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A full educational series on local LLMs, covering inference, tokens, weights, and system-level understanding for beginners and reference.
A comprehensive guide to AI agents, covering the basics, the ReAct loop, task decomposition, context engineering, and the autonomy spectrum, aimed at both beginners and those building production systems.
Sebastian Raschka added a from-scratch implementation of DeepSeek Sparse Attention (DSA) to the LLMs-from-scratch educational repository, including motivation, overview, and a GPT-style reference implementation.
Google DeepMind chip engineer Reiner Pope delivers a comprehensive whiteboard explanation of how chips work, covering logic gates to systolic arrays and the human brain, in a free YouTube video.
This Twitter thread highlights Stanford CS221 lecture 6 on heuristics, explaining how A* search improves agent efficiency by using heuristics to guide decision-making. Key takeaways include building heuristics by relaxing problems, the danger of bad heuristics, and the optimality of A* with the right estimate.
A developer created a free 40-minute breakdown explaining 20 key AI concepts behind models like Claude and ChatGPT, covering tokenization, attention, RAG, agents, and more, aiming to provide practical mental models for builders.
Introduces a Rust macro that suppresses borrow checker errors for educational purposes, warning it is unsafe for production use.
An X thread highlights that Anthropic pays engineers $500K+ to deploy what Percy Liang teaches for free in Stanford CS221 Lecture 3, explaining how watching it changes understanding of Claude's behavior.
A tweet recommends a clear explanation of transformers, urging readers to read it twice.
Stanford provides an explanatory document on Artificial Intelligence & Machine Learning, available via Google Drive.
An archival version of John Regehr's C integers quiz, wrapped in JavaScript for browser compatibility, highlighting tricky undefined behavior and integer issues in C code.
An open-source book that builds the mathematical foundations of large language models, covering linear algebra, calculus, probability, and transformer architectures, with over 1000 pages of clear explanations and practical examples.
A tweet announces a GitHub repository that decodes Claude Code's architecture using source maps, providing an educational 18-chapter book on the internals of Anthropic's AI coding agent.
This paper presents MiniGPT, a compact from-scratch implementation of GPT-style autoregressive language modeling in PyTorch, built after studying nanoGPT. It evaluates the model on the Tiny Shakespeare dataset using character-level tokenization, achieving a validation loss of 1.4780 with a 10.77M-parameter configuration.
A tweet highlights that Citadel pays quants $800K/year for skills in probability theory and PDEs, and recommends an MIT lecture on stochastic differential equations.
Discovered an open-source GitHub project hello-agents, which organizes a complete open-source course from theory to practice on AI Agents, covering core skills like Agentic RL, SFT, GRPO, and has reached the top of GitHub trending.
A free interactive tool called Transformer Explainer runs a live GPT-2 model in the browser, visualizing the internal workings of Transformers with a Sankey diagram and live inference.
A collection of 333 hours of Q&A footage with astronauts, offering extensive educational content about space and astronaut life.
This article from Harvard's CS 61 course covers Unix concepts of pipes, forks, and zombies, explaining how pipes automatically kill programs on closure and how to use pipes to implement blocking waits on child processes.
A blog post guides readers through setting up a Raspberry Pi cluster for distributed training and inference, part of a series aimed at making distributed AI accessible using affordable hardware.