A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
graphify is an AI coding assistant skill that transforms codebases, documents, and images into a structured, queryable knowledge graph. By utilizing deterministic AST parsing via tree-sitter for code and multimodal LLM capabilities for unstructured data like PDFs and screenshots, it creates a comprehensive map of concepts and relationships. This allows developers to understand complex architectures faster and find the "why" behind design decisions. A key advantage is its massive reduction in token usage per query compared to reading raw files, making it highly efficient for large-scale projects. The tool supports 19 programming languages and integrates seamlessly with platforms like Claude Code and Codex, providing an interactive, persistent, and highly organized way to navigate any codebase or research corpus.
This project, `autoresearch-opencode`, is an autonomous experiment loop designed for use with OpenCode. It's a port of `pi-autoresearch`, but implemented as a pure skill, eliminating the need for an MCP server and relying solely on instructions the agent follows using its built-in tools. The skill allows users to automate optimization tasks, as demonstrated by the example of optimizing the BogoSort algorithm which achieved a 7,802x speedup by leveraging Python's `bisect` module for sorted-state detection.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
"Refreshing my understanding of deep learning as a "stack of data transformations" is incredibly powerful. It's like a sequence of layers, each layer transforming the input data into something more abstract and informative. This perspective makes it easier to understand how neural networks process information."
