Technical Guides

Self-Improving Agents: Applying the Sharpening Framework to Local LLMs

Self-Improving Agents: Applying the Sharpening Framework to Local LLMs

This is the second post in a 100-part series, where we take breakthrough AI papers and turn them into working code building the next generation of AI, one idea at a time.

🔧 Summary

In my previous post, I introduced co_ai a modular implementation of the AI co-scientist concept, inspired by DeepMind’s recent paper Towards an AI Co-Scientist.

But now, we’re going deeper.

This isn’t just about running prompts through an agent system it’s about building something radically different:

Building an AI Co-Scientist

Building an AI Co-Scientist

This is the fiOr you guys want to scare all of them All right problem this is where I say norst post in a 100-part series, where we take breakthrough AI papers and turn them into working code building the next generation of AI, one idea at a time.

🧾 Summary

In this post, I’ll walk through how I implemented the ideas from Towards an AI Co-Scientist into a working system called co_ai.