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Discover useful tools, tutorials, and helpful resources organized by category
Hamel's Blog is written by Hamel Husain, a machine learning engineer with more than 20 years of experience who has worked at Airbnb and GitHub. The notes cover applied AI engineering, machine learning and data science, with a strong focus on evals: how teams debug, analyze and measure LLM systems. Long-form posts include A Field Guide to Rapidly Improving AI Products, Stop Saying RAG Is Dead, Selecting The Right AI Evals Tool and "It's Hard to Eval" Is a Product Smell, and the blog stays current with AI Evals: Everything You Need to Know and Claude's new auto eval tool from September 2026. A practical source for engineers who ship LLM features and need to know whether they work.
Chip Huyen's blog covers AI system design and what it takes to bring AI into production. Huyen previously worked on ML tooling at NVIDIA, Snorkel AI and Netflix, taught ML Systems at Stanford, and wrote the books Designing Machine Learning Systems and AI Engineering. The posts are long, structured write-ups such as Building A Generative AI Platform, which starts from the simplest architecture and adds components step by step, Agents, and Common pitfalls when building generative AI applications. A good fit for engineers who build products on top of foundation models and want architecture-level guidance.
colah's blog is the personal site of Christopher Olah, who works on reverse engineering artificial neural networks into human understandable algorithms and is a co-founder of Anthropic and of the journal Distill. The index groups his essays by theme: neural networks in general, recurrent networks, convolutional networks and visualizing neural networks, with pieces such as Understanding LSTM Networks, Calculus on Computational Graphs: Backpropagation, Understanding Convolutions and Neural Networks, Manifolds, and Topology. It also links his later interpretability work, including the Circuits thread on Distill and Transformer Circuits. A strong starting point for developers who want intuition for how networks compute, not just how to train them.
Eugene Yan, a Member of Technical Staff at Anthropic, writes about building AI-powered products: recommendation systems, large language models, evaluation, engineering practices and leadership. His posts are practical and grounded in shipping real systems, with recent pieces on patterns for building cybersecurity evals, using LLMs to secure source code, how to work and compound with AI, and product evals in three simple steps. A strong resource for engineers who need to move LLM features from prototype to a reliable product.
Ahead of AI is the newsletter of Sebastian Raschka, PhD, focused on machine learning and LLM research for researchers and practitioners. Posts walk through new model architectures and research papers in depth, with recent issues covering LLM architecture developments such as KV sharing, mHC and compressed attention, controlling reasoning effort, local coding agents, building an AI text detector from scratch, and a curated list of the 2026 LLM research papers. It is written by the author of Build a Large Language Model (From Scratch) and suits developers who want to keep up with research without reading every paper themselves.
The zartbot/blog repository functions as a technical knowledge base and discussion forum primarily focused on cloud-native networking, high-performance computing (HPC), and system-level optimization. The issues section serves as a collection of long-form articles and engineering deep dives where the author, zartbot (a notable engineer in the networking and AI infrastructure space), explores advanced topics such as SD-WAN architecture, RDMA/RoCE virtualization, BGP and cloud-scale routing protocols, and GPU networking for AI training. By leveraging GitHub Issues as a blogging platform, the repository facilitates technical discourse on specialized subjects like NetDAM (Network Direct Attached Memory) and the trade-offs of the CAP theorem in modern distributed systems, catering to a professional audience of network architects and system programmers.
Lil’Log (lilianweng.github.io) is the personal blog of Lilian Weng, where she documents her explorations in AI, machine learning, and deep learning research. She publishes well-researched, thoughtful essays on topics like large language models, diffusion models, generative AI, hallucination in LLMs, reinforcement learning, model inference, and more. The posts often include background theory, recent progress, tradeoffs, and references to academic papers — making it a bridge between cutting-edge research and accessible discussion for technical audiences.
Shunyu Yao’s site showcases his work as a researcher at OpenAI, focusing on “agents” — systems that perceive, reason, and take actions in digital environments. The site features his selected publications, including papers such as ReAct, Tree of Thoughts, Deep Research, and SWE-agent, along with links to repo, talks, benchmarks, and blog posts. It reflects an overarching interest in how language models can be structured not just to predict text, but to act, interact, evaluate, and generalize — moving toward agents that integrate reasoning and action. The writing and work are forward-looking, engaging questions about what constitutes evaluation, how digital environments should be designed, and what the “second half” of AI might look like.
At Anthropic, engineering centers on building AI systems that are reliable, interpretable, and steerable—especially through things like agents and tool integrations. Their team publishes about how they enable Claude (their AI model) to use tools well, optimize performance, and evaluate tools rigorously. They also talk about internal research efforts like scaling interpretability, making features behaving predictably, and improving developer workflows. Their engineering blog shows not just what they build, but how they think about safety, feedback loops, and tooling to support clean, robust, agent-based AI systems.
Thinking Machines Lab is an AI research & product company founded in 2025 by Mira Murati. Their blog—titled “Connectionism”—serves as a public window into their work, promising technical posts, research, and code releases. Their mission is to make frontier AI systems easier to understand, customize, and reliably determinable (e.g. tackling nondeterminism in inference), while emphasizing human-AI collaboration, model intelligence, infrastructure quality, and safety.
Simon Willison—co-creator of the Django web framework and creator of the open-source tool Datasette—has been blogging since 2002 about web development, data journalism, AI tooling, and LLMs. His site features fast, insightful entries—including project demos like EmbeddingGemma and semantic tools like the “Semantic Galaxy”—plus thoughtful reflections on topics like prompt engineering and AI agents. Willison also publishes both free and paid newsletters, maintains all his posts on a custom Django-powered site, and continues contributing to open-source and tech communities.
Andrej Karpathy’s Blog is the personal website of Andrej Karpathy, an AI researcher and educator known for his work in deep learning and large language models. The blog features a mix of technical content, personal reflections, and educational resources. Notably, Karpathy offers a self-hosted, minimalist blog experience, emphasizing simplicity and directness in his writing. His posts cover topics such as the philosophy of technology, personal anecdotes, and insights into AI development. Additionally, Karpathy has launched Eureka Labs, an AI+Education company, and shares educational videos on his YouTube channel, focusing on deep learning and large language models.