Tags: andrej karpathy* + python*

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  1. 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.
  2. The article details “autoresearch,” a project by Karpathy where an AI agent autonomously experiments with training a small language model (nanochat) to improve its performance. The agent modifies the `train.py` file, trains for a fixed 5-minute period, and evaluates the results, repeating this process to iteratively refine the model. The project aims to demonstrate autonomous AI research, focusing on a simplified, single-GPU setup with a clear metric (validation bits per byte).

    * **Autonomous Research:** The core concept of AI-driven experimentation.
    * **nanochat:** The small language model used for training.
    * **Fixed Time Budget:** Each experiment runs for exactly 5 minutes.
    * **program.md:** The file containing instructions for the AI agent.
    * **Single-File Modification:** The agent only edits `train.py`.

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