 8765 was a standalone local dashboard launch. Found that the Windows Python launcher no longer resolved an installed interpreter and that no existing Hiro virtual environment was available. Created an isolated environment at .runtime/dashboard-env using the bundled Python 3.12 interpreter and installed Hiro's exact pinned requirements. The .runtime tree is ignored and no source or tracked configuration file changed. Started uvicorn with api.server:app on 127.0.0.1:8765. Binding only to loopback preserves the local-only scope of the remembered URL. Validated the live dashboard HTML and overview API. The API reported 100 runs, all 100 completed, 101 variants, and a latest weighted score of 1.0 for the resume-live-soak entry. Opened the restored URL in the system browser and intentionally left the service running for ongoing benchmark tracking. No local language model or LM Studio API server was started. Matched Qwen3.6 Evaluation Observatory ledger run (Completed) Selected the existing frozen emergence-level1-public@1.1.0 suite: eight public cases with two repetitions each, for sixteen observations covering rule induction, structural and relational transfer, composition, distractor resistance, exception transfer, and calibration. Used the same full Hiro run_turn agent path and 512-token local completion ceiling as the compatible Gemma evaluation-isolation baseline. A temporary untracked runner redirected both local routing tiers to the exact loaded qwen36-35b-a3b-8k-ledger identifier without changing Hiro source or persistent configuration. Cold-loaded Qwen from D: in 419.63 seconds with maximum GPU offload, 8,192 context tokens, and one parallel slot. LM Studio reported a 21.59 GiB model allocation and the loaded-state check reported 29,495 of 32,607 MiB total GPU memory in use. The API preflight confirmed that the response model exactly matched qwen36-35b-a3b-8k-ledger. Even the 32-token READY request returned empty visible content and 104 reasoning characters, reproducing the forced-reasoning behavior before the ledger run. Preserved qwen36-35b-a3b-8k-public-20260720-r1 as an invalid infrastructure record. Restricted execution could not write Hiro's existing conversation database, so all sixteen observations stopped before inference with database errors; the result was not used as model evidence. Completed qwen36-35b-a3b-8k-public-20260720-r2 under Hiro's normal host permissions. The append-only resume mechanism retained six valid observations after the supervising command stopped watching and completed the remaining ten without duplication. The valid run produced a 0.854167 weighted score, 0.375 strict pass rate, zero invariant failures, and 3,545.67 ms p95 latency. Six of sixteen observations passed every assertion. Compared against three matched Gemma 4 12B runs, whose means were 0.954861 weighted score, 0.729167 pass rate, zero invariant failures, and 379.91 ms p95. Qwen was 0.100694 lower in weighted score, 0.354167 lower in pass rate, and 9.33 times slower at p95. The strongest Qwen category was relational transfer at 1.0. The largest category deficit against the selected Gemma r3 baseline was distractor resistance at -0.25, followed by exception transfer at -0.1667 and rule induction at -0.125. Inspected failures and found compact-answer violations and apparent reasoning fragments such as incomplete analysis rather than runtime errors. The ledger comparison reported no invariant regression, overlapping pass-rate confidence intervals, and statistically_clear=false. Verified the live dashboard returned HTTP 200 and its compare endpoint reproduced the ledger metrics. Unloaded Qwen, stopped only LM Studio's port-8080 API, removed temporary runners, and reverified that the port-8765 dashboard stayed online. Self-improvement autonomy boundary diagnosis (Completed) Inspected the current proposal-only self-improvement v2 scheduler, the legacy patch-and-promote cycle, the evaluation runner and deterministic scorer, append-only evaluation and variant ledgers, external held-out provider, worktree manager, proposal-only promotion gate, dashboard routes, and the associated evaluation and laboratory tests. Confirmed that the nightly default is self_improvement_v2 in proposal_only mode. It analyzes response envelopes, produces structured ImprovementSpecs and curiosity items, writes research output, and runs lightweight probes, but its proposals are never auto-applied. Confirmed that the newer variant laboratory can pin paired public and held-out baselines, create candidate worktrees outside the source repository, audit declared path scope, compare repeated metrics, reject invariant and category regressions, and produce immutable proposals. Its CLI still requires an operator to sequence those actions and contains no candidate editor, automatic evaluation orchestration, merge, deployment, or cleanup authority. Contrasted the laboratory with the legacy run_cycle path. The legacy path can generate model-authored patches and directly apply low-risk changes to the live checkout before evaluating them. Its evaluator computes a post-change score but does not execute a true isolated baseline-versus-candidate comparison, and its experiment-level rollback command can require manual restoration. Identified the required staged migration: unify observations into evidence-backed opportunities; convert ImprovementSpecs into falsifiable experiments with declared scopes; create candidates only in isolated worktrees; evaluate stable and candidate variants on identical public and external held-out suites; apply statistical, invariant, scope, cost, and reliability gates; package eligible results for human approval; and only later consider bounded automatic promotion for narrowly reversible change classes. No Hiro files, runtime configuration, evaluation records, services, or model state were changed during this diagnosis. No Hiro tests or model evaluations were run; conclusions are based on source, documentation, and existing test definitions. Qwen35 quality-first output budget and perfect ledger rerun (Completed) Traced the failure mechanism to core/router.py: the ordinary chat, non-streaming local call, and streaming local call all defaulted to 512 completion tokens regardless of model capability. Added a persistent qwen35 model profile targeting the standardized qwen36-35b-a3b LM Studio identifier on port 8080. Added per-profile default_max_tokens metadata while preserving 512 for Gemma, Qwen 4B, and Qwen 9B. Added a resolver that expands only the ordinary 512-token default for a matching profile. Explicitly short budgets such as the 120-token grounded-tool summarizer remain unchanged, and unknown models retain the caller's requested value. Applied the resolved budget to both OpenAI-compatible non-streaming and streaming local payloads so API and interactive paths cannot diverge. Added focused tests for Qwen expansion, explicit-short preservation, smaller-profile stability, unknown-model stability, and the actual max_tokens value transmitted in the HTTP payload. Fourteen targeted router and classifier tests passed in 0.65 seconds; pytest emitted only a future fixture-scope deprecation warning. Cold-loaded the same verified Qwen3.6 Q4_K_M model from D: at 8,192 context tokens, one slot, and maximum GPU offload. This load took 618.23 seconds; LM Studio again reported 21.59 GiB and observed total GPU use was 29,496 of 32,607 MiB. Verified the exact qwen36-35b-a3b identifier through Hiro's real local-call path. At a 4,096 effective budget, the model returned visible READY in 1.41 seconds instead of exhausting the allowance in separated reasoning. Ran qwen36-35b-a3b-8k-public-4096-20260720-r3 through emergence-level1-public@1.1.0. It scored 0.979167, passed 15/16 observations, had zero invariant failures, and measured 15,269.71 ms p95. The score gain over r2 was 0.125 and statistically clear. The sole r3 failure was a second relation-class repetition that returned an incomplete reasoning fragment after 23.73 seconds. Retesting that exact full-agent prompt with 7,168-token headroom returned the required LABEL: GLIM in 7.63 seconds. Raised the qwen35 profile to 7,168 tokens, leaving a nominal 1,024-token reserve within the 8K runtime context, and reran all regression tests successfully. Ran qwen36-35b-a3b-8k-public-7168-20260720-r4. All sixteen observations passed, weighted score and pass rate were both 1.0, every category score was 1.0, and invariant failures remained zero. The perfect run's latency distribution was 1,720.59 ms minimum, 4,824.30 ms median, 9,825.35 ms mean, 36,770.73 ms p95, and 37,363.94 ms maximum. Headroom does not force every response to consume the full budget, but two distractor cases and one calibration case used long reasoning paths. Verified r4 as the latest live dashboard entry, unloaded Qwen, stopped LM Studio's port-8080 API, removed all one-off runners, and rechecked that the port-8765 dashboard still returned HTTP 200. Incremental self-improvement transition workflow (Designed) Established an initial division of responsibility: Hiro owns evidence collection, clustering, prioritization, experiment specifications, routine evaluation execution, comparison, and reporting; ChatGPT initially owns code implementation, judgment on ambiguous results, and integration review; the user retains priority and promotion decisions. Defined a six-stage delegation ladder: structured nightly triage, experiment design, evaluation orchestration, isolated candidate construction, bounded repair and retesting, and finally a narrow automatic-promotion lane. Specified one canonical experiment state machine from observed through triaged, specified, approved, baseline_ready, candidate_ready, evaluated, review_ready, promoted or rejected, and monitored. Each transition must append evidence rather than overwrite earlier records. Defined the handoff packet Hiro should produce for every review: source evidence, failure cluster, falsifiable hypothesis, target metric, allowed paths, baseline and candidate manifests, test and evaluation results, category regressions, diff summary, runtime cost, recommendation, and rollback instructions. Prioritized evaluation orchestration as the first substantial offload because it is repetitive, deterministic, already supported by the evaluation kernel, and easier to verify than autonomous code editing. Kept practical minimum boundaries rather than treating security hardening as the primary blocker: candidate changes remain in an external worktree, evaluation assets remain outside candidate write scope, only one experiment advances at a time, and integration continues to require review until rollback and post-promotion monitoring are demonstrated. No Hiro source, configuration, runtime state, evaluation ledger, model process, or scheduled task was changed while designing this workflow. Cycle coordinator v0 implementation (Completed) Saved the agreed six-stage transition plan as docs/HIRO_SELF_IMPROVEMENT_TRANSITION.md, including responsibility transfer, the canonical state machine, stage graduation criteria, daily cadence, transition metrics, and coordinator usage. Added hiro/improvement/cycle_coordinator.py with a CLI that loads exactly one ImprovementSpec from JSON, a v2 run summary, or JSONL and requires explicit selection when several specs are present. Added a normalized v0 handoff contract requiring an identifier, title, problem, falsifiable hypothesis, source evidence, proposed change, expected benefit, rollback plan, valid risk classification, and, for code changes, an allowed path plus at least one planned test. Added deterministic cycle identifiers derived from the normalized spec, public and held-out suite hashes, and the exact baseline manifest. Repeating identical inputs reuses completed runs or resumes interrupted runs instead of duplicating model work. Added append-only ImprovementCycle and cycle-event records to the variant laboratory ledger. A separate pre-candidate cycle record is used because the existing VariantExperiment contract correctly assumes a candidate worktree already exists. The coordinator builds or reuses an immutable baseline manifest, runs public and held-out suites through the existing EvaluationRunner, creates an immutable BaselinePin, and records specified, baseline_started, per-suite completion, review_ready, and failure events. Evidence output is written atomically beneath the ignored .hiro_runtime/cycle_coordinator directory. Packets contain the normalized spec, cycle definition, manifest, baseline pin, and compact public and held-out metrics, while deliberately declaring candidate_created=false and promotion_considered=false. No live model evaluation, production ledger mutation, candidate worktree, patch generation, promotion, service restart, or scheduler activation was performed during implementation. Nightly synthetic evaluation harvest (Completed) Added hiro/improvement/nightly_evaluation.py as a bounded public-development evaluation stage using the existing JsonlCaseProvider, EvaluationRunner, deterministic scorer, variant manifest, and append-only VariantLabLedger. Configured the default harvest to use rsi_cycle_v1.jsonl: eight public cases across identity, reasoning, instruction following, epistemics, and communication, with two repetitions per case and a sixteen-observation hard cap. Derived daily run and variant identifiers from the local date, suite identifier and version, and evaluated code hash. Re-entering the loop on the same day and code reuses a completed report without new model calls; interrupted reports resume only missing repetitions. Captured the most recent prior completed run for the same suite and version so a failure proposal can include pass-rate trend evidence rather than treating each night as an isolated event. Aggregated deterministic failures by category and weighted score impact. Only the highest-impact cluster becomes an ImprovementSpec, with failed case identifiers, top scorer reasons, the ledger run identifier, a falsifiable diagnostic hypothesis, expected benefit, and rollback language. Validated generated failure proposals against the cycle coordinator handoff contract. The diagnostic proposal declares that it does not yet authorize a code change; candidate scope is intentionally deferred until Hiro diagnoses the shared cause. Integrated the harvest as Phase 0 of self_improvement_v2. Run summaries, Markdown reports, console output, and Telegram briefings now expose the evaluation run, execution mode, observation count, failure count, and pass rate. Counted newly executed evaluation observations against the existing nightly model-call budget and limited combined conversation-derived plus evaluation-derived proposals to the configured maximum. Added configurable suite, ledger, case, observation, and enablement settings in both source defaults and the machine-local ignored runtime configuration. Added an in-process scheduler guard so the 22:00 two-minute polling window cannot overlap the self-improvement job or rerun it after a successful same-day completion. Failed launches remain eligible for a retry. Independent nightly API startup and Observatory results tab (Completed) Added scripts/nightly_api_preflight.ps1 with idempotent readiness checks against the real /benchmarks page on ports 8001 and 8765. It does not treat a disconnected model as an API startup failure, because the API can be ready while its /health endpoint correctly reports degraded model state. Added scripts/run_hiro_api.cmd as a blocking wrapper around the pinned .runtime/dashboard-env Python and main.py. The wrapper keeps Task Scheduler attached to the actual API lifetime even though the Windows virtual-environment launcher hands execution to a base Python process. Normalized the inherited Windows process Path before Start-Process and enabled UTF-8 Python output. This handles launch environments that expose both Path and PATH without discarding other runtime variables Hiro needs. Added scripts/register_nightly_hiro_task.ps1. The task runs daily at 21:55, wakes the computer, starts when a trigger was missed, permits battery execution, ignores overlapping instances, retries failure three times at five-minute intervals, and has a ten-hour execution window. Registered the task as Hiro Nightly API Preflight for the current interactive Windows identity. Its stored action points to the checked-in preflight with -KeepAlive, and its verified next trigger is 2026-07-20 21:55:00 America/Los_Angeles. Extended main.py to serve the Evaluation Observatory on 127.0.0.1:8765 in the same process as ports 8000 and 8001. The existing application-level startup lock ensures the scheduler and monitor background loops are started only once across the three Uvicorn server threads. Added GET /evaluations/api/nightly. It filters completed nightly runs, loads full append-only reports, summarizes failed observations, case IDs, and categories, and deterministically reconstructs the same coordinator-compatible improvement signal produced by the nightly harvest. Added a Nightly Observatory tab with latest-run metric cards, a diagnostic proposal panel, and clickable longitudinal run history. Empty-state text explains that the enabled 22:00 cycle will supply the first run and does not suggest a manual command. Restarted Hiro through the registered task path and left that task actively keeping the API online. Live checks returned HTTP 200 from the nightly endpoint on both 8001 and 8765. Used the in-app browser to open the real loopback Observatory, select the Nightly tab, inspect its rendered empty state, and visually verify the complete desktop layout. The tab was left available for the user. Nightly readiness monitoring and expansion criteria (Completed) Created an active daily Codex automation named Hiro nightly readiness review, scheduled for 07:30 America/Los_Angeles so the normal 22:00 run and its documented timeout envelope have time to finish. The review is read-only with respect to Hiro. It may inspect the Nightly Observatory data, append-only evaluation ledger, run summaries, and operational logs, but it may not alter code, configuration, services, schedules, cases, or recorded results. Each review classifies the latest night as a valid completed evaluation, infrastructure or model failure, incomplete run, or no run. Infrastructure failures are explicitly excluded from the successful-run count. Tracked evidence includes run identity, execution mode, duration when available, observation count, pass rate, failure categories, regression-probe status, API and model availability, errors, and whether Hiro's proposal is evidence-based and appropriately scoped. The monitor compares valid nights and recommends changes only after at least three clean completed runs, preferably five, with reliable completion, no recurring infrastructure failures, useful stable metrics, and sensible proposals. When expansion is justified, the policy prioritizes broader test diversity and adaptive focus on weak categories before adding repetitions, and may recommend a larger weekly deep run. It never applies recommendations without user approval. Decisions and reasoning Use two explicit E: destinations instead of merging unrelated directory layouts. LM Studio-managed models and Hiro's legacy files retain their existing organization and identifiers. Keep compatibility junctions even after updating LM Studio's direct download setting. Hiro contains pinned legacy paths, and junctions preserve behavior without coupling this operational migration to application-source edits. Copy and hash before activation rather than performing an unverified cross-volume move. Cross-volume failure must not leave Hiro with a partially populated live library. Keep C: backups until a real model loads and answers from E:. File equality proves copy integrity, while runtime validation proves the inference stack can consume the relocated model. Remove the large backups only after the real-load gate passes. The measured free-space result, not logical file totals, is the authoritative reclaimed-capacity value. Treat E: cold-load speed as a material limitation. The volume offers capacity, but the 3-minute-plus Gemma load means larger candidate startup times must be measured before operational adoption. Do not claim Hiro's launcher validation is sufficient when only the model-discovery endpoint responds. LM Studio can list installed models while none are loaded, so readiness must include loaded-state or completion evidence. Use direct uncached reads of the exact GGUF in addition to application load timing. This separates storage delivery from LM Studio initialization and Windows file-cache effects. Reject ambiguous application trials. A D:-routed load was not accepted until the preserved E: copy was outside LM Studio's scan tree and discovery returned exactly one Gemma candidate. Treat D: as the preferred hot-model volume based on the measured 2.45-times read advantage and 65.1% cold-load reduction. E: remains useful as higher-free-space bulk storage. Restore the permanent E: configuration after benchmarking because the request authorized measurement, not a second storage migration. Moving the active library to D: should be a separate intentional change. After receiving explicit authorization, move both the managed and legacy active libraries to D: rather than moving only Gemma. This makes performance consistent for every currently installed local model and consumes only about 51 GiB of D:'s available capacity. Keep separate D: directories for LM Studio-managed and legacy llama.cpp assets. Their layouts, identifiers, and consumers differ, and merging them would add operational risk without improving speed. Point the C: compatibility junctions directly to D: and retain E: junctions to D:. Direct C: links avoid an unnecessary hop, while E: links preserve scripts or tools that adopted the intermediate E: paths. Keep a small pre-D: LM Studio settings backup but remove the large E: model backups only after discovery, pinned-path checks, real loading, inference, unload, server shutdown, and final junction validation pass. Do not modify Hiro source paths during the storage move. Compatibility junctions preserve behavior while the model-path centralization work remains a separate, reviewable task. Use the base Qwen3.6-35B-A3B rather than an abliterated or instruction-altering derivative for the first trial. This keeps the evaluation anchored to the Qwen team's published model behavior. Select bartowski's Q4_K_M conversion because it is a standard, single-file, recommended default quant with a published SHA-256 and enough quality headroom while fitting the RTX 5090 with a practical context. Let LM Studio manage both the main GGUF and vision projector so discovery, multimodal pairing, identifiers, and future library maintenance remain native to the runtime. Download and verify now, but do not silently make the model Hiro's default. Resource estimation establishes feasibility; a controlled runtime and task-quality evaluation must precede routing changes. Evaluate Qwen3.6 at 8K rather than the earlier 4K estimate because 8K is the intended first operational context for Hiro and still fits the 32 GB GPU at one slot. Raise the suite ceiling from the historical 220 tokens to 1,024 for this checkpoint because separated hidden reasoning otherwise prevents many responses from reaching visible output. Record this as a comparability caveat rather than silently treating starved output as model quality. Do not compensate for the current reasoning configuration by making 4,096 output tokens Hiro's default. Representative tasks become answerable, but the latency and hidden-token overhead are excessive for routine compact responses. Keep Gemma 4 and Qwen 9B as the operational baselines until Qwen3.6 can run with a reliable reasoning-off chat template or runtime control and then pass the same compact-budget benchmark. Treat raw completion throughput and useful visible throughput as separate metrics for reasoning models. The measured 170.32 tokens per second is hardware throughput, not equivalent to 170 visible answer tokens per second. Restore the exact 127.0.0.1:8765 dashboard address as a standalone loopback service rather than changing Hiro's committed 8000/8001 application ports. Reconstruct the missing Python runtime under the already ignored .runtime directory. This restores execution without mixing generated environment files into the dirty source worktree. Use Hiro's existing FastAPI dashboard and evaluation-ledger API instead of creating a replacement static report, preserving all run, comparison, and lineage functionality. Leave the dashboard process running because the user requested an active tracking page; keep it independent from LM Studio so viewing results does not consume model VRAM. Use the existing Gemma 4 12B evaluation-isolation runs as the matched ledger baseline because they use the same suite and full Hiro agent path. The earlier Qwen 9B score remains useful broader context but is not represented by a directly compatible run in this ledger slice. Keep the infrastructure-only r1 record rather than deleting or rewriting append-only evidence. Exclude it explicitly from model comparison and use the completed r2 run as the valid candidate. Do not promote Qwen3.6 from this ledger result. It is materially worse on both compact-answer quality and latency under Hiro's current 512-token agent allowance, even though the small public suite does not make the score difference statistically clear. Treat reasoning control as the blocking experiment. A future rerun should keep the same frozen suite and ledger path while changing only a verified reasoning-off runtime or template, so any improvement is attributable. Treat self-improvement autonomy as an orchestration migration, not a transfer of unlimited authority. Hiro can own the repetitive research loop while protected evaluation assets and high-impact decisions remain independently governed. Retire or quarantine the legacy live-checkout mutation path before enabling autonomous candidate construction. Two competing pipelines with different safety and evidence semantics would make audit results ambiguous. Require candidate generation and execution to occur in disposable, resource-limited worktrees or equivalent sandboxes. Stable Hiro must supervise candidates; candidates must not supervise themselves or share writable evaluator state. Keep held-out cases, evaluator definitions, safety policy, permission boundaries, deployment credentials, and promotion policy outside candidate write scope. Automatic benchmark generation may add training and public-development cases, but it cannot manufacture its own promotion evidence. Use human approval as a permanent authority boundary for evaluator, safety, permissions, dependencies, schema migrations, deployment, and other high-blast-radius changes. A future automatic lane should be limited to predefined reversible scopes with proven rollback and health checks. Use a profile-aware budget instead of globally deleting completion limits. The stronger GPU makes Qwen's long reasoning affordable, but smaller models and intentionally compact tool summaries still benefit from bounded outputs. Set qwen35's 8K quality-first budget to 7,168 tokens after 4,096 passed 15/16 and an isolated 7,168-token retry fixed the remaining failure. This preserves a nominal prompt reserve while allowing the forced-reasoning GGUF to reach visible output. Treat the perfect r4 result as proof that output starvation, not core task capability, caused the earlier failure. Keep latency as a separate promotion dimension because the 36.77-second p95 is operationally significant. Do not silently change Hiro's active default profile as part of output tuning. The qwen35 profile is now supported and validated, while the final routing decision should explicitly balance perfect suite quality against cold-load and tail-latency costs. Transfer self-improvement responsibilities by task class, beginning with repetitive evidence handling and evaluation work rather than handing the complete cycle to Hiro at once. Make evaluation orchestration the first major autonomous responsibility after nightly triage because its outputs are structured and independently checkable. Limit each early cycle to one primary opportunity and one candidate so failures remain attributable and review packets stay compact. Keep ChatGPT as implementation fallback and reviewer during the first stages, then reduce its role after Hiro repeatedly produces complete experiment records and reproducible results. Use measured graduation criteria for each stage: complete artifacts, reproducible reruns, low duplicate or invalid proposal rates, zero worktree-scope violations, and successful rollback drills before authority expands. Treat automatic promotion as the final transition stage and initially restrict it to a small allowlist of reversible prompt, test, benchmark, or non-operational configuration changes. Use a distinct append-only ImprovementCycle record before candidate creation rather than fabricating a VariantExperiment with a nonexistent branch or worktree. Make cycle identity deterministic across the spec, suite contents, and baseline manifest so retrying the same work is idempotent and changing evidence creates a new cycle. Reject reuse of a baseline variant identifier when its immutable code, prompt, configuration, tool, model, or Git identity no longer matches; the operator must choose a new baseline identifier. Keep coordinator runtime packets outside version control under .hiro_runtime and write them atomically so an interrupted report write cannot leave a valid-looking partial packet. Stop coordinator v0 at baseline_ready. Candidate construction, candidate evaluation, promotion recommendation, merge, and deployment remain later transition stages. Summarize held-out evaluation outcomes in the evidence packet without copying held-out cases or assertions into the coordinator output. Use a synthetic public development suite nightly because it creates controlled, repeatable usage without consuming the held-out evidence needed to judge future candidates. Cap the first nightly harvest at eight cases and sixteen observations. This supplies broad evidence while reserving the remainder of the sixty-call self-improvement budget for conversation analysis, curiosity, journaling, and legacy probes. Use deterministic assertions rather than a model judge for nightly scoring. Hiro may generate the answers, but it does not get to reinterpret whether required formats, boundaries, tool restrictions, or content assertions passed. Create an improvement proposal only when deterministic failures exist. A perfect nightly run remains useful trend and stability evidence but does not create synthetic busywork. Generate a diagnostic, non-code-changing ImprovementSpec from the highest-impact cluster. The next transition stage can turn that diagnosis into allowed files and candidate tests after the shared cause is understood. Keep the existing lightweight probes temporarily as a separate compatibility signal. They are not treated as durable improvement evidence because their current integration still reports only a probe count. Run API preflight at 21:55 rather than replacing the 22:00 self-improvement scheduler. This separates process availability from evaluation orchestration and preserves the already-tested nightly path. Use the public /benchmarks page as readiness evidence. The existing /health endpoint includes model connectivity and may return 503 even when the API and scheduler are fully operational. Keep the API process attached to the scheduled task through the overnight window. A short-lived fire-and-forget launcher proved unreliable under Windows process-parent behavior. Serve the stable 8765 Observatory address from main.py instead of maintaining a second dashboard process. One process and one application startup lock avoid duplicate scheduler instances. Build the Nightly tab from the append-only evaluation ledger rather than scraping generated Markdown reports. Ledger data has explicit schemas, immutable run identity, exact case results, and durable historical comparisons. Keep the Nightly API and UI read-only. They expose public-development evidence and diagnostic proposals but do not add candidate creation, held-out access, promotion, or mutation controls. Use completed valid nights, not elapsed calendar nights, as the expansion gate. Model outages, API failures, interrupted cycles, and missing runs are operational evidence but do not establish evaluation stability. Require at least three valid completed nights and prefer five before changing workload size. This is long enough to expose basic scheduling, persistence, runtime, and proposal-quality problems without delaying small, reversible improvements indefinitely. Expand coverage before repetition count. New capability categories, multi-turn behavior, tool use, memory, and recovery scenarios provide more improvement information than repeatedly sampling the same deterministic prompts. Keep readiness review advisory. The monitor may recommend a bounded configuration or suite change, but code, schedule, evaluation, and authority changes still require explicit user approval. Validation and evidence Pre-migration process and port gate: passed — No relevant model process or inference listener was active before copy. LM Studio library copy: passed — Five files totaling 19.392 GiB matched by relative path, byte count, and SHA-256. Legacy llama.cpp library copy: passed — Thirteen files totaling 31.516 GiB matched by relative path, byte count, and SHA-256. Junction targets: passed — Both legacy C: paths resolve to their exact E: destinations. LM Studio setting: passed — downloadsFolder is E:\AI\Models\LMStudio and the rewritten settings file parses as valid JSON. LM Studio discovery: passed — Four language models and one embedding model were indexed from the relocated managed library. Hiro pinned paths: passed — The Qwen 9B and Gemma 4 model_registry paths resolve through the compatibility junction. Real Gemma load: passed — The 6.50 GiB Gemma model loaded from E: in 3 minutes 13.95 seconds with 4K context, maximum GPU offload, and one slot. Warmup completion: passed — The local OpenAI-compatible endpoint returned READY in 0.363 seconds from the relocated model. Unload and server restoration: passed — Gemma unloaded, the loaded-model list became empty, and the temporary API server stopped. C: space recovery: passed — Measured free space increased by 44.42 GiB, from 20.83 GiB to 65.25 GiB. Journal production build: passed — npm run build generated six dated journal pages, passed schema, sitemap, Atom, IndexNow, and noindex validation, compiled TypeScript, and completed the Vite production build. Benchmark file identity: passed — The E: source, verified D: copy, and fresh D: cold-load copy were each 6,975,877,728 bytes and matched SHA-256 after testing. Uncached sequential reads: passed — E: averaged 42.52 MiB/s across two trials; D: averaged 104.16 MiB/s across two trials, a 2.45-times advantage. Cold LM Studio loads: passed — E: loaded in 199.73 seconds and the fresh-file D: trial loaded in 69.67 seconds with identical 4K-context, full-GPU, one-slot settings. Warm-cache classification: passed — A subsequent D: reload completed in 5.25 seconds and was recorded separately rather than misreported as cold storage performance. Cross-drive inference parity: passed — Both drives returned READY for all three prompts; first-response latency was 0.253 seconds on E: and 0.252 seconds on D:, with warm calls near 0.11 seconds. Benchmark rollback and cleanup: passed — The original normal E: model directory and download setting were restored, two temporary D: directories totaling 12.994 GiB were removed, and LM Studio ended unloaded and stopped. Journal rebuild after benchmark update: passed — The updated entry regenerated six dated pages, passed schema, sitemap, Atom, IndexNow, and noindex validation, compiled TypeScript, and completed the Vite production build. D: migration preflight: passed — D: was healthy with 947.84 GiB free, destinations were absent, source directories were normal, and LM Studio was closed with no loaded model or running server. Complete library copy: passed — Five managed files totaling 19.392 GiB and thirteen legacy files totaling 31.516 GiB copied to D: without robocopy failures. Full migration hashes: passed — All eighteen files and 50.908 GiB matched by relative path, byte length, and SHA-256; mismatch count was zero. D: activation paths: passed — Both C: compatibility junctions point directly to D:, both former E: library paths junction to D:, and LM Studio's downloadsFolder is the direct D: managed-library path. Post-activation discovery: passed — LM Studio discovered four language models and one embedding model, with exactly one Gemma candidate; Hiro's pinned Qwen and Gemma files resolved at expected sizes. Post-activation Gemma runtime: passed — Gemma loaded from D: in 69.76 seconds with full GPU offload, 4K context, and one slot; all three fixed prompts returned READY. Final unload and cleanup: passed — Gemma unloaded, the server stopped, both E: backup directories were removed, E: reclaimed 50.91 GiB, and no temporary migration artifacts remained. Journal rebuild after D: activation: passed — The expanded entry regenerated six dated pages, passed schema, sitemap, Atom, IndexNow, and noindex validation, compiled TypeScript, and completed the Vite production build. Qwen acquisition preflight: passed — The direct D: LM Studio destination had 896.94 GiB free, no duplicate model was present, no model was loaded, and the API server was stopped. Qwen download finalization: passed — LM Studio completed the 23.18 GB package download and indexing with no remaining .part file; the main GGUF and F16 projector are present. Publisher hash verification: passed — The 22,285,080,192-byte Q4_K_M GGUF exactly matched the publisher's SHA-256 B46FEDD33E0BFB0CA...39064772. Qwen LM Studio discovery: passed — LM Studio identifies one 35B-A3B Q4_K_M qwen35moe model with vision, tool-use training, the paired projector, and a 262,144-token maximum context. Qwen resource estimate: passed — Estimate-only at 4K context, one slot, and full GPU offload reports 22.38 GiB GPU and total memory, within the 32 GB RTX 5090. Journal rebuild after Qwen acquisition: passed — The expanded entry regenerated six dated pages, passed schema, sitemap, Atom, IndexNow, and noindex validation, compiled TypeScript, and completed the Vite production build. Qwen 8K resource estimate: passed — At 8,192 context tokens, one slot, and full GPU offload, LM Studio estimated 22.51 GiB GPU and total memory. Qwen 8K real load: passed — The Q4_K_M model loaded in 399.48 seconds; LM Studio reported 21.59 GiB and observed total GPU use reached 29,996 of 32,607 MiB. Qwen core suite execution: passed — All 120 deterministic cases completed with zero API errors. The primary run averaged 78.37% and passed 94/120 cases at the 70% threshold. Reasoning budget diagnosis: passed — All 15 troubleshooting cases failed at 1,024 tokens while almost entirely consuming the budget as hidden reasoning; two representative retries reached correct visible answers at 4,096 tokens. Qwen function calling: passed — The model selected get_weather with location Seattle, Washington and returned finish_reason tool_calls in 1.21 seconds. Qwen near-8K retrieval: passed — A 7,623-token prompt and 7,817-token total request returned the exact final marker in 2.46 seconds. Qwen sustained generation: passed — The model generated 2,048 completion tokens in 12.02 seconds, or 170.32 tokens per second; 89.3% were classified as reasoning tokens and visible output was truncated. Qwen vision inference: passed — The model correctly identified a red circle, blue square, green triangle, three-shape count, and HIRO 8K text in 3.10 seconds. Qwen benchmark restoration: passed — The model unloaded, the API server stopped, temporary test fixtures were removed, and GPU use returned to 6,482 MiB. Journal rebuild after Qwen 8K evaluation: passed — The expanded entry regenerated the dated journal pages, passed content and noindex validation, compiled TypeScript, and completed the Vite production build. Dashboard route and ledger presence: passed — The /benchmarks route and static page remain in source, and the approximately 1.5 MB evaluation ledger remains present. Dashboard runtime reconstruction: passed — An ignored Python 3.12 environment was created under .runtime and Hiro's pinned requirements installed successfully. Loopback dashboard listener: passed — The existing FastAPI application is listening only on 127.0.0.1:8765 and serves /benchmarks with HTTP 200. Live Evaluation Observatory data: passed — The overview API returned 100 completed runs, 101 variants, and the latest completed run at a 1.0 weighted score. Dashboard restoration journal build: passed — The expanded entry regenerated the dated journal pages, passed content and noindex validation, compiled TypeScript, and completed the Vite production build. Qwen ledger configuration: passed — LM Studio confirmed Q4_K_M, 8,192 context tokens, one slot, maximum GPU offload, the exact benchmark identifier, and a 21.59 GiB model allocation. Ledger model-identity preflight: passed — The API response model exactly matched qwen36-35b-a3b-8k-ledger; the short probe also reproduced empty visible content with separated reasoning. Invalid-run separation: passed — The r1 database-permission failure was identified as pre-inference infrastructure evidence and excluded from model comparison; it remains preserved for auditability. Valid Qwen ledger run: passed — Run qwen36-35b-a3b-8k-public-20260720-r2 completed all 16 observations at 0.854167 weighted score, 0.375 pass rate, zero invariant failures, and 3,545.67 ms p95. Matched Gemma comparison: passed — Three compatible Gemma runs averaged 0.954861 weighted score, 0.729167 pass rate, and 379.91 ms p95; Qwen was 10.07 score points and 35.42 pass-rate points lower and 9.33 times slower. Evaluation Observatory ingestion: passed — The loopback dashboard returned HTTP 200, reported r2 as latest, and its compare endpoint reproduced the ledger result; the overview contained 130 completed runs and 103 variants. Post-ledger runtime cleanup: passed — Qwen unloaded, LM Studio's API stopped, temporary runners were removed, and the Evaluation Observatory remained online on port 8765. Journal rebuild after matched ledger evaluation: passed — The final expanded entry passed content and noindex validation, regenerated the dated journal pages, compiled TypeScript, and completed the Vite production build. Self-improvement autonomy source diagnosis: passed — Relevant orchestration, proposal, mutation, evaluation, isolation, ledger, promotion, rollback, scheduler, dashboard, documentation, and test surfaces were inspected without modifying or executing Hiro. Hiro implementation and evaluation tests for this diagnosis: not run — This session was explicitly read-only for Hiro. No pytest suite, model inference, evaluation run, candidate worktree, or improvement cycle was executed. Journal build after self-improvement autonomy diagnosis: passed — The updated entry generated six dated journal pages, passed schema, sitemap, Atom, IndexNow, and noindex validation, compiled TypeScript, and completed the Vite production build. Profile-aware budget unit tests: passed — Fourteen targeted tests passed, covering Qwen35 expansion to 7,168, explicit-short preservation, smaller and unknown model behavior, actual API payload propagation, and existing classifier behavior. Qwen35 live router preflight: passed — Hiro resolved the profile to qwen36-35b-a3b with a 4,096-token interim budget and returned visible READY in 1.41 seconds. Qwen35 4,096-token ledger run: passed — r3 completed all 16 observations at 0.979167 weighted score, 0.9375 pass rate, zero invariant failures, and 15,269.71 ms p95; only one observation failed. Remaining-case 7,168-token probe: passed — The exact r3 relation-class failure returned LABEL: GLIM through the full Hiro agent in 7.63 seconds with 7,168-token headroom. Qwen35 7,168-token ledger run: passed — r4 completed all 16 observations with 1.0 weighted score, 1.0 pass rate, every category at 1.0, zero invariant failures, and 36,770.73 ms p95. Qwen35 improvement comparison: passed — r4 improved 0.145833 in weighted score and 0.625 in pass rate over starved r2, with non-overlapping pass intervals and statistically_clear=true; it also exceeded the three-run Gemma mean by 0.045139. Quality-first latency characterization: passed — r4 measured 4,824.30 ms median, 9,825.35 ms mean, 36,770.73 ms p95, and 37,363.94 ms maximum latency; the p95 was 96.8 times the matched Gemma mean. Post-tuning runtime cleanup: passed — Qwen unloaded, LM Studio's API stopped, one-off runners were removed, and the loopback Evaluation Observatory remained online with r4 as latest. Journal rebuild after Qwen output tuning: passed — The final expanded entry passed content and noindex validation, regenerated the dated journal pages, compiled TypeScript, and completed the Vite production build. Incremental transition workflow design: passed — The workflow was mapped to Hiro's existing proposal-only v2 loop, evaluation kernel, variant laboratory, worktree isolation, append-only ledgers, proposal gate, and current human-review boundary. Hiro execution for transition workflow design: not run — This was a design-only session. No Hiro tests, evaluations, candidates, model calls, scheduler changes, or improvement cycles were executed. Journal build after transition workflow design: passed — The expanded entry generated seven journal pages and timestamped aliases, passed timestamp schema, same-day identity, sitemap, Atom, noindex, and IndexNow validation, compiled TypeScript, and completed the Vite production build. Cycle coordinator Python compilation: passed — py_compile completed for cycle_coordinator.py, lab_schema.py, lab_ledger.py, and test_cycle_coordinator.py. Cycle coordinator CLI surface: passed — python -m hiro.improvement.cycle_coordinator --help loaded successfully and documented spec selection, public and held-out suites, immutable baseline identity, ledger, output, and repository options. Initial focused pytest attempt: infrastructure failure — Collection succeeded and nine non-temp tests passed, but sixteen tests using tmp_path could not create pytest's default user-profile temporary directory. No Hiro assertion failure was reported in this attempt. Focused coordinator and evaluation tests: passed — With --basetemp redirected to .hiro_runtime/pytest-cycle-coordinator, 25 tests passed in 1.43 seconds across test_cycle_coordinator.py, test_variant_laboratory.py, test_variant_lab_cli.py, and test_evaluation_kernel.py. Journal build after cycle coordinator implementation: passed — The expanded entry generated seven journal pages and timestamped aliases, passed timestamp schema, same-day identity, sitemap, Atom, noindex, and IndexNow validation, compiled TypeScript, and completed the Vite production build. undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined undefined: unknown — undefined Live pre-night readiness check: partial — At 18:56 America/Los_Angeles, the scheduled preflight was Running with its next trigger at 21:55, the persisted 22:00 self_improvement_v2 schedule was enabled, and the API and Observatory returned HTTP 200. Overall health returned HTTP 503 and the local model endpoint did not respond, so the system was not yet ready for a valid quality run. Daily readiness-review automation: passed — The Codex app accepted and activated the Hiro nightly readiness review as a daily local-project automation at 07:30 America/Los_Angeles. An initial create request with an explicit UTC start anchor was rejected by automation validation; the successful definition uses a local wall-clock recurrence without that anchor. Current state LM Studio's direct download location and managed library are D:\AI\Models\LMStudio. Hiro's active legacy GGUF library is D:\AI\Models\llama.cpp. Both former C: model paths are direct compatibility junctions to D:, so existing Hiro configuration continues to resolve without source edits. Both former E: library paths remain as compatibility junctions to D: for tools that adopted the intermediate storage layout. C: has 65.25 GiB free after verified backup cleanup. No local model or LM Studio API server was left running. D: has 875.34 GiB free after receiving the active model estate and the Qwen3.6 package; E: has 3,391.19 GiB free after its verified backups were removed. D: is the measured performance winner for active models: 104.16 MiB/s uncached reads and a 69.67-second Gemma cold load, versus 42.52 MiB/s and 199.73 seconds on E:. Drive placement does not materially affect steady inference after the model is resident on the GPU. The launcher can confuse model discovery with loaded readiness when LM Studio's server lists installed models. A future readiness fix should require loaded-state or successful completion evidence. Qwen3.6-35B-A3B Q4_K_M and its F16 vision projector remain installed in LM Studio's D: library, hash verified, indexed, evaluated at 8K, and currently unloaded. The Qwen3.6 8K single-slot configuration fits the RTX 5090 but leaves limited VRAM headroom under the active Windows desktop workload: total observed use was 29,996 of 32,607 MiB. Qwen3.6 is not Hiro's default. Its current LM Studio chat template forces heavy hidden reasoning, producing a 78.37% primary core score and severe compact-output budget starvation despite fast raw generation. Qwen3.6 passed actual function calling, a 7,623-token context probe, and multimodal image understanding, so it remains a promising candidate once reasoning mode can be controlled reliably. The local Evaluation Observatory is available at http://127.0.0.1:8765/benchmarks and its loopback-only service is intentionally left running for benchmark tracking. The dashboard runtime lives under the ignored .runtime directory and does not change Hiro's configured 8000/8001 application ports or load an LLM. The valid Evaluation Observatory candidate is qwen36-35b-a3b-8k-public-20260720-r2: 0.854167 weighted score, 0.375 pass rate, zero invariant failures, and 3,545.67 ms p95 on emergence-level1-public@1.1.0. The matched three-run Gemma 4 12B mean remains substantially better at 0.954861 weighted score, 0.729167 pass rate, and 379.91 ms p95; Qwen3.6 remains unpromoted. The append-only ledger also contains an explicitly invalid r1 infrastructure run caused by restricted database permissions; it must not be interpreted as model quality. Hiro's scheduled self-improvement v2 loop is proposal-only: it can observe operational evidence and create structured improvement ideas, but it does not construct or promote candidates. The trusted evaluation and variant laboratory has the correct beginnings of an autonomy boundary, including external held-out cases, append-only evidence, worktree isolation, fingerprints, and proposal-only gates, but operator sequencing still supplies the orchestration now performed through ChatGPT-assisted development. The legacy improvement loop is not a safe shortcut to full autonomy because it mutates the live checkout before evaluation, lacks a rigorous isolated baseline comparison, and does not guarantee automatic restoration after a failed experiment. Hiro's router now includes a persistent qwen35 profile at qwen36-35b-a3b with a 7,168-token ordinary completion budget on both streaming and non-streaming paths. Gemma, Qwen 4B, Qwen 9B, explicitly short calls, and unknown models retain their previous output-budget behavior. The final r4 Qwen result is perfect on emergence-level1-public@1.1.0: 1.0 weighted score, 1.0 pass rate, every category at 1.0, and zero invariant failures. The quality-first Qwen profile has a substantial tail-latency cost: 9.83-second mean and 36.77-second p95 in r4, plus a measured 10-minute 18-second cold load in this session. The live dashboard exposes 132 completed evaluation runs across 105 variants and keeps qwen36-35b-a3b-8k-public-7168-20260720-r4 as its latest entry. The practical transition can begin without granting Hiro merge or deployment authority: its first responsibilities are producing a single ranked opportunity, a falsifiable experiment specification, and a reproducible evaluation packet. ChatGPT remains useful during the transition as a reviewer, difficult-change implementer, and diagnosis fallback rather than as the routine operator of every evaluation command. The desired end state is a Hiro-operated research loop with review-by-exception: routine cycles are collected, run, and summarized internally, while ambiguous, high-impact, or failing cycles are escalated. Cycle coordinator v0 now exists in Hiro but is not scheduled automatically. It is an explicit operator command that may invoke the configured model when given one accepted ImprovementSpec and matched evaluation suites. The coordinator's tested endpoint is baseline_ready: a durable cycle, resumable public and held-out runs, a baseline pin, and compact review artifacts, with no candidate or promotion action. The transition workflow is now preserved in the project documentation rather than only in conversation history. Hiro's persisted lab schedule is enabled, starts at 22:00 local time, and selects self_improvement_v2 in proposal_only mode. When the Hiro API process is running, its startup hook launches the lab scheduler and the next nightly job will execute the public rsi-cycle suite before conversation-derived analysis. The nightly public run writes full per-observation evidence to hiro/evals/evaluations.sqlite, while the self-improvement report includes a compact summary and any generated diagnostic proposal. No held-out evaluation, real model nightly run, candidate worktree, patch, promotion decision, or live runtime code change was executed during this implementation session. The older lightweight regression-probe phase still runs after the new harvest and still exposes only a count; it has not yet been migrated to the durable ledger. The Windows task Hiro Nightly API Preflight is registered for 21:55 local time and is currently Running after a successful manual end-to-end verification. One live Hiro process currently serves HTTPS on 8000, HTTP on 8001, and the loopback Evaluation Observatory on 8765. Both /evaluations/api/nightly endpoints return HTTP 200. The Observatory Nightly tab is live at http://127.0.0.1:8765/benchmarks and currently shows an intentional zero-run state pending the first 22:00 nightly evaluation. The task uses an Interactive logon principal. It can wake the computer and run while the user session is present, but it is not configured with stored credentials for operation after a full sign-out. API availability is now automatically handled before the nightly window; local model availability remains a separate dependency and is reported through evaluation failures or the existing model-health mechanisms. The active Hiro nightly readiness review runs daily at 07:30 America/Los_Angeles and reports whether the latest night was valid, operationally invalid, incomplete, or absent. No nightly evaluation has completed yet. The readiness counter therefore remains at zero valid completed nights, and no workload expansion is currently justified. The current nominal cycle targets 45 minutes with a 60-minute configured limit, but sequential evaluation and probe timeout blocks can allow a severely degraded run to extend to approximately 75 to 80 minutes. The local model endpoint was unavailable during the latest live check. Unless it is started before the cycle, tonight's data should be treated as infrastructure failure evidence rather than Hiro capability evidence. Next steps Use D: for new active model trials while reserving E: for archived models, datasets, and other capacity-oriented storage. Correct Hiro's Gemma launcher readiness check so an installed-but-unloaded model cannot satisfy the existing-server fast path. Record cold-load time, warm-load time, and steady-state inference separately for each model candidate. Keep the compatibility junctions until all Hiro model paths are intentionally centralized through configuration. Find a supported LM Studio runtime or model chat-template configuration that reliably disables Qwen3.6 reasoning for OpenAI-compatible requests, then repeat the 120-case suite at Hiro's historical 220-token ceiling. Update Hiro's benchmark reporting so visible answer tokens and hidden reasoning tokens are recorded separately for reasoning-capable checkpoints. If a reasoning-off rerun clears the compact-budget quality gate, add representative coding, repository-editing, and multi-turn tool tests before considering a routing change. Keep Qwen3.6 unloaded between experiments unless startup policy is intentionally changed; its measured D: load time was 6 minutes 39.48 seconds. If the Evaluation Observatory should survive reboots, add a reviewed launcher or scheduled startup entry for the loopback-only 8765 service instead of relying on an ad hoc process. Obtain a verified reasoning-off control for Qwen3.6, then rerun emergence-level1-public@1.1.0 through the same Evaluation Observatory path with the same 512-token agent ceiling and at least three repetitions. Add an explicit validity or exclusion annotation mechanism to the append-only ledger UI so infrastructure-only runs such as r1 remain auditable without appearing to be candidate model results. Use the Observatory for future model bake-offs whenever a frozen compatible suite and model-identity preflight are available; retain separate ad hoc probes for vision, tool calling, and long-context behavior until ledger suites cover them. Design one canonical self-improvement state machine that joins v2 observation and proposal generation to the isolated variant laboratory, and explicitly deprecate the legacy live-mutation path. Add a candidate-builder interface constrained by experiment-declared allowed paths, then run it inside the candidate worktree with fixed time, process, storage, model-call, and network budgets. Add an orchestrator that evaluates pinned stable and candidate manifests on the same public and external held-out suite versions, resumes interrupted runs, records infrastructure validity separately from model quality, and produces an independently verifiable evidence bundle. Before any automatic promotion lane, implement real transactional promotion and verified automatic rollback, post-deployment canaries, a kill switch outside Hiro's control, loop and budget limits, and durable alerts for stalled or repeatedly failing cycles. Measure evaluator reliability and contamination resistance over multiple generations before expanding authority. Start with Hiro generating proposals for review, then allow autonomous experiments, and only then consider automatic integration of a very small allowlisted class. Repeat the 7,168-token frozen-suite run at least twice more to measure stochastic stability and obtain a multi-run latency distribution before changing Hiro's default routing profile. Add visible-versus-reasoning token accounting to the Evaluation Observatory so output headroom and useful-answer efficiency can be compared directly across reasoning models. Evaluate a larger runtime context if routine Hiro prompts approach the nominal 1,024-token reserve left by the 7,168-token completion ceiling at 8K context. Decide whether qwen35 should be reserved for quality-critical requests or promoted more broadly after weighing its perfect suite score against 36.77-second p95 latency and long cold loads. Begin Stage 1 by standardizing the nightly output to one ranked opportunity plus a complete experiment specification and morning briefing; review ten cycles for usefulness, duplication, and evidence quality. In Stage 2, let Hiro pin or select a baseline, run matching public and held-out evaluations, resume interrupted runs, compare results, and produce the evidence packet without constructing a candidate. In Stage 3, connect approved experiment specifications to candidate worktree creation and allow Hiro to propose edits only inside declared paths, followed by syntax checks and targeted tests. In Stage 4, permit a bounded repair loop with a small attempt limit, then run the full stable-versus-candidate evaluation and prepare a promotion recommendation for review. After repeated successful reviewed promotions and rollback drills, define the first automatic-promotion allowlist and add post-promotion canary checks before expanding it. Track transition metrics separately from product-quality metrics: proposal acceptance rate, duplicate rate, invalid-run rate, human correction rate, time to evidence packet, scope violations, rerun reproducibility, and rollback success. Run coordinator v0 on a deliberately selected real ImprovementSpec and compatible public and external held-out suites when the configured local model is ready, using a new immutable baseline variant identifier. Review the first real evidence packet for completeness, useful failure classification, model and suite identity, and morning-briefing suitability before scheduling coordinator runs. Expose improvement-cycle status and evidence packet references in the read-only Evaluation Observatory after the first real coordinator cycle establishes the desired presentation. After repeated clean baseline packets, implement the approved-to-candidate transition that creates an external worktree and records a real VariantExperiment without adding merge authority. Allow the enabled 22:00 job to complete one real nightly public harvest and inspect its ledger report, runtime, pass-rate stability, and generated proposal quality. Add a morning analysis stage that reads the nightly report and enriches the top diagnostic signal into a falsifiable candidate specification with explicit allowed files and tests. Decide whether the legacy lightweight probes add distinct coverage; migrate useful cases into versioned suites and retire redundant count-only execution. Build suite rotation or staged expansion after several nights establish runtime and stability, while keeping a fixed anchor suite for longitudinal comparison. Add an operating-system wake or catch-up mechanism only if Hiro's API is not reliably running at 22:00; the current scheduler is process-resident and does not backfill a missed launch. Inspect the task state and first real Nightly tab entry after tonight's 22:00 evaluation, including whether the run was new, resumed, or reused. Confirm that the configured local model is available before 22:00 or add a separate bounded model preflight if unattended model startup is also desired. After the first run, verify that clicking its Nightly history row opens the expected per-case responses and deterministic scorer failures. Consider switching the Windows task to a non-interactive service principal only if overnight execution must continue after the user fully signs out. Add explicit startup-attempt and scheduler-launch events to the Observatory after several nights establish which operational details are useful rather than noisy. Let the morning readiness review classify and compare at least three valid completed runs, preferably five, before recommending any nightly workload change. When the valid-run gate is met, broaden the public suite with new capability categories before increasing repetitions, while retaining fixed anchor cases for longitudinal comparison. After nightly reliability is established, consider a separate weekly deep evaluation of approximately 100 to 200 observations rather than stretching every nightly run into a multi-hour job. Close the unattended local-model startup gap so the readiness monitor can distinguish real Hiro quality measurements from repeated connection failures.

RTX 5090 hardware review and local-model trial plan

2026-07-19T00:00:00Z

Executive summary Hiro's host was inventoried after its GPU upgrade. It now has an NVIDIA GeForce RTX 5090 with 32,607 MiB of VRAM, an Intel Core i7-10700K with 16 logical processors, 31.84 GiB of physical RAM, and several terabytes of free space on E:. The practical target is fully GPU-resident models with 20B to 35B total parameters. The GPU can hold high-quality 4-bit or 5-bit versions with a useful context cache, while 32 GiB of system RAM makes aggressive CPU offload of 70B-plus models unattractive. The first recommended trials are Qwen3.6-35B-A3B Q4_K_M, Gemma 4 31B Q4_K_M, and gpt-oss-20b in native MXFP4. Gemma 4 26B-A4B, Qwen3.5-27B, and Devstral Small 2 24B form a second wave for efficiency, dense-model comparison, and coding specialization. No model was downloaded or benchmarked. This session produced an evidence-based acquisition and evaluation plan and leaves Gemma 4 12B as Hiro's baseline until a candidate passes compatibility, quality, latency, memory, and reliability gates. Work completed Host hardware inventory (Completed) nvidia-smi identified an RTX 5090 with 32,607 MiB total VRAM, driver 591.86, CUDA 13.1 capability, compute capability 12.0, and a 575 W power limit. The Windows desktop used about 1,124 MiB of VRAM during inspection, leaving roughly 30.7 GiB before model allocation. The processor is an Intel Core i7-10700K at 3.80 GHz with 16 logical processors. The platform is an MSI MEG Z490 Trident X with an MEG Z490I UNIFY board. A Windows memory-status call reported 31.84 GiB physical RAM, 21.53 GiB available, and a 56.54 GiB total page-file commitment limit. Storage inspection found 24.0 GiB free on C:, 947.8 GiB on D:, and 3,391.2 GiB on E:. Future model files should live on E: rather than the current C: application-data path. Hiro inference stack and baseline (Completed) Hiro uses an OpenAI-compatible local endpoint through LM Studio and llama.cpp. Its approved launcher loads Gemma 4 12B QAT Q4 with maximum GPU offload, a 4,096-token context, and one slot. The registry contains Qwen 9B and Gemma 4 12B. Configuration retains older Qwen 4B fast and Qwen 9B standard tiers while selecting Gemma as the active profile. The existing 120-case bakeoff covers evidence obedience, no-evidence refusal, self-improvement specifications, structured JSON, tool routing, and troubleshooting. The June 21 historical report recorded 80.0% quality for Qwen 9B and 83.1% for Gemma 4 12B QAT, with no recorded timeouts or crashes. These values were reviewed, not rerun. Port 8080 was not running. The LM Studio CLI reported commit 0b2a176, while runtime enumeration timed out after trying to wake the service. Priority model shortlist (Completed) Priority 1 — Qwen3.6-35B-A3B: 35B total and 3B active parameters, 262K native context, multimodal input, agentic-coding improvements, and official llama.cpp support. Start with Q4_K_M, 8K context, one slot, and full GPU offload. Estimated weights are about 21 GiB before runtime and cache overhead. Priority 2 — Gemma 4 31B: a 30.7B dense model with up to 256K context. Q4_K_M should use about 18 to 19 GiB for weights and gives the most useful dense quality comparison with Qwen's sparse model. Priority 3 — gpt-oss-20b: 20.9B total and 3.6B active parameters, a 12.8 GiB native MXFP4 checkpoint, 128K context, adjustable reasoning effort, structured outputs, and strong tool-use training. OpenAI specifies a 16 GiB memory requirement. Priority 4 — Gemma 4 26B-A4B: 26B total with about 4B active. Try Q5_K_M if measured headroom permits, otherwise Q4_K_M. It is the efficiency-oriented Gemma alternative. Priority 5 — Qwen3.5-27B: a 27B dense model with 262K native context. It is a more established control for whether Qwen3.6's sparse architecture and newer runtime path improve Hiro. Q5_K_M should be practical. Priority 6 — Devstral Small 2 24B Instruct: a coding and software-engineering specialist with tools and vision. Mistral positions it for one RTX 4090 or a 32 GiB Mac. Evaluate it for Hiro's code and self-improvement workload, not as an automatic general default. Runtime fit and compatibility (Completed) Total parameters determine resident weight memory for MoE models; active parameters mainly affect compute. A 35B-A3B model still needs VRAM and disk for roughly 35B quantized weights. Reserve 3 to 4 GiB of VRAM for Windows, CUDA allocations, and cache. Q4_K_M is the safe first quant for 31B to 35B models; Q5_K_M is a finalist experiment after measuring actual headroom. Do not begin at advertised 128K to 262K contexts. Establish 8K stability, then measure 16K and 32K because cache growth, prompt processing, and conversation reuse can dominate. Current llama.cpp supports the listed families, but each needs a pinned-runtime check. Open upstream Qwen hybrid-cache and Gemma cache-memory reports make long-conversation testing mandatory. Models needing roughly 60 to 80 GiB for quantized checkpoints, including gpt-oss-120b and similar 100B-plus MoE releases, are out of scope. The 32 GiB system RAM is not a reliable high-performance overflow tier. Benchmark and promotion plan (Completed) Use a staged funnel: load and identity check, deterministic warmup, a balanced six-category smoke subset, the complete 120-case bakeoff, then multi-turn and real tool-call probes. Score by category, not only one aggregate. Evidence obedience, unsupported-claim refusal, valid JSON, and correct tool routing are promotion-critical. Record startup time, first-token latency, generation and prompt throughput, peak VRAM, peak RAM, timeout and crash rates, and cancellation recovery. Test non-thinking behavior separately from reasoning mode. Task content stays constant while each model receives its native supported chat template and controls. Compare Q4 and Q5 only for finalists. A full quant matrix would consume storage and time before establishing whether behavior is competitive. Keep Gemma 4 12B as rollback until a candidate beats its historical 83.1% result or has a justified category tradeoff with no reliability regression. Journal indexing-policy alignment (Completed) Restored noindex, follow metadata to the journal index and generated pages to match current publication policy. Kept direct robots.txt access to /hiro/ and /llms.txt; the journal remains directly retrievable without search indexing. Did not add navigation or change Hiro application code, model configuration, credentials, or runtime services. Decisions and reasoning Try Qwen3.6-35B-A3B first because it combines the strongest current general, coding, multimodal, and agentic profile that fits as a fully resident 4-bit model. Use Gemma 4 31B as the dense quality control; only a dense peer reveals whether the MoE candidate gives up consistency on Hiro's evidence and formatting contracts. Keep gpt-oss-20b in the first wave because Hiro is a tool-using agent and its structured-output, reasoning-effort, and tool-use training may matter more than parameter count. Evaluate Devstral as a specialist. A coding win may qualify it for repository work without replacing Hiro's conversational model. Start large candidates at Q4_K_M and one slot to establish stability and VRAM reserve before maximizing quant fidelity. Move model storage to E: before downloads; C: cannot safely hold a 35B Q4 candidate plus temporary overhead. Do not pursue 70B-plus CPU offload; it would trade the 5090's advantage for pressure on 32 GiB RAM and an older eight-core CPU. Treat maximum context as a ceiling, not a deployment setting. Promote it only after cache, reuse, and multi-turn measurements. Pin and record the runtime for every bakeoff because family support and cache behavior change between builds. Do not change Hiro's default based on external benchmarks. Promotion requires Hiro's local suite and operational probes. Validation and evidence GPU inventory: passed — nvidia-smi reported the RTX 5090, 32,607 MiB VRAM, driver 591.86, CUDA 13.1 capability, and current use. CPU and platform: passed — Registry and environment data identified the i7-10700K, 16 logical processors, and MSI Z490 platform. Physical memory: passed — GlobalMemoryStatusEx returned 31.84 GiB total RAM and 21.53 GiB available. Storage inventory: passed — .NET drive inspection returned 24.0 GiB free on C:, 947.8 GiB on D:, and 3,391.2 GiB on E:. Hiro configuration review: passed — The launcher, settings, registry, and current 4K single-slot Gemma profile were inspected without reading or publishing secrets. Historical bakeoff review: passed — The existing 120-case report was read as historical evidence and not represented as a new run. Local model endpoint: not-running — http://127.0.0.1:8080/v1/models was unreachable during the final read-only check. LM Studio runtime enumeration: incomplete — The CLI reported commit 0b2a176, but runtime listing timed out while waking the service. New-model benchmark: not-run — No weights were downloaded, no server was started, and no new quality or speed claims were made. Journal production build: passed — npm run build generated five dated journal pages, passed the schema, sitemap, Atom, IndexNow, and noindex validation gates, compiled TypeScript, and completed the Vite production build. Current state Hiro remains configured for Gemma 4 12B QAT through its existing launcher when the model lifecycle is started. The local model endpoint was offline at the end of review. The RTX 5090 creates a practical fully resident 20B to 35B tier, substantially above the former 12 GiB-class constraint. System RAM, C: free space, runtime pinning, and long-context cache behavior are the main constraints. No candidate has earned promotion; memory figures for GGUF choices remain estimates until exact files are loaded and measured. Next steps Move or reconfigure the LM Studio and GGUF library to E: before downloading any 20B-plus candidate. Pin a runtime that explicitly loads Qwen3.6, Gemma 4, gpt-oss, and Devstral GGUFs on Blackwell. Acquire Qwen3.6-35B-A3B Q4_K_M first; record its hash and size, then run load, identity, warmup, and VRAM checks at 8K with one slot. Run a balanced smoke subset and the full 120-case bakeoff against unchanged Gemma 4 12B. Repeat the gates for Gemma 4 31B Q4_K_M and native gpt-oss-20b before deciding whether second-wave models are needed. For finalists, test 16K and 32K conversations, cache reuse, cancellation, tools, and JSON before trying Q5. Promote by role if evidence supports it: general assistant, reasoning/tool specialist, and coding specialist need not be one model.

Dashboard recovery and verified workday startup

2026-07-17T00:00:00Z

Executive summary Hiro's local dashboard was unreachable because no process was listening on its web ports. The browser's connection-refused message was accurate and was unrelated to the dashboard API key. The dashboard/API process was restarted independently, preserving the intentional shutdown of the Gemma model server and Telegram notifications. The first hidden-process restart exposed a Windows console-encoding issue: the Unicode startup banner could not be written through the inherited cp1252 output encoding, so the process exited before binding a port. A second restart forced UTF-8 for the child process and succeeded without changing application source. The HTTPS root dashboard and its authenticated evaluation data feed were then verified. No evaluation, behavior, resolver, model, scoring, policy, or production-service change was made. At the user request, the approved Gemma launcher subsequently started the exact Gemma 4 12B QAT model with maximum GPU offload, a 4096-token context, and one inference slot. Startup took 20.599 seconds and the launcher warmup took 0.260 seconds. The first Telegram start exposed the same cp1252 background-output issue. Telegram was restarted with a UTF-8 child environment and reached its polling state successfully. A final local-only model request returned READY in 0.217 seconds. The dashboard, model, and Telegram bot were all running, while the active evaluator count remained zero. The phone web app was prompting for an API key because its private dashboard and chat requests correctly received HTTP 401 after browser-session storage was cleared. Access logs confirmed that the phone was connecting directly through the private Tailscale network, while unrelated public internet scanners were also reaching the protected Funnel endpoint. Disabling authentication globally would therefore have exposed private and control routes. Hiro now trusts only direct loopback and Tailscale address ranges. Those clients no longer need the API key, while public Funnel and other internet clients continue to require it. The focused operational-boundary suite passed 15 tests and the complete repository suite passed 89 tests. Live checks returned 200 for no-key loopback and tailnet requests and 401 for a no-key public Funnel-style request. The local benchmark dashboard was separately checked after the phone-authentication change. Its page and every read-only evaluation API it uses now load without an API key from trusted loopback access. A follow-up search-indexing audit found no current technical block: the dated HTML, full JSON, robots file, and sitemap all returned HTTP 200 with correct content types; robots allowed the journal, the sitemap listed the canonical date URL, and the page declared index, follow. Repository history showed that the July 14 page was initially published with noindex and became eligible for indexing only on July 15 at 4:24 PM Pacific. The search engine had therefore had roughly two days, not the full time since original publication, to reconsider it. Public search returned no results for the entire bballstatistics.com domain, not merely the Hiro page. The journal is also absent from site navigation, leaving the sitemap and machine-readable discovery document as its primary crawl signals. Official Google guidance says recrawling can take from a few days to a few weeks and does not guarantee inclusion. No indexing directive or website behavior was changed during this diagnostic pass. The bballstatistics.com domain property was verified in Google Search Console and the canonical July 14 Hiro URL was inspected directly. Google initially reported the URL as unknown, with no crawl, referring page, or referring sitemap. A priority indexing request was submitted and Googlebot Smartphone fetched the page successfully at 2:19 PM Pacific. After the fetch, Google changed the state to Crawled - currently not indexed. A live URL test then reported that the URL is available to Google and that the page can be indexed. The Hiro journal index was also added to the priority crawl queue to provide a referring page. The sitemap submission was accepted, but Search Console currently reports that the sitemap could not be read; that unresolved Google-side fetch state remains a follow-up. Public retrieval and discovery were strengthened without adding Hiro to the NBA site's navigation. The generator now publishes a standards-based Atom feed containing the full update text and advertises that feed from every journal HTML page and from llms.txt. The sitemap now carries an accurate date-only last-modified value for the journal index, latest alias, and every dated canonical page. A build-time validation gate checks the complete JSON schema, sitemap dates, Atom coverage and content, and IndexNow ownership file after every journal generation. The deployed production validator completed 66 retrievals with zero failures across browser, curl, generic-bot, and ChatGPT-style user agents. The HTML, JSON, robots file, discovery document, sitemap, Atom feed, diagnostics, and ownership file all returned HTTP 200 with the expected content types and no user-agent-dependent bodies. IndexNow received the journal index, latest alias, and all four canonical dated URLs. The first response was HTTP 202 while ownership-key validation was pending; a retry after the key was live returned HTTP 200, confirming receipt. This notification does not guarantee crawl selection or indexing. At the user's explicit request, the earlier unlisted-homepage choice was reversed. The public NBA statistics homepage now contains a clear, visible, crawlable HTML link to the Hiro journal. Work completed Connection-refused diagnosis (Completed) Confirmed that the standard dashboard and API ports had no active listeners. Confirmed that the dashboard is served by Hiro's FastAPI process rather than by the model server. Inspected the prior dashboard runtime record and logs, which showed that the previous process had served evaluation overview requests successfully before stopping. Dashboard-only recovery (Completed) Started only Hiro's API/dashboard process in a hidden background session. Left the Gemma endpoint on port 8080 stopped. Left Telegram notifications disabled and the Telegram bot stopped. Preserved the intentional-shutdown marker used by Hiro's lifecycle monitoring. Windows background encoding diagnosis (Completed) The first restart attempt failed before application startup because the Unicode startup banner was encoded through a cp1252 output stream. The retry explicitly selected UTF-8 for Python input/output and startup execution. The UTF-8 retry reached application startup and brought up both the HTTPS dashboard endpoint and the companion HTTP API endpoint. Dashboard and evaluation-feed validation (Completed) Verified that the canonical local dashboard root returned a successful HTML response over HTTPS. Verified the evaluation overview through the normal API-key-protected path without exposing the key. The evaluation overview reported 100 completed runs and retained proposal-only promotion mode. Workday Gemma startup (Completed) Ran the approved lifecycle launcher after the user explicitly requested that Hiro be available through Telegram during the workday. Verified the exact served model identifier, 4096-token context, one inference slot, and maximum GPU offload from startup telemetry. The launcher measured 20.599-second startup latency and 0.260-second warmup latency with a READY response. A separate final readiness request returned READY in 0.217 seconds. Telegram startup recovery (Completed operationally) The lifecycle marker was initially enabled while the bot process had exited because its Unicode startup banner inherited cp1252 output encoding. Restarted the Telegram lifecycle with UTF-8 forced in the child environment. Verified that Telegram notifications were enabled, the managed bot process was alive, and the Telegram application had entered its started polling state. Evaluation isolation (Completed) Inspected running Python command lines after startup. Confirmed that no evaluation, regression-lite, emergence, or ordered-binding process was active. No new evaluation run or promotion decision was created. Phone web app authentication boundary (Completed and live) Diagnosed repeated no-key 401 responses from the phone chat, lab-status, and dashboard data requests. Confirmed that the phone was a direct authenticated Tailscale client rather than a public Funnel client. Added a narrow trusted-client classifier for loopback, Tailscale IPv4 CGNAT, and Tailscale IPv6 address ranges. Kept API-key enforcement enabled for public and ordinary LAN addresses; the existing browser prompt remains available for untrusted clients. Added an explicit configuration toggle that can disable tailnet trust and restore key requirements everywhere. Restarted only the web API/dashboard; the model and Telegram services stayed online. Benchmark dashboard trusted-access verification (Completed; no additional code change required) Confirmed that the benchmark page itself returned HTTP 200 over the local HTTP listener. Confirmed that overview, run detail, comparison, and lineage requests each returned HTTP 200 without an API key from loopback. Inspected the benchmark client logic and confirmed it prompts only after an HTTP 401; the remaining API-key button is for explicitly authenticating an untrusted public client. Determined that the observed prompt came from a browser page session loaded before the trusted-client fix took effect. Search indexing delay diagnosis (Completed; no active block found) Verified clean HTTP 200 responses and correct MIME types for the canonical HTML, full JSON, robots file, and sitemap. Verified an index, follow page directive, an exact canonical URL, explicit robots permission, and inclusion of the dated URL in the sitemap. Confirmed that exact-title, exact-URL, and site-restricted public searches still returned no result. Reviewed publication history and identified the original noindex launch followed by the indexable-policy change on July 15. Identified weak discovery signals: the domain has no visible indexed footprint and the unlisted journal has no normal site-navigation link. Reviewed official Google, Bing Webmaster Tools, and IndexNow guidance for crawl requests, sitemap submission, URL inspection, and push notification. Google Search Console inspection and crawl requests (Priority crawl submitted; indexing pending) Completed domain-property ownership verification without publishing account details. Inspected the exact canonical July 14 URL and captured Google initial state: unknown URL, no last crawl, no referring sitemap, and no referring page. Requested indexing once; Search Console confirmed that the URL was added to a priority crawl queue. Confirmed a subsequent successful Googlebot Smartphone fetch with crawling allowed. Ran the live URL test; Google reported that the URL is available and the page can be indexed. Requested indexing once for the Hiro journal index so its dated-entry links can become a referring-page signal. Submitted the full sitemap. Google acknowledged the submission, then displayed an unresolved could-not-fetch/could-not-read status with zero discovered pages. Public retrieval and discovery hardening (Deployed and validated) Added a public Atom feed at /hiro/feed.xml with one entry per journal date and full plain-text content covering summary, workstreams, decisions, validation, current state, and next steps. Advertised the Atom feed through an HTML alternate link, llms.txt, and the public retrieval diagnostics document. Added date-only last-modified elements to every sitemap URL, using the newest journal date for the index and latest alias and each entry date for canonical dated pages. Added a root-hosted IndexNow ownership file and a reusable submission command that reads the canonical URL set from the generated sitemap. Added a deterministic generated-artifact validator to the normal journal generation and production build pipeline. Extended the live retrieval validator to cover the feed, sitemap dates, ownership file, and full Atom content under four common user agents. Homepage discovery link (Deployed and validated) Added a semantic navigation block directly to the static homepage document. Added the plain anchor text Hiro development updates with href /hiro outside the React application root. Applied minimal visible styling while keeping the link functional with JavaScript disabled. Published the change through the existing Vercel deployment without changing the Hiro page URLs, schema, or retrieval behavior. Decisions and reasoning Recover the dashboard independently instead of restarting the full Hiro lifecycle, because the user needed read-only visibility while model and Telegram shutdown state should remain intact. Treat the first restart failure as an operational environment issue, not as evidence of a dashboard application defect. Use an explicit UTF-8 child-process environment for this recovery rather than altering source during an operational incident. Direct users to the root dashboard URL; the evaluation data lives behind authenticated API routes and is loaded by the root user interface. Make no evaluation or behavioral-improvement claims from this session because no evaluation run was performed. Start the full model and Telegram lifecycle only after the user explicitly requested workday access. Treat the Telegram marker/process mismatch as a failed startup until the managed process and its application-start log are both verified. Use a local-only READY request as the final model check; do not run evaluations merely to prove conversational availability. Do not remove API authentication globally because the public Funnel endpoint receives unsolicited internet traffic. Use the direct socket client address as the trust signal; do not accept an arbitrary browser-supplied identity header. Trust only loopback and documented Tailscale ranges, not generic private LAN ranges. Leave the front-end prompt logic unchanged so untrusted clients can still authenticate normally after a 401 response. Do not create a benchmark-specific authentication exception because the shared trusted-client boundary already covers every benchmark API and preserves one consistent security policy. Do not misclassify the current delay as a robots, status-code, content-type, canonical, or page-level indexing defect. Measure indexing eligibility from the July 15 removal of noindex, not from the original July 14 publication time. Treat search indexing and OpenAI safe-open retrieval as separate systems; indexing may help discovery but cannot guarantee that a Voice retrieval environment will accept the domain. Do not make another speculative metadata change without evidence from Google Search Console or Bing URL Inspection. Use Search Console evidence instead of inferring crawl state from public search results. Submit each priority URL only once because Google states that repeat submissions do not change queue position or priority. Do not change page metadata after the live test confirmed current index eligibility. Treat sitemap acceptance and sitemap processing as separate states; do not claim the sitemap passed while Google reports that it could not be read. Keep Hiro out of the site's ordinary navigation because the journal remains intentionally unlisted; strengthen machine discovery through standards-based public artifacts instead. Use date-only sitemap last-modified values because the journal source has reliable publication dates but not durable per-update timestamps. Treat an IndexNow HTTP 200 as confirmation that the URL set was received, not as evidence that any participating engine crawled or indexed it. Do not alter scoring, model behavior, Hiro runtime services, credentials, or security policy as part of website retrieval work. Treat the user's explicit homepage-link request as authorization to supersede the earlier unlisted-navigation decision. Place the link in the server-delivered index.html rather than only in React so crawlers and retrieval clients can discover it without executing JavaScript. Use one direct relative URL to the stable /hiro index and avoid tracking parameters, redirects, or client-side click handlers. Validation and evidence Initial listener inspection: passed — No dashboard listener was present, matching the browser's ERR_CONNECTION_REFUSED result. First hidden restart: failed — The process exited before binding because the Unicode startup banner could not be encoded with cp1252. UTF-8 restart: passed — Both FastAPI application instances completed startup; HTTPS port 8000 and HTTP port 8001 were served. Dashboard root: passed — GET https://127.0.0.1:8000/ returned HTTP 200 with text/html content. Authentication boundary: passed — The protected evaluation route rejected an unauthenticated request with HTTP 401. Authenticated evaluation overview: passed — The read-only overview returned HTTP 200, reported 100 completed runs, and reported proposal_only promotion mode. Model isolation: passed — The local model endpoint remained unavailable after dashboard recovery; Gemma was not restarted. Telegram isolation: passed — Telegram notifications remained disabled and the Telegram bot remained stopped. Source and evaluation changes: passed — No Hiro source, evaluator, scoring, held-out data, policy, grounding, credential, certificate, or environment configuration was changed. Gemma launcher: passed — The exact gemma-4-12b-it-qat-q4_0 model started with 20.599-second startup latency and a 0.260-second READY warmup. Initial Telegram start: failed — The managed bot exited during its Unicode startup banner because the hidden child inherited cp1252 output encoding. UTF-8 Telegram recovery: passed — The managed Telegram bot stayed alive, notifications were enabled, and the application log reported that polling had started. Final local model readiness: passed — A local-only chat-completions request returned READY from gemma-4-12b-it-qat-q4_0 in 0.217 seconds. Evaluator process audit: passed — Zero evaluator processes were active after the workday startup. Phone request-path diagnosis: passed — Local access logs showed the phone requests arriving directly from the authenticated tailnet and receiving 401 before the change. Focused operational-boundary suite: passed — 15 tests passed, covering trusted ranges, rejected public and ordinary LAN ranges, no-key tailnet access, key enforcement, and the disable switch. Complete repository suite: passed — 89 tests passed in 9.46 seconds. Live loopback no-key access: passed — The protected lab-status endpoint returned HTTP 200 without an API key from loopback. Live tailnet no-key access: passed — The protected lab-status endpoint returned HTTP 200 without an API key over the direct Tailscale address. Live public boundary: passed — A no-key Funnel-style request carrying a public client address returned HTTP 401. Service continuity: passed — The Gemma model endpoint remained HTTP 200 and the Telegram bot remained running after the web API restart. Benchmark page no-key load: passed — GET http://127.0.0.1:8001/benchmarks returned HTTP 200. Benchmark overview no-key load: passed — The evaluation overview endpoint returned HTTP 200 from loopback without an API key. Benchmark detail no-key load: passed — A real evaluation run detail endpoint returned HTTP 200 from loopback without an API key. Benchmark comparison no-key load: passed — A real candidate-versus-baseline comparison returned HTTP 200 from loopback without an API key. Benchmark lineage no-key load: passed — The evaluation lineage endpoint returned HTTP 200 from loopback without an API key. Canonical HTML indexing audit: passed — The July 14 HTML returned HTTP 200, text/html, index and follow metadata, and the exact www canonical URL. Machine-readable endpoint audit: passed — The full JSON endpoint returned HTTP 200 with application/json content. Robots audit: passed — robots.txt returned HTTP 200, allowed the Hiro path, and referenced the sitemap. Sitemap audit: passed — sitemap.xml returned HTTP 200 with application/xml content and included the canonical July 14 URL. Public search discovery: failed — Exact-title, exact-URL, and site-restricted searches returned no result; no page from the domain was visibly indexed. Indexing-policy history: diagnostic — The page launched with noindex and changed to index, follow on July 15 at 4:24 PM Pacific. Search Console ownership: passed — The bballstatistics.com domain property was ownership-verified. Initial canonical URL inspection: diagnostic — Google reported the July 14 URL as unknown, with no crawl, sitemap, or referring page. July 14 priority crawl request: passed — Search Console confirmed that the canonical dated URL was added to the priority crawl queue. Googlebot fetch: passed — Googlebot Smartphone crawled the page at approximately 2:19 PM Pacific; crawl was allowed and page fetch was successful. Post-crawl index state: pending — Google changed the URL state to Crawled - currently not indexed. Live URL eligibility test: passed — Search Console reported URL is available to Google and Page can be indexed. Journal index priority request: passed — The canonical /hiro journal index was added to the priority crawl queue. Sitemap submission: partial — Google accepted the sitemap submission but currently reports Sitemap could not be read and zero discovered pages. Generated retrieval artifact gate: passed — The build-time validator passed for four journal entries, full latest JSON content, every sitemap last-modified value, full Atom feed coverage, and the public IndexNow ownership file. Full website production build: passed — The TypeScript and Vite production build completed after the new generation and validation steps. Live multi-user-agent retrieval suite: passed — 66 deployed requests completed with zero failures across browser, curl, generic-bot, and ChatGPT-style user agents; bodies were identical per endpoint and all expected status and content-type checks passed. Live Atom and IndexNow ownership endpoints: passed — The deployed Atom feed returned HTTP 200 with application/xml, and the root ownership file returned HTTP 200 with text/plain. IndexNow canonical URL notification: passed — After an initial HTTP 202 pending key validation, the retry returned HTTP 200 for the journal index, latest alias, and four canonical dated pages. Homepage-link production build: passed — The complete TypeScript/Vite production build and generated Hiro artifact gate passed with the new static homepage navigation link. Live raw homepage HTML: passed — The deployed homepage returned HTTP 200 text/html and contained the exact anchor Hiro development updates outside the React root. JavaScript-independent discovery: passed — The Hiro link is present in the initial HTML response and does not require hydration, cookies, authentication, or client-side JavaScript. Current state The local dashboard is available at https://127.0.0.1:8000/. The dashboard process is running independently and its authenticated evaluation feed is healthy. The dashboard requires the configured Hiro API key for private evaluation data; the root interface stores the supplied key only for the browser session. Gemma 4 12B QAT is loaded locally and ready on port 8080. Telegram notifications are enabled and the managed Telegram bot is running for workday access. No evaluator is running. Evaluation history remains read-only through the dashboard and promotion mode remains proposal-only. The dashboard and Telegram hidden Windows startup paths remain sensitive to output encoding unless UTF-8 is explicitly selected. Direct Tailscale and loopback web-app clients no longer receive the API-key prompt. Public Funnel and other untrusted clients still require the configured API key. The tailnet trust behavior is configurable and can be disabled without removing authentication. The local benchmark dashboard and all of its read-only evaluation APIs no longer require an API key from loopback or direct Tailscale access. The journal has no current technical indexing prohibition, but public search still shows no indexed result for the domain. The strongest next evidence must come from Google Search Console URL Inspection and Bing Webmaster Tools rather than repeated unauthenticated search queries. Google now knows and has successfully crawled the canonical July 14 page, but has not yet selected it for the index. Both the dated page and the Hiro journal index are in Google priority crawl queues. The live page passes Google current index-eligibility test. Google sitemap processing remains unresolved and should be rechecked after the new property finishes initial processing. A full-content Atom feed is live at https://www.bballstatistics.com/hiro/feed.xml and is advertised from journal HTML and llms.txt. The live sitemap includes last-modified dates for the index, latest alias, and each canonical dated page. Future deployments can rerun deterministic generated-artifact checks and notify IndexNow with one repository command. IndexNow has confirmed receipt of the current canonical journal URL set, while Google indexing remains separately pending in Search Console. The public www.bballstatistics.com homepage now links directly to /hiro with the visible label Hiro development updates. The homepage supplies an ordinary internal referring link for crawlers in addition to the sitemap, Atom feed, llms.txt, and IndexNow notifications. Next steps Recheck the canonical July 14 URL in Search Console after Google processes the priority crawl; do not resubmit it repeatedly. Recheck the sitemap status after property initialization. If it still cannot be read, inspect Google reported fetch details before changing the valid public XML. Monitor public search only after Search Console changes the page from Crawled - currently not indexed to an indexed state. Close and reopen any benchmark tab that was loaded before the trusted-client change so its stale prompt state is discarded. Have the phone reopen or refresh the Hiro web app while Tailscale is connected and confirm the session no longer presents the API-key prompt. Persist UTF-8 mode in the managed Telegram launcher and add a focused regression test so future lifecycle starts cannot recreate the marker/process mismatch. Add or standardize a dashboard-only launcher that always selects UTF-8, records its process identity, and verifies the root and authenticated overview endpoints. Make dashboard lifecycle status distinct from model and Telegram lifecycle status so an intentional model shutdown cannot obscure dashboard availability. Keep model and Telegram startup under explicit user control; do not infer that dashboard recovery authorizes inference or notification services. Continue to require proposal-only evaluation gates before any behavioral promotion. Add the domain to Bing Webmaster Tools when an authenticated owner session is available, then submit and inspect the sitemap there. Recheck Google Search Console sitemap processing and the July 14 URL after the new sitemap deployment; do not repeat the existing priority request. Run the production retrieval validator and IndexNow notification command after future journal updates. Recheck Search Console discovery after Google recrawls the homepage and sitemap; do not repeat the existing priority indexing request.

Controlled Hiro reliability and emergence lab

2026-07-16T00:00:00Z

Executive summary The launcher now uses the supported pinned LM Studio CLI, verifies Gemma 4 12B model identity and readiness, warms the model, records telemetry, and enables Telegram only after success. Complete local validation passed all 20 response probes, all 10 tool/resolver probes, a 1.396-second real Hiro agent turn, 0.144-second cancellation recovery, and all 76 repository tests. No paid or external LLM API was used. The smoke report is PARTIAL only because the Hiro HTTP/HTTPS application endpoints were not running. A separate non-fatal log-rotation file contention warning remains a follow-up. Three consecutive unchanged public and repository-external held-out evaluation pairs were completed after the launcher repair. Every repetition produced 87.5 percent public and 87.5 percent held-out pass rates, zero invariant failures, and sub-second p95 latency, demonstrating that the previous timeout cascade is resolved under the current machine state. All misses were deterministic resolver false positives rather than model-server failures: the public word-sort case was routed to Gmail and the held-out uncertainty case was routed to weather. No response change was created or promoted. Two narrowly scoped resolver intent-boundary corrections eliminated the only repeatable failures from the stable three-run baseline. The matched candidate evaluation passed all 16 public and all 16 repository-external held-out observations with a 1.0 weighted score, zero invariant failures, and no latency regression. A 20/80 program was designed to move Hiro from infrastructure stabilization into controlled testing for transferable and emergent capabilities. Twenty percent of each laboratory budget is reserved for eliminating measurement threats; eighty percent is reserved for an escalating capability ladder using novel, independently scored tasks. Level 1 emergence testing began with three unchanged rule-induction and transfer baseline pairs. The new suites are deliberately harder than the operational smoke suite: mean public pass rate was 66.7 percent and mean repository-external held-out pass rate was 52.1 percent, with zero invariant failures and stable sub-second p95 latency. The results show selective transfer rather than general mastery. Hiro was consistently strong on relational classification, analogy, explicit exception transfer, and distractor resistance, but consistently weak on multi-step numeric composition, position-based token extraction, and a harder held-out symbol transformation. Ambiguity calibration varied across repetitions. Level 1 measurement was extended prospectively without rewriting version 1.0. Version 1.1 broadened calibration expression coverage, a structurally independent repository-external held-out family tested surface transfer, and a nine-case diagnostic isolated the composition bottleneck. The diagnostic evidence rules out basic arithmetic, intermediate-state retention, single-operation inference, explicit natural-language composition, and output formatting as primary causes. Hiro failed both abstract operation-order controls and the latent two-step case, supporting a specific hypothesis that the weakness lies in binding inferred or named operations into the correct ordered sequence. The Level 1.1 stability requirement was completed with two additional unchanged matched pairs. All three repetitions were quality-identical: 75 percent public, 62.5 percent held-out, calibration 1.0, zero invariant failures, public weighted score 0.9583, and held-out weighted score 0.9375. A narrowly scoped ordered-operation-binding prompt candidate was implemented and tested against three stable pre-candidate baselines and three matched candidate repetitions. It was rejected and removed because it produced no public gain, no latent-binding gain, and only one non-repeatable held-out improvement. The negative result is informative: a generic instruction to bind operations, preserve order, compute intermediates, and verify them does not change Gemma behavior reliably. The limitation requires a different mechanism than adding a high-level system-prompt reminder. A materially different two-stage infer-then-execute candidate was tested. It consistently raised the counterbalanced public ordered-binding suite from 57.14 to 71.43 percent, but held-out gains were mixed and the broader Level 1.1 suite regressed from 75 to 62.5 percent. The candidate was rejected and removed under the no-regression gate. A separate two-hour lab ran in C:\tmp\hiro-two-hour-lab-20260716-2120 on codex/two-hour-lab-20260716-2120, based on codex/rsi-first-cycle. The dirty main workspace and its Telegram lifecycle changes were inspected but not edited. Sixteen local reliability commits were retained: Windows-safe shared log rotation, a single-writer lease, resumable run IDs, exact-model readiness preflight, bounded timeout and transport recovery, immutable case fingerprints, in-flight lease heartbeats, preflight-before-model lease ordering, explicit READY sentinels, invalid-work rejection before preflight, evaluation-memory isolation, internal-session filtering, and read-only recovery status. Three behavior mechanisms remained proposal-only and were removed. A typed internal ledger regressed public quality and latency; broad dual solving helped named operations but hurt latent chains and latency; a narrowed named-operation consensus path failed to improve a fresh external family. No response behavior was promoted. After reliability hardening, three public Level 1.1 confirmations were identical at 75 percent pass, 0.9583 weighted score, zero invariant failures, and 509-915 ms p95. Three frozen external named-consensus confirmations were identical at 50 percent, 0.9167, zero invariants, and 548-811 ms p95. A non-promotable direct-local prompt diagnostic showed that Gemma retained 75 percent public performance while reaching 75 percent on the already-observed external family at roughly 309-333 ms p95. Prompt ablation then exposed cross-case contamination: normal evaluations were persisting synthetic conversations and injecting recent evaluation cases into later prompts. Evaluation turns now read no personal or episodic memory, persist no conversations, and extract no facts. Internal evaluation, simulation, lab, and benchmark sessions are filtered from real-user cross-session context. A live run added zero conversation rows; 2,898 historical evaluation rows remain stored but are now excluded from retrieval and were not deleted. The final isolated repository suite passed 114 tests. Five forced-timeout soak cycles recovered in 184-201 ms and every next control passed in 181-334 ms. A hard-killed run resumed to 2/2, and a competing evaluator was rejected before preflight in 601 ms with no run record. Work completed Launcher and readiness gate (Completed) Registered the existing 6.5 GiB GGUF with LM Studio by hard link, avoiding a duplicate copy. Pinned the local CLI; configured localhost port 8080, maximum GPU offload, 4096-token context, and one inference slot. Verified the served model, performed a warmup, recorded PID/model/timestamps/latencies, and gated Telegram startup on readiness. Complete Gemma reliability validation (Completed) Cold startup took 7.437 seconds and warmup 0.210 seconds; the final lifecycle rerun took 7.366 and 0.146 seconds. The smoke benchmark passed 30 automated model and tool checks with zero timeouts/errors and 0.71-second average latency. The full Hiro agent path completed in 1.396 seconds. A cancelled long request released the single slot and the first follow-up succeeded after 0.144 seconds. The complete Python suite passed 76 tests in 5.18 seconds; final focused lifecycle coverage passed 10 tests in 0.69 seconds. Shutdown verification (Completed) Extended shutdown for LM Studio while preserving Telegram-first ordering. Final state: Telegram stopped and disabled, no evaluator, no loaded model, port 8080 closed, dashboard untouched. GPU use fell from approximately 8.65 GB loaded to approximately 1.15 GB after unload. Three-run evaluation baseline (Completed) Each repetition ran 16 public observations followed by 16 repository-external held-out observations through one serial ledger writer. Both suites and their assertions remained unchanged. Public pass rate was 87.5 percent in all three runs with weighted score 0.975, zero invariant failures, and p95 latencies of 931.6, 943.6, and 973.7 milliseconds. Held-out pass rate was 87.5 percent in all three runs with weighted score 0.9792, zero invariant failures, and p95 latencies of 546.2, 565.1, and 568.1 milliseconds. The code, configuration, prompt, toolset, and model hashes matched across all three repetitions. Repetition-specific labels were required because the CLI includes a fresh created_at timestamp in the otherwise immutable manifest. Public run IDs: 727e269d-1308-4af4-8a1f-2b8f0153e83e, b3afb742-32ff-4a50-bb52-4c4d591cce00, f38a080f-1319-4c19-99cb-c9aa578e8ee6. Held-out run IDs: e75fb330-3347-49e8-855d-72052ffed442, 15f4541e-9b8b-4744-9a12-746b81ca162c, efcb7883-887a-4a31-959e-9eadfc7ab641. Narrow resolver intent-boundary corrections (Completed) Gmail order routing now requires commerce or shipping context instead of treating every bare ORDER token as an email-search request. Normal requests such as where is my order, find the latest order email, and track my package remain supported. Weather detection now uses complete-word matching for strong weather terms. Ambiguous adjectives such as hot, cold, warm, and cool require an outdoor, time, or measurement cue, preventing fragments inside unrelated words from triggering weather. Added public and synthetic regression tests for the observed formatting-label failure, non-weather substring collisions, and preserved positive commerce/weather routes. Allowed implementation scope was limited to core/gmail_resolver.py, core/weather_resolver.py, and tests/test_resolver_intent_boundaries.py. Evaluator code, assertions, public cases, held-out data, safety policy, grounding, credentials, environment files, and CI were unchanged. Twenty-percent reliability track (Planned) Time budget: one unit of reliability work for every four units of capability testing. Reliability work may exceed that ratio only when a failed gate makes capability measurements invalid. R1 fixes non-fatal log rollover contention so evaluation output remains readable and attributable. Exit gate: concurrent process ownership cannot flood stderr or lose evaluation records. R2 fixes immutable-manifest repetition semantics so identical code, config, prompt, toolset, and model hashes can share one variant identity while individual run timestamps remain run metadata. Exit gate: three repetitions under one immutable manifest. R3 adds explicit single-writer leasing and stale-lease recovery. Exit gate: a second evaluator is rejected, an interrupted owner can be recovered, and the ledger remains append-only. R4 validates resumability by interrupting a run between observations and completing it without duplicate case records. R5 adds preflight readiness and throughput gates. Exit gate: wrong model, occupied slot, insufficient latency headroom, and unhealthy endpoint all prevent measurement before the first case. Reliability work cannot change evaluation scoring, assertions, held-out content, safety policy, grounding requirements, credentials, environment files, or CI. Eighty-percent emergence-testing ladder (Planned) Level 1, rule induction and transfer: infer a latent rule from examples, explain it minimally, and apply it to structurally different unseen cases. Measure transfer accuracy, calibration, and sensitivity to irrelevant surface features. Level 2, compositional generalization: combine two familiar skills in novel arrangements without a dedicated resolver. Use counterbalanced tasks to distinguish composition from memorized templates. Level 3, self-diagnosis and recovery: inject incomplete evidence, misleading intermediate results, and recoverable tool failures. Score detection, revised plan quality, final correctness, and whether Hiro avoids repeating the failed approach. Level 4, multi-tool agency: solve bounded objectives requiring tool selection, sequencing, evidence reconciliation, and stopping. Include decoy tools and conflicting sources; require grounded final answers and full traces. Level 5, cross-session learning: provide a lesson in one session and test transfer later with new wording and a different domain. Measure retention, appropriate retrieval, non-interference, and resistance to false-memory prompts. Level 6, hypothesis formation: present ambiguous observations and require multiple competing hypotheses, discriminating tests, evidence updates, and calibrated conclusions. Reward information gain rather than confident guessing. Level 7, bounded self-improvement: Hiro may propose a narrowly scoped change and predicted effects, but an external evaluator runs untouched public and held-out tests. Changes remain proposal-only until independent gates pass. Every level includes public development cases, repository-external held-out cases, adversarial variants, and negative controls where the learned behavior should not activate. Program cadence and evidence gates (Planned) Use two-week cycles or equivalent work units: approximately one day/unit on reliability and four on emergence experiments. Unused reliability budget returns to emergence testing; it does not accumulate into speculative refactoring. Each experiment starts with a written capability hypothesis, causal mechanism, allowed-file scope, predicted public and held-out effects, invariant list, latency budget, and falsification condition. Use at least three unchanged baseline repetitions before candidate comparison. Candidate evidence requires matched public and external held-out runs, zero invariant regressions, no material latency regression, and improvement across task families rather than one prompt. Advancement requires success on two independently generated held-out task sets and at least one delayed transfer test. A perfect score on a small suite is treated as suite mastery, not emergence. Promotion remains proposal-only. No experiment may edit its evaluator, assertions, held-out vault, grounding requirements, or safety constraints. Stop immediately for timeout cascades, slot contamination, ledger ambiguity, safety or grounding regression, held-out leakage, unexplained cross-run variance, or improvement confined to development cases. Maintain a failure taxonomy separating model reasoning, resolver routing, tool execution, memory retrieval, orchestration, infrastructure, and evaluator defects. Only capability-level failures count toward emergence conclusions. Primary program measures: unseen-family transfer, compositional success, recovery rate, calibration, evidence grounding, cross-session retention, negative-transfer rate, intervention efficiency, latency p95, and invariant failures. Level 1 rule-induction and transfer baselines (Completed) Created eight versioned public cases and eight separately stored repository-external held-out cases. Families cover latent-rule induction, relational classification, symbolic transfer, two-step composition, irrelevant-feature resistance, exception transfer, analogy, and underdetermination. Each case ran twice per baseline. Both suites validated without changing the evaluation kernel, scoring implementation, existing suites, safety policy, grounding rules, credentials, environment files, or CI. Focused evaluation tests passed 21 of 21 and the complete repository suite passed 80 of 80. Baseline 1: public 62.5 percent, weighted 0.9375, p95 931.4 ms; held-out 43.75 percent, weighted 0.90625, p95 888.7 ms. Baseline 2: public 75 percent, weighted 0.9583, p95 993.4 ms; held-out 62.5 percent, weighted 0.9375, p95 876.4 ms. Baseline 3: public 62.5 percent, weighted 0.9375, p95 904.3 ms; held-out 50 percent, weighted 0.9167, p95 861.6 ms. Public run IDs: 06e5e9a5-3088-4146-8f1c-68d286493653, bd203b2a-c064-4195-bebb-2f742b3fd3c9, bfee14d5-c556-4e2e-8b7b-c4e6bddf50c7. Held-out run IDs: b2950708-df59-4554-ac24-d3e3020b5453, 91064225-20bc-464a-a6da-2f5cfcfc85dd, 01768885-ab68-492c-a7c7-e83f3fb859f1. Stable successes across all six observations per family included public and held-out relational classification, analogy transfer, and exception transfer. Public symbolic shifting and distractor resistance were also stable. Stable failures included both public and held-out two-step composition, public position extraction, held-out token selection, and held-out symbolic shifting. The failed composition and symbol responses varied, indicating unstable inferred mechanisms rather than a fixed routing error. Calibration scoring has known undercoverage: semantically appropriate responses such as indeterminate, undefined, or does not have a unique mapping did not always match the narrow accepted-expression regex. Existing results were preserved and not rescored after inspection. Level 1.1, independent transfer, and composition diagnosis (Completed) Preserved all version 1.0 suites and ledger results. Added public and external held-out version 1.1 files with only prospective calibration-language expansion. Created a second repository-external held-out family using different surface structures: length parity, token rotation, vowel counting, letter composition, shape distractors, case-preserving exceptions, access analogies, and underdetermination. Created nine public composition diagnostics with two repetitions each, isolating single-operation inference, intermediate output, arithmetic execution, explicit composition, operation order, formatting, and the original latent composition. All four suite files validated. Evaluation-kernel tests passed 21 of 21 and the complete repository suite passed 80 of 80. The v1.1 matched diagnostic scored 75 percent public and 62.5 percent held-out with calibration 1.0 on both, zero invariant failures, and p95 latency 885.9/903.4 ms. Public run ef4ec653-e490-436b-8817-002dfa434ddf; held-out run dc616b8a-b43c-4a12-a776-4baff0d5036a. The independent held-out family scored 37.5 percent, weighted 0.875, zero invariant failures, and p95 868.8 ms. Run dbe81239-cab8-4f93-b48c-ba76c9865a77. Rotation, distractor resistance, and exception transfer passed; length parity, vowel counting, and letter composition failed. The independent analogy case was contaminated by Gmail routing because the prompt contained ticket; it is classified as resolver overreach rather than relational reasoning evidence. The calibration answer Not determined uniquely was semantically correct but still outside the prospective regex, showing that phrase-list scoring remains brittle. The composition diagnostic scored 66.7 percent pass rate, weighted 0.9444, zero invariant failures, and p95 1097.3 ms. Run d4c60fd6-73e6-4927-b841-09882866f924. Single-operation inference, intermediate state, arithmetic, explicit natural-language composition, and formatting each passed 100 percent. Both operation-order controls and the latent composition case passed 0 percent. This localizes the dominant failure to ordered operation binding rather than the component skills. Three unchanged Level 1.1 baselines (Completed) The earlier v1.1 diagnostic pair was retained as baseline repetition 1 because it used the same unchanged public and external held-out suite versions, model, prompt, code, configuration, and toolset surfaces. Repetition 1: public run ef4ec653-e490-436b-8817-002dfa434ddf scored 75 percent with p95 885.9 ms; held-out run dc616b8a-b43c-4a12-a776-4baff0d5036a scored 62.5 percent with p95 903.4 ms. Repetition 2: public run 76013adc-3999-4dc2-8888-db1574b2f8b5 scored 75 percent with p95 1026.1 ms; held-out run 2bb4639d-ed63-4050-931d-1ee1780f2f3f scored 62.5 percent with p95 891.9 ms. Repetition 3: public run e59c34e3-05c2-4f5a-a84a-59adbc579c2d scored 75 percent with p95 921.5 ms; held-out run b7d38fd0-59e9-4002-8047-a95de45709ab scored 62.5 percent with p95 926.5 ms. Every category score was identical across all three repetitions. Calibration, distractor resistance, exception transfer, and relational transfer were 1.0 on both suites. Public structural transfer was 1.0; held-out structural transfer was 0.8333. Composition was 0.8333 and rule induction 0.9167 on both. All 96 observations across the three public/held-out pairs completed with zero invariant failures. No candidate or behavior change was introduced between repetitions. Rejected ordered-operation-binding candidate (Rejected and removed) Hypothesis: Hiro has the component skills but loses the binding between operation identity, sequence position, and intermediate state. A generic internal procedure might improve ordered composition without teaching evaluation answers. Allowed behavior scope was only core/prompt_builder.py. The temporary candidate added one generic sentence instructing Hiro to bind each operation to its stated step, apply steps in order to the preceding intermediate, verify intermediates, and return the requested format. A bounded regression test asserted the instruction contained no evaluation terminology or suite identifiers. Before implementation, created seven counterbalanced public cases and seven third-family external held-out cases, each repeated twice. They cover latent numeric/letter binding, both orders of noncommutative string and arithmetic operations, and single-step controls. Both suites validated; evaluation tests passed 21 of 21 and the full suite 80 of 80. Three pre-candidate baselines were quality-identical. Public pass rate 57.14 percent, weighted 0.9286; held-out pass rate 28.57 percent, weighted 0.8810; zero invariant failures. Single-step controls were 1.0 while latent binding was 0.8333. Baseline public runs: b61d9bac-8e18-4c64-b3db-f0923d49ee18, 681cd064-8cb8-41bb-938d-0a888c469a06, 8fe97d0a-ab4c-4767-b603-17d0f145903f. Baseline held-out runs: 4fc1331e-1692-40aa-b437-127553a42e7b, 548d8465-2f80-4aba-b56a-c1c9f9a1cb90, f9fbd4f7-2232-43c3-917a-90c78598f74a. With the candidate, focused tests passed 23 of 23 and the full suite 81 of 81. Candidate run 1 left public unchanged at 57.14 percent and increased held-out to 42.86 percent. Runs 2 and 3 exactly matched baseline at 57.14/28.57 percent. Latent binding remained unchanged in every run and invariant failures remained zero. Candidate public runs: 25920070-a3fb-4c92-a7ec-5714a91209e0, ed8ae109-534b-44e4-bc42-0bf6670dccf8, 8a249710-3b1d-4010-bb9a-841054b19d13. Candidate held-out runs: faeeaa1f-01bb-47c1-a7df-c28ada240627, f9bbb539-a11f-4709-b232-df34aac00d83, 0db56bfe-c3bb-46e6-a0de-6ec3b090113d. The candidate failed its falsification gate because improvement was neither repeatable nor present in the target latent-binding category. The prompt change and temporary test were removed. The restored full suite passed 80 of 80. No evaluator, assertion, held-out case, resolver, safety policy, grounding requirement, credential, environment file, or CI configuration was changed by the candidate. The reusable public counterbalanced suite was retained and committed locally. Rejected two-stage ordered-operation candidate (Rejected and removed) Hypothesis: separating operation inference from execution into two local Gemma calls would preserve order and intermediate state better than a prose reminder. The first call produced a compact internal plan without the answer; the second executed that plan against the original request. The generic activation scope required either multiple arrow demonstrations or two defined named operations plus an ordered application request. Focused tests passed 13 of 13 and the candidate full suite passed 87 of 87 before live evaluation. Three public qualification runs were identical at 71.43 percent, weighted score 0.9524, zero invariant failures, and 100 percent single-step controls, improving the 57.14 percent baseline. Public run IDs: b7122ca0-4832-4ca2-81bd-539b0fcd9e74, 56cfcb2d-d43a-4ac3-ba9e-1108324dd656, 3d67c51e-5248-423f-a060-909534a0c4cd. Because the public gate passed, three unchanged repository-external held-out runs were executed. Pass rates were 28.57, 42.86, and 42.86 percent versus three 28.57 percent baselines; weighted scores were 0.8810, 0.9048, and 0.9048; all had zero invariant failures and 100 percent controls. Held-out run IDs: e7a69f8b-fe4b-4f0f-9be8-ce70bee17627, eaf7ab66-b4fb-429a-be64-bc1d0020a498, 0264cb5c-c9cf-4e82-a808-18bdc21c5f52. Broader regression gates were decisive. Core RSI remained perfect at 100 percent with zero invariants (dc10bc6d-969b-471e-8fbb-9044aabc2343), but Level 1.1 fell from the stable 75 percent baseline to 62.5 percent with p95 latency 2590.5 ms (e6f9f4e4-e426-4f05-bbd8-f1453e94a41b). The broad arrow trigger intercepted ordinary single-rule induction examples, demonstrating negative transfer. The candidate was removed rather than tuned after held-out observation. The restored complete suite passed 80 of 80. No response behavior was promoted. Gemma startup identified the intended model at PID 2624, with 7.521-second startup and 0.167-second warmup. Concurrent log rollover repeatedly encountered a non-fatal Windows file-lock error; evaluation completion and ledger records remained intact. Evaluation reliability kernel hardening (Completed locally) Shared one rotating handler per log file and deferred Windows rollover retries instead of emitting repeated PermissionError tracebacks. Added an atomic SQLite single-writer lease with process liveness, stale/dead recovery, in-flight heartbeats, process-bound owner identity, and acquisition before readiness traffic. Added planned run IDs, resume, immutable SHA-256 case fingerprints, incomplete-run diagnostics, and exact skip semantics for already-recorded repetitions. Added exact configured-model checks, model-list verification, explicit READY sentinel warmup, latency ceiling, local-standard-only routing, and a no-paid-tier evaluation guard. Added bounded recovery probes after case deadlines and local HTTP transport failures. Recovery failure leaves an incomplete resumable run and always releases its owned lease. Behavior candidates and no-regression gates (Rejected; code removed) Typed internal ledger candidate public runs were 25, 35, and 35 percent versus 40-50 percent baselines, with 3.4-6.1 second p95 latency. It failed the public gate, so the external suite was not run. Broad dual solving reached 50 percent on all three public named-operation runs and 55-60 percent external, but latent-chain behavior worsened and p95 latency reached 6.1-9.6 seconds. The broad mechanism was removed. The narrowed named-operation-only mechanism held public at 50 percent but remained exactly 50 percent on all three fresh external-family runs. It failed the generalization gate and was removed. Candidate changes never touched evaluator scoring, assertions, held-out content, safety policy, grounding, credentials, environment files, or CI. No promotion decision was recorded as accepted. Evaluation memory isolation (Completed locally) Diagnosed that evaluation sessions were saved into the normal conversations table and then selected by cross-session episodic retrieval, creating order-dependent leakage and contaminating real-user context. Evaluation and other internal session types now suppress message persistence and fact extraction through a context-local guard. Evaluation prompts also skip personal memory, learned facts, history, and episodic context. User-facing cross-session queries exclude eval:, sim_, lab_, and benchmark_ session prefixes, so the 2,898 historical evaluation rows are no longer retrieved. Existing data was not deleted. Three isolated public runs scored 75, 68.75, and 75 percent; three isolated external runs scored 68.75, 62.5, and 62.5 percent. All had zero invariants and sub-505 ms p95, confirming cleaner but still sampling-variable capability measurement. Live failure and recovery drills (Passed) Forced-timeout run timeout-recovery-local-only-20260716-2231 recovered in 0.140 seconds; the next READY control passed in 0.683 seconds. Five later soak runs recovered in 0.184-0.201 seconds with every next control passing. Cross-process contention rejected blocked-contention-live-20260716-2340 in 601 ms before run_started or readiness traffic, then returned to zero lease rows. Hard interruption left resume-live-soak-20260716-2345 started with zero results and a dead-owner lease. A 0.435-second slot probe recovered, resume completed both cases with zero invariants, and status returned no writer or incomplete run. The wrong-model preflight validation created neither run evidence nor a lease. A separate live interruption previously resumed from one result to exactly 20 without duplication. Prompt-load capability diagnostic (Diagnostic only; not promotable) Three direct minimal-prompt runs were identical at 75 percent public and 75 percent on the already-observed external family, weighted score 0.9583, zero invariants, and approximately 309-333 ms p95. Hiro base-persona direct prompting held the external family at 75 percent at temperature zero; the full memory/tool prompt fell to 62.5 percent. Matched 0.7-temperature runs showed the same direction. String-order failures remained under every prompt ablation, demonstrating a real model limitation. Full context added a formatting miss, and cross-case episodic contamination added an additional list-order miss. Because these task families had already been observed and the direct path omitted real Hiro orchestration, the diagnostic is mechanism evidence only. It does not justify response promotion. Decisions and reasoning Use LM Studio CLI as the supported Windows runtime entry point; the bundled llama-server is a backend shim. Keep controlled evaluation single-slot and serial. Treat HTTP/HTTPS failures as missing application-server coverage, not Gemma failures. Do not promote response behavior from this runtime test alone. Accept these runs as a stable runtime baseline, not as authorization to promote response behavior. Investigate the two resolver false positives with narrowly scoped hypotheses before proposing any behavior change. Keep the candidate as a local proposal supported by matched evidence; do not auto-promote or deploy it. Adopt the 20/80 allocation as a guardrail: reliability work validates measurements, while the majority of effort targets transferable capability rather than benchmark-specific patches. Treat suite mastery and resolver correctness as prerequisites, not evidence of emergence. Accept the three runs as an informative Level 1 baseline, but do not treat aggregate pass rate alone as the emergence measure; family-level transfer and variance are the primary evidence. Do not create a behavior candidate until calibration undercoverage is addressed prospectively in a new suite version and the stable composition failures have a falsifiable mechanism hypothesis. Adopt ordered operation binding as the leading composition-failure hypothesis: when multiple inferred or abstractly named transformations must be bound and sequenced, Hiro loses or substitutes the intended operations. Do not use the independent held-out aggregate as a pure reasoning score because one case was resolver-contaminated and another exposed assertion undercoverage; preserve the run and use uncontaminated family evidence. Accept Level 1.1 as a stable matched baseline. The exact repetition agreement satisfies the planned stability gate for forming - but not yet promoting - an ordered-operation-binding candidate. Reject generic system-prompt reminders as the ordered-binding intervention. They do not produce stable transfer and should not be promoted. Retain the ordered-binding hypothesis but revise the mechanism: future work should test structured intermediate representations or an explicit bounded reasoning scaffold rather than another prose reminder. Reject the two-stage candidate because a stable public gain and partial held-out gain do not outweigh a measured Level 1.1 regression. Do not tune this tested mechanism against already-observed held-out results. Retain reliability and evaluation-isolation changes locally for review; do not auto-promote, merge, push, deploy, or change production services. Reject all three response mechanisms because public regression, latency, latent-chain weakness, or failure on a fresh external family violated the gates. Treat the minimal-prompt result as evidence that prompt and memory relevance matter, not as a production-ready fast path. Do not delete the 2,898 historical evaluation conversation rows automatically. Exclude them from retrieval now and require an explicit backup-and-cleanup decision later. Keep production user-channel persistence behavior unchanged while suppressing internal evaluation, simulation, lab, and benchmark state. Validation and evidence Startup and warmup: passed — Verified gemma-4-12b-it-qat-q4_0; startup 7.437 seconds, warmup 0.210 seconds. Gemma smoke benchmark: partial — 30 passed, zero model timeouts/errors, 0.71-second average. Two app-channel connections failed because the app server was off; two manual checks skipped. Hiro agent and cancellation: passed — Agent turn 1.396 seconds; first recovery probe succeeded 0.144 seconds after cancellation. Python suites: passed — 76 complete-suite tests passed; 10 final focused tests passed. Final process state: passed — Telegram disabled, evaluator absent, model unloaded, port 8080 closed, dashboard untouched. Three consecutive evaluation pairs: passed — All three produced identical 87.5 percent public and held-out pass rates with zero invariant failures and stable sub-second p95 latency. Manifest equivalence: passed — Code, config, prompt, toolset, and model hashes were identical across the three repetition labels. Evaluation shutdown: passed — Telegram disabled, model unloaded, port 8080 closed, no evaluator process, dashboard untouched. Focused resolver and integration tests: passed — 21 tests passed in 0.80 seconds. Complete Python suite after resolver changes: passed — 80 tests passed in 5.01 seconds. Matched public candidate evaluation: passed — 16 of 16 observations passed, weighted score 1.0, zero invariant failures, p95 latency 924.1 milliseconds; run 2f5d56b1-53d3-420a-8557-01ca346cd4fa. Matched held-out candidate evaluation: passed — 16 of 16 observations passed, weighted score 1.0, zero invariant failures, p95 latency 545.3 milliseconds; run c4073d44-62b7-400f-9892-908e7f647b95. Post-evaluation lifecycle: passed — Telegram disabled, Gemma unloaded, port 8080 closed, no evaluator process, dashboard untouched. Level 1 suite validation: passed — Eight public and eight external held-out cases validated at version 1.0.0. Level 1 three-run baseline: passed — All six component runs completed with zero invariant failures and sub-second p95 latency; public mean pass rate 66.7 percent, held-out mean 52.1 percent. Final lifecycle state: passed — Gemma unloaded, Telegram disabled, port 8080 closed, no evaluation writer, dashboard PID 41900 remains running. Level 1.1 matched diagnostic: passed — Public 75 percent and held-out 62.5 percent; calibration 1.0 on both; zero invariants. Independent held-out transfer: diagnostic — 37.5 percent overall with stable genuine failures plus one resolver-contaminated analogy and one semantically correct calibration false negative. Composition mechanism isolation: passed — Component skills and explicit composition 100 percent; operation-order controls and latent composition 0 percent, supporting the ordered-binding hypothesis. Final lifecycle state: passed — Gemma unloaded, Telegram disabled, port 8080 closed, no evaluator process, dashboard PID 41900 running. Three-run Level 1.1 stability: passed — All three pairs were quality-identical at 75 percent public and 62.5 percent held-out, with calibration 1.0 and zero invariant failures. Post-baseline process state: passed — Gemma unloaded, Telegram disabled, port 8080 closed, no evaluator process, dashboard PID 41900 running. Ordered-binding pre-candidate stability: passed — Three identical baselines: public 57.14 percent, held-out 28.57 percent, zero invariants. Ordered-binding candidate replication: failed — Public unchanged in all runs; held-out improved once but returned to baseline twice; latent binding never improved. Rejected-candidate cleanup: passed — Candidate prompt and temporary test removed; complete suite restored to 80 of 80 passing. Final process state: passed — Gemma unloaded, Telegram disabled, port 8080 closed, no evaluator process, dashboard PID 41900 running. Two-stage public qualification: passed — Three identical 71.43 percent runs, weighted score 0.9524, zero invariants, and 100 percent single-step controls. Two-stage held-out transfer: diagnostic — 28.57, 42.86, and 42.86 percent; partial repeatable gain but not uniform across runs; zero invariants. Broader no-regression gate: failed — Level 1.1 regressed from 75 to 62.5 percent, while core RSI remained 100 percent. Two-stage cleanup: passed — Candidate and temporary tests removed; restored repository suite passed 80 of 80. Final process state after two-stage test: passed — No evaluator, port 8080 closed, Telegram notifications disabled, dashboard PID 41900 listening on port 8001. Final isolated Python suite: passed — 114 tests passed in 4.99 seconds; git diff --check passed and the worktree was clean. Public reliability confirmation: passed — final-public-r1/r2/r3-20260716: all 75 percent, weighted 0.9583, zero invariants, p95 544.7, 508.8, and 914.6 ms. External reliability confirmation: passed — final-heldout-r1/r2/r3-20260716: all 50 percent, weighted 0.9167, zero invariants, p95 810.6, 547.7, and 755.4 ms. Evaluation memory isolation: passed — Live held-out run added zero rows to the conversation database. Known synthetic prompts were absent from real-user episodic retrieval. Timeout cascade soak: passed — Five forced deadlines recovered in 184-201 ms; all five immediately following controls passed in 181-334 ms. Writer contention: passed — Second process rejected as busy in 601 ms, with zero blocked run_started rows and zero lease rows after owner exit. Hard interruption and resume: passed — resume-live-soak-20260716-2345 recovered a dead lease, completed 2/2 with zero invariants, and left no incomplete run. Scope audit: passed — No scoring, assertion, held-out, safety, credential, certificate, environment, CI, or grounding file changed. All-run invariant audit: passed — 64 completed lab runs were audited. Only seven deliberately forced timeout cases had invariants; no ordinary run had an unexpected invariant failure. Idle-gap readiness soak: passed — Five exact READY probes passed at 172.5-474.0 ms. Final lifecycle state: passed — Telegram disabled and absent; no evaluator; no writer or incomplete run; port 8080 closed; dashboard HTTP 200 on PID 41900. Current state Launcher and shutdown changes remain local with pre-existing Telegram lifecycle work; no Hiro commit or push was made. Gemma is fast and reliable under the present low-contention machine state. Model and Telegram are intentionally stopped; dashboard remains running. A non-fatal PermissionError occurred when another process held logs/main.log during rollover. Runtime readiness is established, but behavior promotion still requires repeatable evaluation baselines. The three-run stability requirement is now satisfied for the repaired Gemma runtime. Two repeatable resolver false positives remain: sort intent incorrectly triggers Gmail, and an unknowable held-out query incorrectly triggers weather. The resolver-boundaries-20260716 candidate is perfect on the matched public and held-out pair, improving both pass rates from 87.5 to 100 percent without an invariant or latency regression. The candidate remains local and unpromoted pending an explicit promotion decision. The next Hiro phase is a controlled emergence program beginning at rule induction and compositional transfer while reliability fixes proceed under a capped twenty-percent budget. Level 1 now has a non-ceiling baseline with reproducible strengths, reproducible weaknesses, and measurable cross-run variance. No response candidate was created or promoted from these baseline observations. Steps 1 through 3 are complete. Hiro now has a falsifiable ordered-operation-binding hypothesis rather than a generic reasoning diagnosis. No reasoning behavior, prompt, resolver, evaluator implementation, scoring code, safety rule, or grounding requirement was changed in this diagnostic pass. The originally requested steps 1 through 4 are now complete. Level 1.1 has three stable unchanged baselines plus independent held-out and mechanism-diagnostic evidence. The first ordered-binding candidate is rejected, removed, and not promoted. All evidence remains in the append-only ledger. The next mechanism should be materially different: a structured internal step representation or bounded two-stage execution, tested first on development-only counterbalanced cases. The two-stage infer-then-execute mechanism is rejected and absent from the working tree. Its public gain was real, but it caused negative transfer on Level 1.1. All new evaluation evidence remains in the append-only local ledger; no Hiro commit, push, deployment, or promotion occurred. The isolated branch contains sixteen new local commits and is clean. It has not been pushed or merged. The main Hiro workspace still contains the same pre-existing uncommitted Telegram lifecycle work and was not edited by this lab. All response candidates are absent. Only reliability, diagnostics, the prospective public typed-operation suite, and evaluation-memory isolation remain. The shared ledger has no active writer and no incomplete run. The dashboard remains on port 8001. 2,898 historical eval: conversation rows remain in the database but are excluded from all user-facing episodic retrieval. Final process state: Telegram disabled and stopped, Gemma unloaded, port 8080 closed, no evaluator, no ledger lease, no incomplete run, and dashboard PID 41900 still serving HTTP 200. Next steps Review and selectively integrate the reliability commits before any further long capability session. Decide explicitly whether to back up and remove the 2,898 historical synthetic conversation rows; do not perform destructive cleanup implicitly. Design a production-capable memory-relevance or prompt-budget mechanism, then freeze a genuinely new external family before testing it. Do not tune against the observed named-consensus families. Separate genuine string-operation binding limits from formatting and context-load effects with counterbalanced fresh tasks. Continue proposal-only gates with at least three public and two independently generated external families, zero invariant regressions, and production-channel fidelity.

Reliable direct retrieval for Hiro journal updates

2026-07-15T00:00:00Z

Executive summary The public Hiro journal was audited after ChatGPT Voice could not retrieve a dated update that remained accessible in a normal browser and regular ChatGPT. Direct production HTTP tests showed that the existing HTML and JSON URLs already returned clean 200 responses, correct content types, no cookies, and identical bodies for browser, curl, generic-bot, GPTBot, and ChatGPT-style user agents. OpenAI's retrieval tool nevertheless rejected the dotted dated URL before issuing a normal page request, while search discovery returned no result because the journal is intentionally noindex. The site now offers hyphenated date aliases, stable latest-update endpoints, an llms.txt discovery document, explicit robots access, and a public diagnostics report so AI systems do not need to depend on dotted paths or search indexing. A new automated validator exercises the deployed HTML, JSON, discovery, robots, and diagnostics endpoints across representative user agents. It checks status codes, redirects, cookies, MIME types, response equality, body completeness, and the full versioned JSON schema after successful deployments. After direct ChatGPT Voice retrieval remained inconsistent, the journal indexing policy was changed from unlisted/noindex to public and indexable so Voice can use web search as a redundant retrieval path. A Gemma 4 12B reliability review compared the local configuration with the official Google QAT model card, current llama.cpp server controls, current upstream issue reports, and observed GPU state. The model should fit a 12 GB RTX 5070 at the 4K context; the leading risks are competing VRAM allocation, an unpinned runtime executable, and missing explicit flash-attention, KV-cache, memory-fit, and observability settings. Work completed Production retrieval audit (Completed) Both bballstatistics.com and www.bballstatistics.com were tested for the dated HTML page and full JSON endpoint. Browser, curl, GPTBot-compatible, generic-bot, and ChatGPT-User request headers all received HTTP 200 with the same response type and no Set-Cookie header. The HTML response was static and complete without client-side JavaScript. The JSON response contained the full journal entry rather than metadata alone. No server-side user-agent discrimination, challenge page, authentication requirement, or redirect difference was observed. Stable AI retrieval aliases and discovery (Implemented) Each dotted journal date now receives equivalent hyphenated HTML and JSON aliases, avoiding URL parsers that treat dotted path segments conservatively. Stable /hiro/latest and /hiro/latest.json endpoints always expose the newest complete update. The machine-readable journal index now includes schemaVersion 1 plus stableUrl and stableJsonUrl fields for every entry. A root llms.txt document identifies the journal index, latest update, full JSON, and retrieval diagnostics URLs. An explicit robots.txt allows /hiro/ and /llms.txt while the journal pages retain noindex metadata. Direct retrieval is supported without asking search engines to index the journal. Automated post-deployment validation (Implemented) The validator discovers the latest date from the journal index and checks index, latest, hyphenated, legacy dotted, robots, discovery, and diagnostics endpoints. For browser, curl, generic-bot, and ChatGPT user agents it requires HTTP 200, no redirect, no cookie, correct MIME type, a nontrivial body, and byte-identical content. The latest JSON must include schemaVersion, date, title, executive summary, workstreams, decisions, validation, current state, and next steps with non-empty full-content arrays. A deployment-status GitHub Actions workflow runs the validator against both apex and www production hostnames after successful deployment and can also be launched manually. Voice-compatible search discovery (Implemented) Removed noindex metadata from generated journal HTML. Added an explicit sitemap covering the journal index, latest alias, and every stable dated page. Kept robots access explicit and preserved full HTML and JSON direct retrieval endpoints. The journal remains absent from the NBA site navigation, but search engines are now permitted to discover it. Gemma 4 12B on 12 GB research and local diagnosis (Completed) The official QAT Q4_0 GGUF is approximately 6.5 GiB, leaving theoretical headroom on a 12 GB GPU for a modest context and runtime buffers. Observed GPU allocation was approximately 10.7 GiB of 12.2 GiB while LM Studio was active, leaving insufficient clean headroom for a separately launched fully offloaded server. The launcher invokes llama-server by bare command name, but that command was not resolvable in the inspected shell; an NVIDIA CUDA backend exists under LM Studio and should be pinned explicitly or replaced with a separately versioned official runtime. Current llama.cpp supports automatic device fitting, flash attention, quantized KV caches, explicit micro-batching, prompt-cache controls, and per-slot metrics. The launcher does not currently set most of these. Recent upstream Gemma 4 reports include CUDA and experimental MTP instability, so the initial reliable profile should avoid multimodal projection and speculative MTP until a text-only baseline is proven. Decisions and reasoning Preserve noindex because the journal is public but intentionally unlisted. Retrieval reliability should come from stable direct URLs and machine-readable discovery, not search ranking. Keep the legacy dotted URLs working while making hyphenated dates and latest aliases the canonical retrieval paths. This improves compatibility without breaking existing references. Treat an AI tool's pre-request URL rejection separately from website availability. A clean 200 response cannot override a client-side safety policy, but simpler aliases and explicit discovery remove avoidable triggers. Validate deployed behavior across hostnames and user agents rather than assuming a successful local browser render proves AI accessibility. Use www.bballstatistics.com for all AI-facing canonical and discovery URLs because it serves query-string requests directly, while the apex hostname can trigger a Vercel managed challenge. Prioritize reliable ChatGPT Voice access over the previous unlisted-journal policy. Search discovery is now an intentional fallback when a Voice environment cannot directly fetch the supplied URL. Treat the present failure as an operational runtime and resource-isolation problem before changing response behavior or evaluator expectations. Validation and evidence Pre-change production HTTP matrix: passed — Both hostnames returned 200 with text/html or application/json as appropriate for browser, curl, generic-bot, GPTBot, and ChatGPT-style user agents; no cookie or differential response was observed. OpenAI retrieval-tool reproduction: passed — The direct dotted URL was rejected as unsafe by the retrieval layer even though independent HTTP requests succeeded, reproducing the user-visible mismatch between retrieval surfaces. Frontend production build: passed — Journal generation, TypeScript compilation, and the Vite production build completed successfully after the retrieval changes. Local production-preview retrieval matrix: passed — The final validator completed 50 requests with zero failures across HTML, full JSON, robots, llms discovery, diagnostics, and cache-busting query-string endpoints. All representative user agents received byte-identical content. Post-deployment production retrieval matrix: passed — Both apex and www production hostnames passed 42 of 42 requests each with zero failures. Browser, curl, generic-bot, and ChatGPT-style agents received 200 responses, correct MIME types, no redirects or cookies, identical bodies, and complete JSON. Apex query-string compatibility: failed-external — Vercel returned a JavaScript security checkpoint and HTTP 403 for apex journal URLs with query parameters across all tested user agents. Equivalent www URLs returned clean 200 responses. No application user-agent rule caused the difference. Indexability and discovery build: passed — The generator produced indexable HTML and a valid sitemap, and the complete TypeScript/Vite production build passed. Gemma runtime research and environment inspection: passed — Confirmed the local 6.98 GB model file, RTX 5070 12.2 GB capacity, approximately 10.7 GB current VRAM allocation, missing llama-server command resolution, installed LM Studio CUDA backend, and current upstream runtime controls and known Gemma 4 issue reports. Current state The journal remains static, public, unlisted, and noindex. Legacy dotted URLs remain available alongside hyphenated and latest aliases. Full versioned JSON is available without authentication, cookies, or JavaScript. The remaining external limitation is that an AI client may independently refuse an unindexed or previously unseen domain before making an HTTP request. After deployment, OpenAI's retrieval tool still rejected the apex latest, JSON, llms, and hyphenated URLs as unsafe before issuing an HTTP request. This external URL-trust decision is not explained by the verified website response behavior. All AI-facing canonical, index, llms, and diagnostics URLs now use www.bballstatistics.com. The apex hostname remains available for plain requests but is not advertised for AI retrieval because Vercel may challenge query-string requests. The journal is now intended to be public and indexable; search-engine discovery may take time after deployment. No Gemma launcher or response behavior was changed during this research pass; the result is a staged implementation and measurement plan. Next steps Confirm ChatGPT Voice can retrieve /hiro/latest or the hyphenated dated URL after deployment. Monitor deployment validation failures for CDN, MIME type, redirect, schema, or user-agent drift. Keep stable aliases and full JSON generation in the standard journal publication workflow. Escalate the bballstatistics.com domain-level safe-open rejection to OpenAI support with the successful production HTTP matrix as evidence. If apex query-string support is required, explicitly approve a narrowly scoped Vercel managed-firewall bypass for /hiro, /llms.txt, and /robots.txt after reviewing the security tradeoff. Confirm search engines index the sitemap and test ChatGPT Voice using a title-based web search after discovery propagates. Create an isolated Gemma runtime profile, unload competing GPU models, pin a current CUDA llama.cpp binary, benchmark explicit memory-safe configurations, and accept a profile only after repeated Hiro-shaped latency and cancellation tests.

Lifecycle controls and evaluation timeout-cascade diagnosis

2026-07-14T00:00:00Z

Executive summary Hiro's local operating workflow was tightened so the model server and Telegram integration behave as one intentional lifecycle instead of unrelated background processes. Starting Hiro now enables Telegram notifications and starts the bot, while the new shutdown path disables notifications before stopping services. The change also teaches Hiro's monitoring and notification layers to distinguish an intentional shutdown from a failure. That prevents avoidable alerts and automatic recovery behavior when the operator has deliberately taken the system offline. Documentation and focused tests were added alongside the implementation. The code and test intent were inspected during this work session, but the targeted Python test could not be executed in the current tool environment because a project Python runtime with pytest was not available. Full runtime verification therefore remains an explicit follow-up. A later controlled laboratory session validated the reliability branch with all 75 Python tests passing, then ran repeated public and repository-external held-out evaluations using only the local Gemma model. Four consecutive daytime repetitions converged on the same degraded result, establishing that the failure was systematic rather than sampling noise. Detailed tracing identified a timeout cascade: Hiro's full agent request currently takes about 97 seconds, while each evaluator case is limited to 60 seconds. Cancellation then leaves the single model slot occupied for roughly 16 additional seconds, consuming the next case's budget and propagating failures through the suite. No behavioral candidate was promoted. Work completed Unified startup and shutdown behavior (Implemented) The existing model-server startup command now invokes a dedicated Telegram lifecycle helper after launching the local model. The helper is idempotent: it reuses an already-running managed Telegram process instead of starting duplicates. A companion shutdown command disables Telegram notifications first, stops the managed bot, records that downtime is intentional, and then stops local model-server processes. Runtime marker and process files are kept in a local-only state directory that is excluded from version control. Notification gating (Implemented) Central configuration now exposes explicit state for whether Telegram notifications are enabled and whether Hiro's servers were intentionally stopped. Improvement-cycle summaries, regression alerts, and task-runner messages check the lifecycle state before attempting a Telegram delivery. Disabling notifications is therefore a system-level state rather than a convention that each operator must remember manually. Downtime-aware monitoring (Implemented) The local model health check returns without trying to restart the model when an intentional-stop marker is present. The continuous health monitor similarly suppresses model-unavailable incidents during deliberate downtime. Starting the system clears the intentional-stop state, restoring normal health checks and recovery behavior. Operator documentation and tests (Added) The project README now describes the coupled startup behavior, the new shutdown command, and the reduced set of processes that must be started manually. Focused tests cover the lifecycle marker semantics and confirm that notification delivery remains disabled when the enabling marker is absent. The test implementation avoids making a real outbound Telegram request. Controlled evaluation reliability study (Diagnosed; promotion rejected) A clean external worktree and local-only branch isolated the study from Hiro's uncommitted lifecycle work. The local model identity, startup latency, warmup latency, external-suite fingerprint, single-writer discipline, and protected-file scope were checked before evaluation. Three requested sequential repetitions produced identical held-out metrics: 12.5 percent pass rate, a weighted score of about 0.519, 24 invariant failures per run, and latency pinned near the 60-second case deadline. Across those three repetitions, 6 of 48 held-out observations passed and 72 invariant failures were recorded. Combined with an earlier matching run, four consecutive repetitions reproduced the degraded state. Earlier nighttime measurements had reached a 75 percent held-out pass rate, demonstrating that the current daytime failure was an operational reliability regression rather than a justified response-behavior change. Timeout and cancellation tracing (Root cause confirmed) Per-case inspection showed that deterministic resolver responses completed in milliseconds while every model-backed case reached the 60-second deadline with an empty response. A direct short model request completed in about 32.6 seconds. The exact Hiro agent path used a roughly 1,662-token prompt and completed correctly in about 97.0 seconds: approximately 82.8 seconds of prompt processing followed by 13.9 seconds for nine generated tokens. A controlled cancellation reproduced delayed recovery. The client stopped at 60 seconds, but the model server retained its only slot for approximately 16 more seconds before releasing it. The evidence supports a combined cause: the evaluator deadline is shorter than current end-to-end latency, and delayed single-slot cancellation causes subsequent requests to inherit queue delay. Decisions and reasoning Use explicit marker files for operator intent. Process availability alone cannot distinguish a crash from a deliberate shutdown, while persisted local intent can be read consistently by independent Hiro components. Disable notifications before stopping other services. This ordering closes the window in which shutdown-related health failures could generate false alerts. Manage only the Telegram process started by the lifecycle helper. The helper validates the saved process identity before stopping it, reducing the chance of affecting an unrelated process. Keep lifecycle state outside version control. The markers describe one machine's current runtime state and are not project configuration. Do not promote response behavior from the current measurements. Repeated failures were dominated by infrastructure timing and invariant regressions, not credible evidence of a behavioral improvement. Stop repeating an unchanged evaluation once identical timeout-bound results become systematic. Further repetitions have diminishing diagnostic value until cancellation recovery and throughput gating are implemented. Treat resolver false positives as a separate measured problem. They should be addressed only with a narrowly scoped hypothesis and matched validation after the evaluation harness is reliable. Validation and evidence Implementation inspection: passed — Startup, shutdown, configuration, monitoring, notification, documentation, and test changes were reviewed together for consistent lifecycle semantics. Secret and security-content screening: passed — This update contains no credentials, API values, private identifiers, or actionable descriptions of an unresolved security weakness. Focused Python tests: not-run — The current tool environment did not expose a configured Python installation with pytest, so the new targeted tests remain to be run in Hiro's normal development runtime. Live process-cycle exercise: not-run — A real start-stop-start exercise was not performed during this publication step because it would alter the operator's currently running local services. Hiro full Python suite: passed — All 75 tests passed on the isolated reliability branch. The changed-file audit was limited to local model readiness code and its focused tests. Repeated public and external held-out evaluations: passed-with-regression — The evaluator completed all requested sequential runs and wrote their append-only records successfully, but response quality was rejected because the three requested held-out repetitions each passed only 2 of 16 observations and recorded 24 invariant failures. Extended core-agent latency trace: passed — The exact model-backed identity request completed correctly under an extended diagnostic deadline in about 97.0 seconds, proving the model was reachable and the 60-second evaluation deadline was insufficient under current throughput. Cancellation recovery trace: passed — A controlled 60-second cancellation showed that the server's single slot remained occupied for approximately 16 seconds after client cancellation, confirming the cascade mechanism. Current state The lifecycle implementation and its focused tests are present in Hiro's current development working tree. Telegram notification attempts are gated by explicit lifecycle state across the main notification paths touched by this change. Intentional downtime is represented separately from an unexpected health failure. Full automated and live operational verification is still pending. The lifecycle implementation was subsequently exercised during controlled local sessions: notifications were disabled before model shutdown, no evaluator or model process remained afterward, and the dashboard was preserved. Evaluation measurements are not currently suitable for behavioral promotion because model-backed requests can exceed their deadlines and contaminate following cases through delayed slot recovery. No resolver, response-latency, baseline-pin, or promotion candidate was accepted from the diagnosed runs. Next steps Run the focused Telegram lifecycle tests in Hiro's configured Python environment. Exercise the full startup, status, shutdown, and restart sequence on the development machine and confirm process and marker cleanup. Run the broader integration suite to detect interactions with scheduling, monitoring, and improvement-cycle reporting. Commit the verified Hiro lifecycle changes once the operational checks pass. Add cancellation recovery that waits for the local model slot to become idle before advancing to another evaluation case. Record per-case slot state and server timing so prompt processing, generation, queueing, and cancellation delays can be distinguished. Add a readiness and throughput probe that refuses to start a measurement when predicted latency cannot fit within the evaluation deadline. Calibrate the evaluation timeout or reduce evaluation prompt overhead without changing scoring, held-out content, safety policy, or grounding requirements. Run matched baseline and candidate repetitions only after three consecutive stable baseline measurements are available.