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docs: full mdBook site + GitHub Pages deploy; rewrite README as project front page
- docs/: an mdBook documentation site — thesis, architecture, measurement
discipline, equivalence gates, agent loop, isolation/anti-reward-hacking,
playbook, ROI, reproduce, roadmap, and the five case studies (pulled from
their single source in results/ via {{#include}}). Builds clean (21
pages).
- .github/workflows/docs.yml: builds mdBook and deploys to GitHub Pages on
push to main (one-time setup: Settings → Pages → Source = GitHub Actions).
- README.md: rewritten as the project front page — the thesis, the verified
results table, layout, quick start, how it stays honest, links to the
book. Replaces the stale Phase-0-era text.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EcKPWQ3MQD4NdEHwar96u2
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# The playbook
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Optimizations are tried in a fixed order — **cheapest and safest first** —
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because cost-per-attempt and risk both rise as you go down the list, and
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the ledger stops you from re-grinding a class that already has a verdict.
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| # | Class | Mechanism | Verified on |
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|---|---|---|---|
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| 1 | Build config | LTO, codegen-units, target-cpu | tokei ✅ +10.4% |
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| 2 | Allocator | Global allocator swap (mimalloc) | comrak ✅ +4.6% (human-ruled) |
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| 3 | Allocation churn | Remove per-item malloc/free | tokei ✅, cJSON ✅ (+ one overturned) |
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| 4 | Data layout | Make the cache hierarchy fit the access pattern | comrak (attempted) |
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| 5 | Algorithmic | Eliminate redundant work | tokei ×3 ✅, cJSON ✅ |
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| 6 | SIMD | Autovectorization enablement | comrak, tokei (attempted) |
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| 7 | Concurrency | Parallelism — always `needs-human-review` | — |
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Six classes were exercised across the case studies, with verified wins on
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classes 1, 2, 3, and 5 across **both a Rust and a C target**.
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## Why the ordering is load-bearing
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The cheapest class produced the project's single largest individual win:
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LTO on tokei, **+10.4%**, a pure build-config change with no source edit.
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The lesson the ledger enforces is the one every performance engineer learns
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the hard way — *you do not hand-optimize a hot loop before you have turned
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on the optimizer's own switches.* An agent that jumped straight to SIMD
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would have missed a bigger, safer, free win sitting one config flag away.
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Each class entry (`playbook/0N-*.md`) carries its **preconditions** (the
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profile signature that justifies trying it), its **procedure**, its
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**verification** requirements, and its **known failure modes**. The agent
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reads the profile, matches the signature, and picks the cheapest class
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whose preconditions are met and that isn't already spent on this hotspot.
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