continuous-learning

To capture engineering knowledge within your code repository, you can use a file-based continuous learning loop that tracks raw observations, session episodes, and decision traces. This system synthesizes development discoveries directly in your local environment.

Preventing repeated engineering mistakes requires promoting durable signals from decision traces into project documentation like AGENTS.md or PRINCIPLES.md. This approach records architectural trade-offs to ensure team members avoid previous errors.

A repository memory system tracks codebase evolution by logging raw items and promoting them into durable lessons, principles, and architecture docs. It manages an index state to synthesize fragmented engineering knowledge over time.

Yes, you need Python to implement this continuous learning loop. Python script execution is required to manage the local learning directory structure and maintain the index state for your repository's knowledge management.

Yes, you can update AGENTS.md files using this knowledge management approach. The system promotes durable signals and synthesized lessons from decision traces directly into your project identity files to maintain accurate documentation.

You should avoid a file-based learning loop if your engineering knowledge management requires real-time synchronization across distributed databases instead of a local directory structure. This approach relies on file-based tracking without external dependencies.