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`.
* **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`.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
FastCode is a token-efficient framework for comprehensive code understanding and analysis, delivering superior speed, exceptional accuracy, and cost-effectiveness for large-scale codebases and software architectures. It features a three-phase framework for semantic-structural code representation, lightning-fast codebase navigation, and cost-efficient context management.
LLM Council works together to answer your hardest questions. A local web app that uses OpenRouter to send queries to multiple LLMs, have them review/rank each other's work, and finally a Chairman LLM produces the final response.
Our goal at OpenMV is to make building machine vision applications on high-performance, low-power microcontrollers easy. We've done the hard work designing professional hardware and writing reliable, high-performance software for you, leaving more time for your creativity.
This GitHub repository directory contains resources for evaluating Large Language Models (LLMs), including a Jupyter Notebook demonstrating how to use LLM Arena as a judge and a Python script for the same purpose. It also includes a README file with instructions on how to view the notebook if it doesn't render correctly on GitHub.
MarkItDown is an open-source Python utility that simplifies converting diverse file formats into Markdown, designed to prepare data for LLMs and RAG systems. It handles various file types, preserves document structure, and integrates with LLMs for tasks like image description.
PaperCoder is a multi-agent LLM system that transforms scientific papers into code repositories through a three-stage pipeline: planning, analysis, and code generation. It aims to create faithful, high-quality implementations.
This repository organizes public content to train an LLM to answer questions and generate summaries in an author's voice, focusing on the content of 'virtual_adrianco' but designed to be extensible to other authors.
Powering the future of open-source AI agent development. Discover, run, and compose AI agents from any framework. Build production-grade AI agents in both Python and Typescript. Join our community on Discord, BlueSky, and YouTube.
