Your AI second brain. Self-hostable. Get answers from the web or your docs. Build custom agents, schedule automations, do deep research. Turn any online or local LLM into your personal, autonomous AI (gpt, claude, gemini, llama, qwen, mistral). Get started - free.

A complete guide to start and improve your LLM skills in 2026 with little background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

Streamlit LLM app examples for getting started

JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).

Cuckoo is a Decentralized AI Model-Serving Platform, starting with GPU-sharing for text-to-image generation and LLM inference.

The Official Repo for "Quick Start Guide to Large Language Models"

The fastest way to build and start training your own LLM. CLI tool that scaffolds production-ready PyTorch training projects in seconds. Like create-next-app but for language models.

Explorations into training LLMs to use clinical calculators from patient history, using open sourced models. Will start with Wells' Criteria

This repository introduces and helps organizations get started with building Intelligent Apps and incorporating Large Language Models (LLMs) via AI Orchestration into them.

Curated collection of AI dev tools from YC companies, aiming to serve as a reliable starting point for LLM/ML developers

User friendly CLI tool for AI tasks. Stop thinking about LLMs and prompts, start getting results!

🤖 Get started with LLMs on your kind cluster, today!

🐤 A minimal viable logger for Prompt/LLM Engineering. Use your IDE as Logging UI - a fast, simple, extensible, zero dependency Node.js logging tool that starts simple in development and is easily extended for production!

Reliable LLM outputs start with clean context. Deterministic deduplication, compression, and caching for RAG pipelines.

Centralize ESP32 related commands and simplify getting started with seamless, LLM-driven interaction and help.

Start running LLMs, vision models, and camera pipelines in under an hour. This fully-configured single board computer comes with a bootable NVMe pre-loaded with Ollama, Llama.cpp, Roboflow vision inference, various LLM and VLM models, and 20+ applications ready to run. Save 40-120 hours of setup time.

Reticle intercepts, visualizes, and profiles JSON-RPC traffic between your LLM and MCP servers in real-time, with zero latency overhead. Stop debugging blind. Start seeing everything.

A Cookbook to start building with LLMs

Local LLM Notepad is an offline plug-and-play app for running local large-language model. Drop the single bundled .exe onto a USB stick, walk up to any computer, and start chatting, brainstorming, or drafting documents. No installation, no internet, no admin rights required.

Nodes: Wildcard Processor, Get File Path, Save Text File, Download Image from URL, Tiktoken Tokenizer, String Cleaning, String Text Splitter, String Text Extractor, Format Date Time, Load Text-Image Pairs, Metadata Extractor, Audio Visualizer, Load Image Advanced, Colorful Starting Image, Groq LLM, VLM, ALM API

The AVR Infrastructure project is designed to launch the Agent Voice Response application, which will start the Core, ASR, LLM, and TTS services integrated with Asterisk Audiosocket.

LLM Inference via Triton (Flexible & Modular): Focused on Kernel Optimization using CUBIN binaries, Starting from gpt-oss Model

LLM Prompt Testing Quick Start

Simple starting point function to host LangChains with LLMs and other tools in an Azure Function.

Repo that contains resources to learn or get started with Large Language Models (LLMs) and AI in General

Blueprints is an open-source tool that integrates with your text editor to help you generate code with an LLM based on patterns you already have in your codebase. Download one of our official editor plugins to get started.

Repository for all the code and notebooks for the O'Reilly live-training: "Getting Started with LLM Agents using Langchain"

A ground-up LLM engineering project: tokenizer → architecture → training → scaling laws → inference. Starts at 80M, engineered to scale into 1B+ models with minimal changes. Clean, research-ready code for anyone serious about understanding and building LLMs from first principles.

An MCP server for Google Tag Manager. Connect it to your LLM, authenticate once, and start managing GTM through natural language.

Simple getting-started code examples for LLM applications powered by OctoAI