Search Results for "learning classifier system"

CUDA Agent is a research-driven agentic reinforcement learning system designed to automatically generate and optimize high-performance CUDA kernels for GPU workloads. The project addresses the long-standing challenge that efficient CUDA programming typically requires deep hardware expertise by training an autonomous coding agent capable of iterative improvement through execution feedback. Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. ...

darwin-skill is an experimental framework designed to automatically improve AI agent “skills” through iterative evaluation and optimization loops inspired by machine learning training processes. Instead of treating prompts or skill definitions as static assets, the system applies a continuous improvement cycle that evaluates performance, proposes changes, tests outcomes, and either retains or reverts modifications. The framework introduces a scoring system across multiple dimensions, enabling quantitative assessment of skill quality and ensuring that only improvements are preserved over time. ...

The Compound Engineering plugin project is an AI-driven workflow system designed to improve software development by turning each unit of work into a reusable and compounding asset. It provides a structured set of commands and agents that guide developers through stages such as brainstorming, planning, execution, review, and knowledge capture. The core philosophy is to reduce technical debt by emphasizing thorough planning and continuous learning, ensuring that each iteration improves future work rather than increasing complexity. ...

...After creating a new Wasp app, the user opens an AI coding agent inside the project and lets it fetch course module instructions. The agent then works as both tutor and pair programmer, explaining the system while helping the user build features from plain-language requests. Progress is tracked through JSON files written into the project, making the learning path structured while still letting the user build their own app idea.