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Claude
328aafae77
Commit the spec and scaffold the system it describes:
- SPEC.md: the full v0.1 engineering spec
- crates/: trust-layer Rust workspace, unit-tested
- bench-runner: interleaved ABAB scheduling with discarded warm-ups,
deterministic bootstrap CIs over the ratio of medians,
CI-lower-bound accept rule, env fingerprinting, and a CLI with
compare + A/A self-test modes (A/A verified passing end-to-end)
- diff-test: gate outcome model, sha256 corpus pin verifier that
refuses on mismatch, per-target equivalence policy parser
(byte-identical default, explicit fp-tolerance opt-in)
- ledger: append-only SQLite attempt schema with UPDATE/DELETE
refused by triggers, anti-double-attempt query
- report: cores-saved and dollars/yr ROI math over speedup CIs,
pricing.toml inputs
- playbook/: classes 1-7 with preconditions, procedure, verification
notes, and known failure modes per class
- agent/: Python package skeleton — six-tool contract and prompting
spine, harness IPC deferred to Phase 1
- config/: accept.toml (2% threshold, 30 runs/side), pricing.toml
- targets/, corpora/, results/calibration/: layout and policy docs
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
61 lines
2.4 KiB
Markdown
61 lines
2.4 KiB
Markdown
# peltier.io
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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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hotpath finds the 10–90% left on the floor between "the compiler" and
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"hire a perf consultant," with two properties no consultant offers:
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**verified equivalence** (a change provably preserves behavior or is
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discarded) and **self-verifying ROI** (the value claim is a stopwatch on
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trusted infrastructure, not a survey).
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Full design: **[SPEC.md](SPEC.md)**.
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## Layout
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```
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crates/ Trust layer (agent has NO write access — SPEC §10)
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bench-runner/ Interleaved A/B timing, bootstrap CIs, A/A calibration
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diff-test/ Equivalence gates, corpus hash-pinning, per-target policy
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ledger/ Append-only SQLite attempt ledger (enforced by triggers)
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report/ ROI math: speedup CI → cores → dollars, caveats included
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agent/ Untrusted proposer (Claude Agent SDK, Python)
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playbook/ Optimization classes 1–7, tried strictly cheapest-first
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config/ accept.toml (thresholds), pricing.toml (ROI inputs)
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targets/ Per-target checkouts — the only agent-writable path
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corpora/ Hash-pinned golden-replay inputs (read-only to agent)
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results/ Calibration evidence, per-engagement outputs
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```
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## Quick start
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```sh
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cargo test --workspace # trust-layer unit tests
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just aa # A/A self-test: must yield a null verdict
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just compare "cmd-a" "cmd-b" # interleaved A/B with bootstrap CI
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```
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## Status
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Phase 0–1 scaffold (see SPEC §5 for the phase plan and exit criteria).
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Done:
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- Workspace with the four trust-layer crates; core statistics
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(interleaved scheduling, bootstrap ratio CIs, CI-lower-bound accept
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rule), append-only ledger schema with mutation-refusing triggers,
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corpus hash-pin verifier, equivalence-policy parser, ROI formulas —
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all unit-tested.
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- bench-runner CLI: whole-program `compare` and `aa` modes with env
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fingerprinting.
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- Playbook classes 1–7; agent tool contract and prompting spine.
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Next (Phase 1 exit criteria):
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- `perf stat` counters + max RSS + RAPL capture per run.
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- Automated A/A calibration sessions and regression-injection self-test
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recorded to `results/calibration/` (<5% FP, ≥95% detection).
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- diff-test orchestration end-to-end on two real targets; harness IPC
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for the agent tool surface.
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## License
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GPL-3.0-or-later — see [LICENSE](LICENSE).
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