A curated library of the best AI learning resources—courses, tutorials, docs, and talks on agentic AI, MCP, Agent Skills, RAG, machine learning, fine-tuning, models, and frameworks, for AI engineers and technical teams.
Twelve chapters on instructions, retrieval, tools, memory, evaluation and governance — in Python, JavaScript and Java.
Stop parsing and retrying JSON — constrain decoding so the model cannot emit an invalid output.
The RAG tutorial that teaches ranking — phased retrieval, recall evaluation, and a trained first-phase ranker.
One Markdown file that every coding agent reads — replace your .cursorrules sprawl in an afternoon.
The standard PyTorch toolkit for image, video and audio generation — inference, LoRA adapters, quantization and ten maintained training scripts.
Ten worked kernels — from vector add to fused attention — that teach GPU programming at block level in Python instead of CUDA.
The resources learners opened most this week.
The reference for ZeRO, offloading and 3D parallelism — how to train or fine-tune a model that does not fit on your GPUs.
One storage layer for vector search, full-text search, versioning and training-data loading — instead of a vector DB bolted beside your data lake.
The open spec for letting an agent spend money for a user — and cryptographically prove it was authorized.
One decorator to trace an LLM app, and the same scorers for offline evals, live monitors and guardrails.
The full training account of a 229.9B-parameter MoE with 9.8B active — built end-to-end for agentic work.
Build your own coding agent — sandboxed execution, custom tools and multi-LLM routing, all documented.
Trace it, score it, gate it in CI — the whole LLM evaluation loop, self-hostable under Apache-2.0.
The post-training stack behind Nemotron — one codebase from a single GPU to thousand-GPU GRPO runs.
Stand up a self-hosted, citation-backed document QA system over messy enterprise PDFs in an afternoon.
The missing protocol layer between your agent backend and the screen a human is actually looking at.
Score your terminal agent against the benchmark everyone quotes, and learn how the harness decides pass or fail.
The GPU-accelerated pipeline NVIDIA uses to clean, dedupe and synthesize training data at trillion-token scale.
Turn prompt tweaking into a test suite, then attack your own agent before someone else does.
Derive flow matching and diffusion from the SDEs up, then build a latent diffusion model from scratch.
One API for distributed data, training, tuning and serving — the compute layer under most in-house AI platforms.
The official recipe book for quantizing an LLM into a format vLLM serves — FP8, INT4, NVFP4 and MXFP4.
Song Han's MIT course on making models small and fast — pruning, quantization and LLM deployment, with five labs.
Run RAG retrieval inside the Postgres you already operate — HNSW indexes, metadata filters and hybrid search, in SQL.