Notebook-first deep learning textbook that teaches concepts through runnable multi-framework code, math, and exercises. Includes lecture-ready notebooks, community contributions, and broad university adoption—designed for hands-on learners and instructors.
AI Train2019
Implements deep RL algorithms (PPO, DQN, SAC, TD3, DDPG, C51, PPG) as standalone single-file scripts — the PPO Atari variant is ~340 readable lines. Built for research debugging and reproducibility, with W&B and TensorBoard tracking.
Collects 60+ PyTorch implementations of neural network papers — transformers, diffusion, GANs, RL, optimizers — each annotated line-by-line and rendered beside the code at nn.labml.ai, so you study the math and a runnable implementation together.
AI Dataset2022
Provides 115M public GitHub source files (≈873GB of code, ~1TB uncompressed) with per-file metadata (repo, path, language, license). Supports streaming, language/license filtering and full download for training and evaluating code LLMs and code generation models.
Hands-on lecture series that teaches neural networks from first principles up to building a GPT: each lecture pairs a YouTube video with Jupyter notebooks and exercises so you code models (micrograd → MLPs → WaveNet-like convs → GPT) while learning training and debugging.
AI Train2022
Reproduces GPT-2 (124M) from scratch on OpenWebText in ~4 days on an 8xA100 node, with the whole stack kept to two ~300-line files: train.py for the loop and model.py for the architecture. A char-level Shakespeare run finishes in ~3 minutes on one GPU.
AI Agent2023
Builds no-code automations with TypeScript-based integrations, AI pieces, human-in-the-loop steps, and MCP exposure for community and product workflows.
AI Client2023
Self-hosted AI coding assistant you run on your own hardware as an alternative to cloud Copilot. Offers context-aware completion, an in-IDE answer engine and chat, using RAG over your repositories so suggestions match your team's code.
AI Agent2023
Runs local-first AI coding agents in a native desktop workspace to inspect, edit, and execute code with per-session model selection, plugin extensibility, and an approval-based permission layer — keeping projects, keys, and transcripts on your machine.
Chatbot2023
Pulls context from your whole codebase via Sourcegraph's search API to power chat, autocomplete, and edits across VS Code, JetBrains, and the CLI. Now ships only inside Sourcegraph Enterprise; the free and Pro tiers are retired.
AI Client2023
Edits code across an existing repo from the terminal: you describe a change in plain English, it maps the whole codebase, applies edits to the right files, and auto-commits each change as a reviewable git commit. Works with most LLMs.
Builds a GPT-style LLM in PyTorch step by step — tokenizer, attention, pretraining, and finetuning — with no external LLM frameworks. Companion code to a Manning book, with bonus chapters on LoRA and modern Llama/Qwen-style architectures.