EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 29·5 min read
Warp builds self-improving agents on Claude
TL;DR
Warp has developed a method for creating self-improving agents using user feedback, significantly enhancing the experience for nearly 1 million developers. This approach addresses the common issue of low-quality output from AI agents.
✦ Why It Matters
Implement user feedback loops in your AI projects to enhance agent performance and user satisfaction.
Key Takeaways
- Utilize user feedback to refine agent performance continuously.
- Implement a two-skill framework for agent knowledge management.
- Ensure human oversight in the feedback loop for better results.
- Apply this self-improvement pattern across various agent tasks.
How It Works
Warp's self-improvement loop consists of a base skill that holds functional knowledge and an outer improver skill that processes user feedback. The outer skill runs on a schedule, analyzing feedback and suggesting updates to the base skill, which allows agents to learn and adapt over time.