Claude 0d29d51f72 gates: diff-test orchestration — corpus pin + tests + golden per target
Adds the diff-test CLI ('just gates <target>'): loads
targets/<name>/target.toml (new [corpus] section: manifest, root,
golden_sha256; commands now repo-root-relative), verifies the corpus
manifest FIRST and refuses to run any gate on mismatch, then runs the
upstream test suite and golden replay (stdout sha256 vs pinned hash).
Differential fuzz and sanitizers remain per-attempt manual steps in
Phase 1 and are reported as skipped-with-reason; all-skipped is not a
pass.

Verified end-to-end on both vendored targets (comrak: 1 pinned file,
848 tests, golden PASS; tokei: 545 pinned files, 250 tests, golden
PASS) and the refusal path (tampered corpus -> 'HASH MISMATCH ...
refusing to run' before any gate executes).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
2026-07-06 23:30:40 +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.

S
Description
Spot cooling for hot paths. A profile-guided optimization agent that only ships wins it can prove.
Readme MIT
1.3 MiB
Languages
Rust 66.4%
Python 14.4%
Shell 10.9%
C 4.1%
PowerShell 2.9%
Other 1.3%