Claude abaed3c49d agent: phase2-tokei-001 — first auto-accepted win from the unattended loop
Ledger row: class 5 (algorithmic) on tokei, first-byte gate table for
the per-byte token matchers in perform_multi_line_analysis.
Speedup median 1.107, 95% CI [1.084, 1.131] vs the banked-wins baseline,
workload: 221 MB / 4360 files, single rayon thread, pinned core.

Manual audit gates (pipeline covers tests+golden+bench only):
- Differential fuzz, 10k mutated inputs across 200 batched runs:
  0 divergences after canonicalizing tokei's benign parallel-walker
  output ordering (present in pristine baseline; counts identical).
  Fuzzer added as scripts/diff-fuzz-tokei.py.
- ASan run recorded in the case study (next commit).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
2026-07-10 05:08:16 +00:00
2026-07-06 14:20:25 -04:00

peltier.io

Spot cooling for hot paths. A profile-guided optimization agent that only ships wins it can prove.

hotpath finds the 10–90% left on the floor between "the compiler" and "hire a perf consultant," with two properties no consultant offers: verified equivalence (a change provably preserves behavior or is discarded) and self-verifying ROI (the value claim is a stopwatch on trusted infrastructure, not a survey).

Full design: SPEC.md.

Layout

crates/            Trust layer (agent has NO write access — SPEC §10)
  bench-runner/    Interleaved A/B timing, bootstrap CIs, A/A calibration
  diff-test/       Equivalence gates, corpus hash-pinning, per-target policy
  ledger/          Append-only SQLite attempt ledger (enforced by triggers)
  report/          ROI math: speedup CI → cores → dollars, caveats included
agent/             Untrusted proposer (Claude Agent SDK, Python)
playbook/          Optimization classes 1–7, tried strictly cheapest-first
config/            accept.toml (thresholds), pricing.toml (ROI inputs)
targets/           Per-target checkouts — the only agent-writable path
corpora/           Hash-pinned golden-replay inputs (read-only to agent)
results/           Calibration evidence, per-engagement outputs

Quick start

cargo test --workspace          # trust-layer unit tests
just aa                         # A/A self-test: must yield a null verdict
just compare "cmd-a" "cmd-b"    # interleaved A/B with bootstrap CI

Status

Phase 0 complete (see SPEC §5). Five attempts in the ledger across two pinned targets (comrak 0.53.0, tokei 14.0.0), A/A-calibrated, every number with CI and workload:

  • tokei +10.4% median [95% CI +8.5%, +12.0%] — read-path buffer zeroing eliminated + fat LTO, byte-identical, 4,332-iteration diff-fuzz clean, sanitizers clean, auto-accepted (results/phase0/case-study-tokei.md). Meets the Phase 0 exit bar.
  • comrak +4.6% [+3.3%, +5.8%] — mimalloc swap, needs-human-review on an LSan reachability flag (results/phase0/case-study-comrak.md).
  • Two null/sub-threshold results correctly rejected and logged.

Next: Phase 1 trust-layer hardening (containers, perf, automated calibration, gate orchestration, agent IPC).

Done:

  • Workspace with the four trust-layer crates; core statistics (interleaved scheduling, bootstrap ratio CIs, CI-lower-bound accept rule), append-only ledger schema with mutation-refusing triggers, corpus hash-pin verifier, equivalence-policy parser, ROI formulas — all unit-tested.
  • bench-runner CLI: whole-program compare and aa modes with env fingerprinting.
  • Playbook classes 1–7; agent tool contract and prompting spine.

Next (Phase 1 exit criteria):

  • perf stat counters + max RSS + RAPL capture per run.
  • Automated A/A calibration sessions and regression-injection self-test recorded to results/calibration/ (<5% FP, ≥95% detection).
  • diff-test orchestration end-to-end on two real targets; harness IPC for the agent tool surface.

License

GPL-3.0-or-later — see LICENSE.

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Spot cooling for hot paths. A profile-guided optimization agent that only ships wins it can prove.
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