educational

The author explains operator fusion as a key mechanism behind torch.compile's speedups, and provides a minimal 500-line Python implementation and notebook as an educational tool.

A tweet promoting a free deep learning resource with 68 interactive Python notebooks covering topics from basics to advanced techniques like GANs and diffusion models, ideal for self-learners.

A tweet promotes Stanford's free CS324 course on large language models, which uses a simple example of a mouse eating cheese to explain how LLMs work, and includes interactive demos.

Tweet announces a RAG Playlist covering topics from basic RAG to advanced techniques like CRAG and Self-RAG using LangChain and LangGraph, with a link in the comments.

Promotes Chip Huyen's AI Engineering companion GitHub repo, which is touted as more valuable than expensive AI courses.

Microcrad reimplements Karpathy's micrograd autograd engine in C, providing an educational scalar-valued automatic differentiation library with reference counting and a small neural network, aimed at understanding backpropagation at the scalar level.

A beginner-friendly GitHub repository covering PyTorch fundamentals, including tensor initialization, operations, indexing, and reshaping, with over 900 stars.

An interactive article explaining the intricate mechanics of a mechanical watch movement, from the mainspring to the timekeeping system.

A fully functional 8-bit Harvard architecture CPU built from individual logic gates, designed in Logisim-Evolution, with open-source files and documentation. Created by second-year EE students.

A repository that builds a GPT-style transformer from scratch without high-level libraries, covering everything from data preprocessing to generation, and includes guides for SFT and RLHF.

Built an MCP server allowing Claude to control NetLogo for agent-based modeling, including headless sweeps and model loading from CoMSES Net.

AgentSwarms launched 67 free, TypeScript-based interactive notebooks for learning multi-agent systems, covering LangChain fundamentals to production-grade error handling and failure modes.

Introduces the Machine Learning Visualized project on GitHub. This tool uses interactive notebooks and visualizations to show the training process of machine learning algorithms (e.g., neural networks, logistic regression, etc.), helping beginners understand the principles.

The author built Joey, a 170M parameter masked diffusion language model from scratch, trained on FineWeb-Edu and fine-tuned on DailyDialog, achieving fluent but incoherent sentences due to capacity limitations. The project highlights the differences from autoregressive LLMs and the lessons learned from building and debugging the system.

A hands-on tutorial series that bridges the gap between understanding async Rust internals (Future, poll, Pin, executor) and shipping real async code with Tokio, aimed at developers familiar with JavaScript async and basic Rust.

A Twitter thread listing 35 essential system design concepts with links to detailed explanations, aimed at helping developers learn and review key topics.

A detailed walkthrough of how transformer-based LLMs work, covering tokenization, embeddings, attention, and next-token prediction without heavy math.

This blog post explains Large Reasoning Models (LRMs), how they differ from standard LLMs, their training, and when to use them. It covers examples like DeepSeek R1 and GPT-5.5 Thinking.

A tweet curating foundational resources for understanding modern AI, covering topics from transformers to physical AI, including key papers and models.

CMU Software Engineering Institute publishes an overview of ML training infrastructure, covering hardware considerations like GPU vs CPU and memory requirements.