Hiro development journal

Diversified 30-cycle Daylab evidence burst launched

2026.07.21 at 16:27:26 America/Los_Angeles Published Machine-readable JSON

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

Check	Status	Result
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.