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 explores five Python decorators that can be used to optimize LLM-based applications. These decorators leverage libraries like functools, diskcache, tenacity, ratelimit, and magnetic to address common challenges such as caching, network resilience, rate limiting, and structured output binding. The article provides code examples to illustrate how each decorator can be implemented and used to improve the performance and reliability of LLM applications.
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.
Examples for common OpenSandbox use cases. Each subdirectory contains runnable code and documentation. Integrations and sandboxes are available for various tools and services like AI models, desktop environments, and web scraping.
Learn how to equip your Microsoft Agent Framework agents with portable, reusable skill packages that provide domain expertise on demand using Agent Skills. This article covers what Agent Skills are, progressive disclosure, creating skills, connecting skills to an agent (with .NET and Python examples), use cases, and security considerations.
Alibaba has released CoPaw, an open-source framework designed to provide a standardized workstation for deploying and managing personal AI agents. It addresses the shift from LLM inference to autonomous agentic systems, focusing on the environment in which models operate. CoPaw utilizes AgentScope, AgentScope Runtime, and ReMe to handle agent logic, execution, and persistent memory, enabling long-term experience and multi-channel connectivity.
This course takes you from Python fundamentals to AI Agent development, covering core Python, NumPy, Pandas, SQL, Flask, FastAPI, LLMs, and open-source models via HuggingFace.
Learn how to build a simple Minecraft server (MCP) using Python. This tutorial covers setting up the environment, creating a basic server, and handling client connections.
OpenClaw is an open-source project that allows you to turn a Raspberry Pi into a capable AI agent. It provides a framework for connecting large language models (LLMs) to physical sensors and actuators, enabling the Pi to interact with the real world. This article details how to set up OpenClaw, including installing the necessary software, configuring the LLM, and connecting sensors like a microphone and camera. It also explores potential applications, such as home automation, robotics, and environmental monitoring.
This article details how to use OpenClaw, an open-source framework, to build a personal assistant. It covers the setup, configuration, and basic usage of OpenClaw, focusing on its ability to connect to various tools and services to perform tasks like sending emails, browsing the web, and executing commands. The guide provides a practical walkthrough for creating a customized AI assistant tailored to individual needs.
