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Self-referential self-improving AI agents that optimize for any computable task using meta-learning and code generation
Patterns for building AI agents that learn from their own execution, detect failure modes, and improve autonomously. Covers feedback loops, performance regression detection, memory curation, skill extraction, and meta-learning architectures. Use when building agents that need to get better over time, managing auto-memory, or designing self-correcting systems.
You are updating this project's CLAUDE.md to reflect **meta learnings** (conventions, philosophy, gotchas) and **significant recent changes** to the codebase.