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A tweet promoting a GitHub repository containing over 300 real ML system design case studies from major companies like Google, Amazon, Microsoft, and Netflix, aiming to teach how production ML systems are actually built.
Kyle Kingsbury shares a free outline for a 16-32 hour distributed systems fundamentals class, covering theory, algorithms, and practical production concerns, with optional labwork via Maelstrom.
A set of four cards covering the core concepts of neural networks: neuron, forward pass, activations, and backpropagation, aimed at helping learners understand how models from perceptrons to transformers work.
GPU Mode is a learning resource featuring a YouTube series, GitHub repo with slides/notebooks, and a practice website for mastering CUDA programming.
Introducing the codecrafters-io/build-your-own-x repository, a collection of tutorials for building various technologies from scratch, helping developers understand underlying principles through hands-on practice.
Introduces an open-source course called 'AI Engineering from Scratch', containing 503 lessons, covering from linear algebra to autonomous swarms, with special emphasis on Agent Engineering and Multi-Agent phases.
A tweet recommending the 'Hands-on Modern RL' website as the best resource to learn reinforcement learning from scratch, with a link to a chapter on BipedalWalker.
An interactive 3D step-by-step guide to learning how LLMs work, covering key transformer concepts like embedding, self-attention, and softmax. It recommends a visual approach over reading papers.
A comprehensive system design master tree covering fundamentals through real-world applications, including architecture patterns, databases, caching, messaging systems, API design, and deployment strategies. Intended as a structured learning guide for software engineers.
A Python learning repo that reverse-engineers Claude Code-style agent architecture through 23 incremental sessions, covering planning, subagents, context management, and more.
A tweet recommends the Language AI Handbook, a free online resource that covers LLM components from classical NLP to modern transformers, quantization, RL, and safety.
A free, open-source AI engineering curriculum that covers math, LLMs, and agents across 20 phases and 435 lessons in Python, TypeScript, Rust, and Julia, designed to fill gaps in fragmented AI tutorials.
This post shares a curated GitHub repository containing over 30 practical AI projects, covering domains from regression to generative AI, with many end-to-end examples, suitable for learners and developers.
A project-based course repository on Harness Engineering for AI coding agents, covering environment setup, state management, verification, and control mechanisms to make AI coding agents work reliably. The course synthesizes best practices from OpenAI and Anthropic on building effective harnesses for long-running agents.