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Claude
017491010b
- Profiles regenerated with callgrind --cache-sim --branch-sim for both targets: per-function D1/LL miss and branch-mispredict columns unlock playbook class-4 preconditions (comrak: syntect write_highlighted owns 31% of D1 read misses; tokei: parse_lines owns 56% of Ir). - tokei workspace: accepted Phase 0 wins banked as a local commit so stash-based baseline rebuilds attribute only the marginal patch to new attempts (false-accept trap closed before grinding). - read_ledger now returns per-attempt history (class, hotspot, hypothesis, verdict): a class may be re-entered with a materially new hypothesis, never a duplicate one. Task prompt updated to match. - read_verdict long-polls the ledger up to 45s server-side (under the 60s MCP cap), cutting the agent's polling turn burn ~15x. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
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-reviewon 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
compareandaamodes with env fingerprinting. - Playbook classes 1–7; agent tool contract and prompting spine.
Next (Phase 1 exit criteria):
perf statcounters + 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.