safiyu/kontexta

kontexta tackles a genuinely hard problem in the LLM space: how to give a language model enough context without blowing through token limits or exposing private data. The concept of a surgical context economy is well-named because the core challenge is deciding what to include and what to leave out, and doing that safely. The README communicates the value proposition clearly, and the privacy-first angle is a strong differentiator in a market where most context solutions just throw more tokens at the problem.

The project being TypeScript aligns well with the modern AI tooling ecosystem, and local-first memory for LLMs is a space that is growing quickly as developers realize cloud-only approaches arent viable for sensitive data. The repository structure looks well organized and the description of safe, private memory for local LLMs matches real market demand. I would love to see example usage patterns and benchmark comparisons showing how much token reduction kontexta achieves in common scenarios. A comparison against naive full-context approaches would help users understand the real savings. Overall, kontexta addresses an important pain point with a thoughtful architecture, and the privacy-first approach gives it a clear moat.