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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
44 lines
2.1 KiB
Markdown
44 lines
2.1 KiB
Markdown
# Class 5 — Algorithmic
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Complexity class and primitive selection. Highest variance: the biggest
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wins live here, and so do the most seductive wrong ideas.
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## Preconditions
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- Profile signature: one dominant frame that is *algorithm*, not memory
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(high IPC, low miss rate, still slow); superlinear scaling of runtime
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with input size; hot `contains`/`find` over linear structures; repeated
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recomputation of pure functions on repeated arguments.
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## Procedure (cheapest first)
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1. Better std/library primitive: `HashMap` for linear scans, `sort_unstable`
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over `sort`, `entry()` API over get-then-insert, buffered I/O,
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`memchr`-class scanning.
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2. Precomputation: hoist invariant work out of loops; build lookup tables
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at startup when the domain is small.
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3. Memoization for pure hot functions with high argument repeat rates —
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confirm the repeat rate from a counting instrument before adding a
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cache.
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4. Complexity-class replacement (the real prize): O(n²) → O(n log n)
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with a known-correct algorithm. Prefer a well-tested library
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implementation over a hand-rolled one, always.
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## Verification notes
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- HashMap iteration order is not deterministic order: if results flow
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from iteration order into output, replay diverges — either sort at the
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boundary or reject the change.
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- Memoization + interior mutability in multithreaded targets touches the
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concurrency review tier (SPEC §8).
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- Differential fuzz is load-bearing here: algorithmic replacements have
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edge-case surfaces (empty input, duplicates, already-sorted, unicode)
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where "equivalent" algorithms differ. 10k iterations minimum, seeded
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with corpus-derived cases.
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## Known failure modes
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- Asymptotic win, constant-factor loss at real input sizes — the ledger
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records the workload; the verdict is measured at that workload.
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- Caches without bounds: memoization that trades a 10% speedup for
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unbounded RSS growth. Check max RSS; bound the cache.
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- "Equivalent" floating-point reformulations that aren't (different
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rounding paths) — FP output changes route to the tolerance policy or
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human review, never silently absorbed.
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