7efd1368d1
Domain papers distilled from python-numbers-everyone-should-know: - async-overhead: 1,400x sync vs async overhead - collection-membership: 200x set vs list at 1000 items - json-serialization: 8x orjson vs stdlib - exception-flow: 6.5x exception overhead (try/except free) - string-formatting: f-strings > % > .format() - memory-slots: 69% memory reduction with __slots__ - import-optimization: 100ms+ for heavy packages - database-patterns: 98% commit overhead in SQLite RULEBOOK.md: ~200 token distillation for coding subagents 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Python Performance ADRs
Architecture Decision Records for Python performance patterns, distilled from benchmarks.
For Coding Agents
Load RULEBOOK.md into context for coding tasks. It's tight (~200 tokens).
For deeper understanding, load the relevant paper from papers/.
For Review Agents
Use evals/rules.yaml for automated code review checks.
Source
Distilled from python-numbers-everyone-should-know benchmarks.
See PLAN.md for the full methodology.