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Bootstrap hotpath from engineering spec v0.1
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
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# Accept thresholds for bench-runner (SPEC §3.1).
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# This file is trust-layer: read-only to the agent (SPEC §10).
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# CI lower bound of the speedup ratio must clear 1 + threshold.
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threshold = 0.02
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# Measured runs per side (SPEC minimum: 30). Interleaved ABAB.
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runs_per_side = 30
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# Warm-up runs per side, discarded.
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warmup_runs = 3
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# Bootstrap resamples for the ratio CI.
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bootstrap_iters = 10000
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# Confidence level for the ratio CI.
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confidence = 0.95
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# Fixed PRNG seed so calibration runs are reproducible.
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bootstrap_seed = 1887343589
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# ROI pricing inputs (SPEC §3.6, §9). Defaults are public-cloud list
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# prices; replace with the customer's own numbers per engagement.
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# This file is trust-layer: read-only to the agent (SPEC §10).
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# On-demand vCPU rate, dollars per core-hour.
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dollars_per_core_hour = 0.04
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# Hours per year the fleet actually runs the workload (8760 = 24/7).
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hours_per_year = 8760.0
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# Printed on the report so the number survives hostile review.
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rate_source = "example: AWS m7i on-demand us-east-1, per-vCPU approximation — replace per engagement"
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