wong2/bing-chat-sidebar-for-chrome

AutoGPT is an open-source platform designed for building, deploying, and running AI agents that can carry out complete workflows. Users can define tasks in plain English or use a visual builder to shape each step. The project offers two primary paths: a managed, hosted AutoGPT Platform that handles infrastructure and model access for a fee, and a self-hosting option that is free but requires users to provide their own infrastructure and model API keys. Agents can run on demand, on schedules, or from triggers, connecting to over 45 platforms and hundreds of AI models. It's presented as a tool to automate various functions, from executive operations and sales research to marketing campaign drafts and incident triage in engineering.

prompts.chat is an open-source library of prompts written for AI chat assistants, first released in December 2022 under the name Awesome ChatGPT Prompts. The GitHub project has since grown and now distributes prompts through a website, a CSV file, a Markdown file, and a Hugging Face dataset, alongside a self-hosting option, a CLI, an MCP server, and a Claude Code plugin.

prompts.chat is the largest open-source prompt library for AI, formerly called Awesome ChatGPT Prompts. It hosts curated prompts in CSV and Markdown, available as a public website, Hugging Face dataset, or self-hosted instance. The project supports multiple LLM providers including ChatGPT, Claude, Gemini, Llama, and Mistral. Self-hosting uses a Next.js setup wizard that configures authentication via GitHub, Google, or Azure AD, with PostgreSQL as the recommended database. CLI access, an MCP server, and a Claude Code plugin extend its reach into developer workflows. The codebase is MIT-licensed while prompt data falls under CC0. Its 166k GitHub stars make it an AI resource on the platform with 166k GitHub stars, and it has been cited by Harvard, Columbia, and Forbes.

LLMs-from-scratch is Sebastian Raschka's GitHub companion to the Manning book Build a Large Language Model (From Scratch), ISBN 9781633437166. It walks through building, pretraining, and finetuning a GPT-style model in PyTorch across seven chapters and five appendices, plus a growing set of bonus notebooks covering newer open architectures.