Show HN: I made an agent can self-improving while working (yehuitang0316.github.io)

A developer has introduced GEAR, an innovative agent that employs recursive self-improvement (RSI) to enhance productivity through continuous skill refinement based on task feedback. This capability allows GEAR to outperform standard general-purpose agents, such as Astra max utilizing the Codex model, by significantly improving task completion rates. For instance, GEAR’s enhancements led to a notable increase in task pass rates from 27% to 53%, with GEAR-tuned agents achieving up to 84.26% pass rates on complex tasks. The introduction of GEAR is significant for the AI/ML community as it demonstrates a tangible evolution in agent performance through optimized learning processes. By allowing an agent to inspect failed attempts, propose improvements, and retain the best-performing versions, GEAR exemplifies how adaptive systems can effectively handle real-world challenges. This approach not only raises the bar for task completion rates but also opens new avenues for deploying AI in more specialized applications, marking a pivotal shift toward more intelligent and capable digital assistants.