Links

OpenAI's flagship consumer product. GPT-4o and GPT-5 models available. Strong at creative writing, analysis, and general knowledge. The free tier is generous. Pro tier ($20/mo) unlocks the strongest models and longer conversations. Has plugins, vision, DALL-E image generation, and browsing built in.

Anthropic's consumer chat interface for Claude models. Known for strong reasoning, careful analysis, and excellent code generation. The Pro plan ($20/mo) gives access to Claude Opus — widely considered the best model for complex coding and technical work. Supports file uploads, projects (persistent context), and artifacts (interactive outputs).

Anthropic's CLI tool that turns Claude into an autonomous coding agent. It reads your codebase, edits files, runs commands, and commits to git. Supports hooks, routines (scheduled tasks), MCP servers, and custom skills. This is what we use to build this website. Available on Max plan ($100/mo) or via API.

Google DeepMind's AI assistant. Strong at multimodal tasks (text, images, video, code). Free tier available. Deep integration with Google Workspace (Docs, Gmail, Drive). Gemini 2.5 Pro is competitive with the best models on reasoning and coding benchmarks. Notable for its massive context window (up to 1M tokens).

Elon Musk's xAI chatbot, integrated into X (Twitter). Known for having fewer content restrictions than competitors. Access to real-time X data. Grok-3 is competitive on coding and math benchmarks. Available with X Premium+ subscription.

AI search engine that answers questions with cited sources. Combines web search with LLM reasoning — gives you answers, not just links. Great for research questions where you want sources you can verify. Free tier is usable. Pro ($20/mo) gives access to stronger models and more searches per day.

Meta's free AI assistant powered by Llama models. Integrated into WhatsApp, Instagram, and Facebook. The Llama models are open-source, which means you can also run them locally. Less powerful than Claude or GPT-4 for complex tasks, but completely free and widely accessible.

Mistral AI's free chat interface. French AI company known for efficient, high-quality models that punch above their weight. Le Chat gives access to their latest models including Mistral Large and Codestral. Strong at multilingual tasks and coding. No account required for basic use. A good alternative when you want a different perspective from Claude or ChatGPT.

Terminal-based AI coding agent by Anthropic. It understands your entire codebase, makes multi-file edits, runs tests, and commits to git autonomously. Supports parallel agents (worktrees), custom skills, MCP servers for tool integration, and scheduled routines. The tool we use daily for this project.

GitHub's AI code completion tool, powered by OpenAI models. Works inside VS Code, JetBrains, and other editors. Suggests code as you type, explains code, generates tests, and can chat about your codebase. $10/mo for individuals. Free for students and open-source maintainers.

A VS Code fork rebuilt around AI. Has inline code generation, multi-file editing (Composer), and chat that understands your project. Supports Claude, GPT, and other models. Popular in the vibe coding community. Free tier available, Pro at $20/mo. Main competitor to Claude Code for AI-assisted development.

Open-source terminal tool for AI pair programming. Works with Claude, GPT, and local models. Understands git, makes commits, and can work across multiple files. Lighter weight than Claude Code. Great option if you want AI coding help with model flexibility and full control over your workflow.

Vercel's AI tool that generates React/Next.js UI components from text descriptions. Describe what you want and it produces working code with Tailwind CSS and shadcn/ui. Excellent for quickly prototyping frontends. Free tier available. Useful when you know what you want visually but don't want to write the CSS yourself.

Browser-based IDE with AI built in. Write, run, and deploy code without local setup. The AI agent can build full applications from descriptions. Supports many languages. Good for quick experiments and prototyping. Free tier for basic use, paid plans for more compute and features.

Official docs for building with Claude via API. Covers messages API, tool use, vision, embeddings, and batch processing. Well-written with practical examples. The Claude SDK is available for Python and TypeScript. Important: this is where you learn about features like extended thinking, system prompts, and structured outputs.

Hands-on Jupyter notebooks showing real use cases: RAG pipelines, function calling, multi-step agents, citation extraction, and more. Better than the docs for understanding how to actually build things with Claude. Start here if you want to see what's possible before diving into the API reference.

Official docs for GPT-4, GPT-5, DALL-E, Whisper, and other OpenAI models. Covers chat completions, assistants API, function calling, fine-tuning, and embeddings. The most widely-used AI API — most tutorials and examples online use it. Pay-per-use pricing, no subscription required.

Official collection of practical examples and guides for building with OpenAI. Covers RAG, function calling, embeddings, fine-tuning recipes, and integration patterns. Good companion to the API docs — shows the 'how' and 'why' behind common patterns.

Google's Gemini API docs. Notably generous free tier for developers. Supports text, vision, audio, and code generation. The AI Studio playground lets you experiment before writing code. Gemini's 1M+ token context window is unique — useful for processing entire codebases or long documents.

xAI's developer API for Grok models. Relatively new entrant. Compatible with OpenAI's API format, making it easy to switch. Competitive pricing. Notable for having access to real-time X/Twitter data through the API.

French AI company known for efficient, high-quality models. Their open-weight models (Mistral 7B, Mixtral) are popular for local deployment. The API offers both open and proprietary models. Good balance of cost and capability. Their coding model (Codestral) is specifically tuned for development tasks.

Enterprise-focused NLP API. Strong in embeddings (for semantic search and RAG), reranking, and text classification. Less general than Claude or GPT but excellent for specific NLP tasks. Their Command models are good for enterprise AI applications. Generous free tier for experimentation.

Documentation for the Hugging Face ecosystem — the largest open-source AI platform. Covers the Transformers library (run any open model), Datasets (training data), PEFT (efficient fine-tuning), and more. Essential reference for anyone working with open-source AI models. The model hub hosts 500K+ models.

Human-curated tech news aggregator that surfaces the most important stories of the day. Not AI-only, but AI dominates the feed right now. Great for getting a quick overview of what's happening in tech without wading through noise. The clustering of related stories is excellent.

Community-driven tech news and discussion. The comments are often more valuable than the articles — experienced engineers and researchers weigh in on every major AI development. Check the front page daily for the most important AI stories. Use /best for the highest-quality discussions.

The most serious ML subreddit. Actual researchers post and discuss papers here. Good for keeping up with new research, benchmark results, and technical discussions. The [D] tag marks discussion threads, [R] marks research papers, [P] marks projects. Higher signal-to-noise ratio than most AI communities.

The community for running AI models on your own hardware. Discussions about quantization, hardware requirements, new model releases, and optimization techniques. Essential if you want to run models locally with Ollama or llama.cpp. Very practical, focused on what actually works.

Ars Technica's AI section offers well-researched, technically accurate journalism. Their reporters understand the technology and don't just rewrite press releases. Good for understanding the implications of AI developments, not just the announcements.

The Verge covers AI from a product and industry perspective. Strong on company news, product launches, and the business side of AI. Less technical than Ars Technica but better at covering the broader impact and competitive dynamics between AI companies.

MIT Technology Review's AI coverage combines academic rigor with accessible writing. Covers research breakthroughs, ethical implications, and long-term trends. Particularly good for understanding where AI research is heading, not just where it is today. Some articles are paywalled.

The premier podcast for AI engineers. Swyx interviews founders, researchers, and engineers building with AI. Deep technical conversations about architecture decisions, scaling challenges, and what actually works in production. Essential listening for anyone serious about AI engineering.

Free daily email with the top AI news, research papers, and tools. Each item gets a 2-3 sentence summary. Takes about 5 minutes to read. The best way to stay current on AI developments without spending hours on Twitter and Reddit. Also has newsletters for other tech topics.

Weekly newsletter by Andrew Ng (Stanford professor, Google Brain/Coursera co-founder). Curates the most important AI news with Ng's personal analysis. More measured and educational than most AI media. Good for understanding significance rather than just following the hype cycle.

Our own AI-generated daily AI news podcast. 15-minute daily briefing on the most important AI stories. Quick way to stay current while commuting or during a coffee break.

Our own AI-generated weekly AI news podcast. 20-minute weekly roundup of the biggest AI stories. Covers the week's developments with more depth than the daily version.

Jeremy Howard's practical deep learning course. Famous for its top-down teaching approach — you build working models from day one, then go deeper into the theory. Free, no prerequisites beyond basic Python. The fastai library makes state-of-the-art techniques accessible. Highly recommended starting point for the father's AI learning journey.

Andrew Ng's platform with courses ranging from ML basics to specialized topics like LLMs, agents, and fine-tuning. The short courses (1-2 hours each) are excellent for quickly understanding specific topics. The Machine Learning Specialization is the gold standard introduction. Most content is on Coursera but some short courses are free on the site.

Free courses by Hugging Face covering NLP, transformers, reinforcement learning, and audio processing. Hands-on with code examples using the HF ecosystem. Good for understanding how to work with open-source models. The NLP course is particularly strong as an introduction to transformer-based language models.

Andrew Ng's Stanford Machine Learning course, recorded and free on YouTube. More mathematical and rigorous than the Coursera version. Covers the foundations: linear regression, neural networks, SVMs, clustering, dimensionality reduction. Good for building solid theoretical understanding alongside the UvA curriculum.

Andrew Ng's updated ML specialization on Coursera. Three courses covering supervised learning, advanced algorithms, and unsupervised learning. Uses Python (the original used Octave). Can audit for free. The most popular ML course in the world — a reliable foundation that millions of people have used.

Community-maintained guide to prompt engineering techniques. Covers chain-of-thought, few-shot learning, retrieval-augmented generation, and model-specific tricks. Essential reading for getting the most out of AI models. Both the father (for practical use) and son (for understanding capabilities) will find this valuable.

Sebastian Raschka's book companion repository. Walks you through building a GPT-like language model from scratch in PyTorch — tokenization, attention, training, fine-tuning. The best resource for truly understanding what's happening inside an LLM. Code-heavy and hands-on. Perfect for bridging the son's theory with the father's engineering instincts.

Tutorial for building Model Context Protocol servers — the standard that lets AI tools like Claude Code connect to external services. Learn how to give AI agents access to your databases, APIs, and tools. A practical skill for building AI-powered workflows.

Anthropic's official guide to building AI agents. Covers routing, tool use, planning, reflection, and multi-agent orchestration patterns. Essential reading for understanding how to structure AI systems that can take actions autonomously. Directly applicable to building with Claude Code and the Claude API.

Founded by Peter Thiel, Palantir builds data integration and AI platforms for defence and intelligence agencies. Their AIP platform lets military users deploy AI models on classified data. One of the most successful defence-tech companies. Their Foundry platform is also used in commercial settings.

Founded by Palmer Luckey (Oculus VR creator). Builds autonomous defence systems including drones, counter-drone systems, and the Lattice AI platform for battlefield awareness. Represents the new wave of Silicon Valley defence startups. Their approach: bring tech company speed and AI-first design to defence.

Builds AI pilots that can fly military aircraft and drones autonomously without GPS or communications. Their Hivemind system enables swarm autonomy. One of the most technically ambitious defence AI companies. Demonstrates where embodied AI meets real-world high-stakes applications.

One of the largest defence contractors, increasingly integrating AI into communications, surveillance, and electronic warfare systems. Traditional defence company adapting to the AI era. Shows how established players are incorporating AI alongside startups like Anduril.

DARPA's AI research initiative. The agency that funded the creation of the internet is now pushing the frontiers of AI for defence. Funds research in explainable AI, adversarial AI, and autonomous systems. Their research often becomes commercially available 5-10 years later. Good for understanding where AI research is heading.

The world's largest code hosting platform. Where virtually all open-source AI projects live. Free for public repositories. Essential for exploring AI tools, contributing to projects, and hosting your own work. GitHub Actions provides free CI/CD for public repos.

Open-source AI gateway that routes requests across multiple AI providers (OpenAI, Anthropic, Google, etc.) through a single API. Useful for building applications that need to work with multiple models or switch between providers. Simplifies multi-model architectures.

The npm of AI models. Hosts 500K+ models, 100K+ datasets, and 200K+ demo apps (Spaces). You can download and run any open model, share your own, and explore what others have built. The Transformers library is the standard way to use open-source AI models in Python. Essential platform for the AI ecosystem.

The easiest way to run AI models on your own machine. One command to download and run Llama, Mistral, Gemma, and many other models. No cloud needed, everything runs locally. Great for experimentation, privacy-sensitive work, and understanding how models work without API costs. Works on Mac, Linux, and Windows.

The most popular framework for building LLM-powered applications. Provides abstractions for chains, agents, retrieval (RAG), memory, and tool use. Supports all major model providers. Can feel over-engineered for simple tasks, but invaluable for complex AI applications. LangGraph (their agent framework) is particularly useful.

The engine behind local AI inference. Runs quantized LLMs on CPU and GPU with remarkable efficiency. Powers Ollama and many other local AI tools. Understanding llama.cpp means understanding how AI models actually run on hardware — quantization formats (GGUF), context management, and performance optimization.

Open-source AI pair programming in your terminal. Supports many models (Claude, GPT, local models). Makes clean git commits, understands project context, and can edit multiple files. Lighter than Claude Code, more flexible in model choice. Good alternative when you want more control over which AI model you use.

Lets you control your computer through natural language. It can run Python, JavaScript, shell commands, and more — all from a chat interface. Think of it as an AI that can actually execute code on your machine. Supports Claude, GPT, and local models. Good for automation and one-off tasks.

Framework for building teams of AI agents that collaborate on tasks. Define roles (researcher, writer, reviewer), give them tools, and let them work together. Good for understanding multi-agent patterns and building complex AI workflows. Simpler to get started with than LangGraph for multi-agent use cases.

LMSYS's blind comparison platform. Users chat with two anonymous models and vote for the better response. The resulting Elo rankings are the most trusted measure of real-world model quality — more meaningful than cherry-picked benchmarks. Check this first when a new model claims to be 'the best'.

Hugging Face's leaderboard for open-source models. Ranks models on standardized benchmarks (MMLU, reasoning, math, coding). Useful for choosing which open model to run locally. Updated frequently as new models are released. Filter by size to find models that fit your hardware.

Independent benchmarks comparing AI models on speed (tokens/second), cost (price per token), and quality. The best resource for understanding the practical trade-offs between models when building applications. Their charts make it easy to see which model gives the best quality for your budget.

Tracks state-of-the-art results across thousands of ML tasks. Every paper linked to its code and datasets. The best way to find what's actually working best for any specific AI task (image classification, text generation, translation, etc.). Great for research and understanding the current frontier.

The book we're currently reading. Covers the practical side of building with AI — evaluation, deployment, orchestration, and production concerns. Not about how models work internally, but how to engineer systems that use them. Most-read book on O'Reilly since launch (Jan 2025). Relevant for both of us.

Chip Huyen's earlier book focusing on the full ML lifecycle — data engineering, feature engineering, training, deployment, monitoring. More focused on traditional ML systems than LLMs, but the engineering principles are universal. Good companion to AI Engineering for a complete picture.

Walk through building a GPT-like model from zero in PyTorch. Covers tokenization, attention mechanisms, training loops, and fine-tuning. The best way to truly understand what's inside an LLM. Code-heavy and hands-on. Perfect for bridging theory (son) with engineering (father).

A beautifully concise 150-page overview of deep learning by François Fleuret (University of Geneva). Free PDF. Covers the essential concepts without bloat — architectures, training, optimization, attention. Perfect reference to keep nearby. Dense but readable. Good for the son to review fundamentals and the father to build theoretical foundation.

The most complete guide to prompt engineering techniques. Covers basics through advanced methods like chain-of-thought, tree-of-thought, and retrieval-augmented generation. Community-maintained and regularly updated. Useful for both understanding how to communicate with AI (son's focus) and getting practical results (father's focus).

The complete reference for Claude Code — installation, configuration, CLAUDE.md files, permissions, memory, and advanced features. Start here for understanding what Claude Code can do. Covers the CLI, desktop app, and web interfaces.

Anthropic's official repository of Claude Code skills — reusable prompt packages that give Claude specialized capabilities (frontend design, document creation, API building, etc.). You can install these with /plugin and also create your own custom skills.

Claude Code Routines are persistent, autonomous agents that run on Anthropic's cloud on a schedule or in response to events. They can commit code, review PRs, run scripts, and manage repos without human intervention. Minimum interval: 1 hour. This is what we planned to use for the AI news feature.

Hooks let you run shell commands automatically in response to Claude Code events — before/after tool calls, on conversation start, etc. Useful for enforcing project rules (linting, formatting), triggering builds, or adding custom validation. Configured in settings.json.

How to connect external tools to Claude Code via the Model Context Protocol. MCP servers give Claude access to databases, APIs, file systems, and services. This is how we connected Cloudflare and other tools to our workflow. You can use existing servers or build your own.

The official specification for MCP — Anthropic's open standard for connecting AI models to external tools and data sources. Defines how tools, resources, and prompts are exposed to AI. Understanding MCP is key to building AI systems that can interact with the real world. Supported by Claude Code, Cursor, and others.

Community directory of available MCP servers. Browse and discover servers for databases, APIs, file systems, and cloud services. Good starting point when you want to connect Claude Code to a new tool — someone may have already built the server you need.

Microsoft's MCP server that gives AI agents browser automation capabilities via Playwright. Lets Claude Code navigate websites, fill forms, take screenshots, and extract data. We use this for testing our website with automated screenshots.

Cloudflare's MCP server that gives AI agents access to Cloudflare services — Workers, Pages, DNS, KV storage, and more. Since our website is hosted on Cloudflare Pages, this server lets Claude Code manage our infrastructure directly.

Official GitHub MCP server. Gives AI agents access to repositories, issues, pull requests, and GitHub Actions. Enables workflows like automated PR reviews, issue triage, and code search across repositories.

MCP server that provides AI with up-to-date documentation for libraries and frameworks. When Claude Code needs to know the latest API for a tool, Context7 fetches the current docs instead of relying on training data. Reduces hallucination about APIs and configuration options.

Former Tesla AI director and OpenAI founding member. His 'Neural Networks: Zero to Hero' series builds a GPT from scratch in Python. The best resource for understanding transformers from first principles. His teaching style bridges theory and practice perfectly — relevant for both father and son.

Grant Sanderson's channel with beautiful animated explanations of math concepts. His neural network and linear algebra series are legendary for building visual intuition. If you want to understand the math behind AI without getting lost in equations, start here. The animations make abstract concepts click.

Jeff Delaney's channel — 'X in 100 seconds' format explains technologies in rapid, entertaining videos. Covers AI tools, frameworks, and news with a developer's perspective. Good for quickly understanding what a new tool or technology does before deciding to dive deeper. Weekly AI news recaps.

Karoly Zsolnai-Feher explains cutting-edge AI research papers in short, accessible videos. Covers computer vision, generative AI, physics simulation, and more. Great for staying current on research breakthroughs without reading papers. His enthusiasm is infectious — 'What a time to be alive!'

Deep, technical paper reviews and ML news. More rigorous than most AI YouTubers — actually reads and explains the math in papers. Good for the son's academic understanding and the father's desire to go deeper. Also covers AI industry news and drama with informed commentary.

Philip's channel offers thorough, balanced analysis of major AI developments. Longer videos (20-40 min) that actually explain the significance of new papers, models, and benchmarks. Less hype, more substance. Good for understanding why a new development matters, not just what it is.

Practical AI tool reviews and tutorials. Covers new model releases, AI coding tools, local model setups, and workflow tips. Good for discovering new tools and seeing them in action before investing time. Frequent uploads covering the latest AI releases and how to use them.

Dave Plummer, retired Microsoft engineer (wrote Task Manager, among other things). Covers coding, AI, and tech history from the perspective of decades of engineering experience. The father will appreciate his veteran developer perspective on how AI is changing software development.

Google's professional certificate for ML engineering. Covers TensorFlow, ML pipelines, and deployment on Google Cloud. Recognized by employers. Coursera-based, self-paced. Good for validating practical ML skills. Takes about 3-6 months to complete. Provides Google Cloud credits for practice.

AWS's professional-level ML certification. Covers data engineering, modeling, deployment, and operations on AWS. More challenging than Google's cert. Good for anyone building ML systems on AWS infrastructure. Requires understanding of SageMaker, data pipelines, and model deployment patterns.

Andrew Ng's suite of specializations on Coursera. Covers ML fundamentals, deep learning, NLP, computer vision, and MLOps. The most recognized AI education brand. Can audit courses for free, pay for certificates. The TensorFlow and PyTorch specializations are particularly practical.

Microsoft's certification for building AI solutions on Azure. Covers Azure AI services, cognitive services, and responsible AI principles. Good if you're working in a Microsoft ecosystem. The learning path is free, only the exam costs money. Microsoft Learn provides excellent free study materials.

NVIDIA's hands-on training courses for deep learning and accelerated computing. Focused on GPU-accelerated AI — training, inference, and deployment. Good for understanding the hardware side of AI. Courses range from introductory to advanced. Provides cloud GPU instances for practice during courses.

Official Python documentation. Python is the language of AI — virtually every ML framework, API client, and data tool is Python-first. The tutorial section is good for learning, the library reference is comprehensive. Bookmark the standard library docs — you'll use them constantly.

GNU Bash reference manual. Essential for working with Linux servers, CI/CD pipelines, and command-line AI tools. Claude Code runs Bash commands constantly. Understanding Bash scripting lets you automate workflows and understand what AI tools are doing under the hood.

The definitive JavaScript reference. MDN is community-maintained and consistently excellent. JavaScript runs the web and increasingly runs AI (TensorFlow.js, WebLLM, Node.js AI tools). The tutorials section is great for learning; the reference section is essential for daily work.

TypeScript documentation. TypeScript adds types to JavaScript, making large codebases manageable. Most modern AI web tools (Next.js, Vercel AI SDK, LangChain.js) are written in TypeScript. The Handbook is the best way to learn. Types help AI tools like Claude Code understand your code better.

Node.js API documentation. Server-side JavaScript runtime. Used by many AI web applications and tools. Understanding Node.js lets you build AI-powered web backends, CLI tools, and MCP servers. The Anthropic SDK and OpenAI SDK both have Node.js clients.

Ubuntu server documentation. Most AI development happens on Linux — cloud servers, GPU machines, and development environments. Understanding Linux is essential for deploying AI applications, managing servers, and working with tools like Docker and CUDA.

Official Git documentation. Git is how we track changes, collaborate, and deploy (pushing to master triggers our Cloudflare deployment). Claude Code uses git constantly — committing changes, creating branches, reading history. Understanding git deeply makes AI-assisted development much smoother.

Docker documentation. Containers are how AI models are deployed in production. Understanding Docker lets you package AI applications reproducibly, run model servers, and deploy to any cloud platform. The 'Get Started' guide is excellent. Essential for moving from local development to production.

Hugo documentation. The static site generator we use for this website. Fast, flexible, and deploys easily to Cloudflare Pages. Understanding Hugo templates, shortcodes, and data templates lets you customize the site without Claude's help. The quick start guide gets you running in minutes.

Tailwind CSS documentation. The CSS framework this site uses. Utility-first approach means you style directly in HTML with classes like 'text-gray-900' and 'flex'. AI tools like Claude Code are excellent at writing Tailwind — it's almost like a design language that both humans and AI understand well.

Microsoft's browser automation framework. We use it for taking screenshots of our website during development. Also useful for web scraping, testing, and building AI agents that interact with websites. Supports Chromium, Firefox, and WebKit. Has an MCP server for AI integration.

Cloudflare developer documentation. Our website is hosted on Cloudflare Pages (free tier). Also offers Workers (serverless functions), KV (key-value storage), R2 (object storage), and AI (run models on the edge). The free tier is remarkably generous. Their developer docs are well-written.

The company behind Next.js. Deploy web applications with zero configuration. Their AI SDK makes it easy to build AI-powered web apps with streaming responses. v0.dev (their AI UI generator) is built on this platform. Free tier for personal projects. Popular choice for AI app frontends.

Open-source backend platform with PostgreSQL database, auth, storage, and real-time subscriptions. Their pgvector extension enables vector search — essential for building RAG (retrieval-augmented generation) applications. Free tier available. Good alternative to building everything from scratch when you need a database for your AI app.

Deploy any application with minimal configuration. Supports Docker, Python, Node.js, and more. Simple pricing, good developer experience. Useful for deploying AI model servers, APIs, and backends. More flexible than Vercel (which focuses on frontends) but similarly easy to use.

Open-source workflow automation platform. Visual editor for connecting APIs, databases, and AI models into automated workflows. Has native AI nodes for calling LLMs. Self-hostable or cloud-hosted. Good for building AI-powered automation without writing a full application.

Package manager for macOS and Linux. The easiest way to install development tools, languages, and utilities. We use it on this machine for Hugo, Python, and other tools. Understanding Homebrew makes setting up development environments much faster. Run 'brew install' and you're done.

Markdown-based knowledge management app. All notes stored as local files (no vendor lock-in). Excellent for building a personal knowledge base about AI. The graph view shows connections between notes. Plugins extend functionality. Many AI researchers and engineers use it for note-taking and learning.

Co-founder and CEO of Anthropic, the company behind Claude. Former VP of Research at OpenAI. His essay 'Machines of Loving Grace' outlines an optimistic vision for AI's impact on science and society. Thoughtful, measured voice in AI leadership. Posts about Anthropic strategy and AI safety.

CEO of OpenAI. Former president of Y Combinator. The most visible figure in the AI industry. His posts signal OpenAI's direction and priorities. Controversial but influential — understanding his perspective is essential for understanding where the industry is heading.

Former Tesla AI director, OpenAI founding member, now independent. The best AI educator on the internet. His 'Zero to Hero' lecture series and live-coding sessions are legendary. Posts insightful threads about AI architecture, training, and the state of the field. Follow for deep technical understanding.

Turing Award winner, inventor of convolutional neural networks, Meta's Chief AI Scientist. Vocal advocate for open-source AI. Known for contrarian takes — skeptical of autoregressive LLMs, pushing for 'world models'. His debates with other AI leaders are educational. Follow for a rigorous academic perspective.

Shawn Wang, creator of the 'AI Engineer' concept and the Latent Space podcast. Bridges the gap between AI research and practical engineering. His threads synthesize complex AI developments into actionable insights for builders. One of the best follows for understanding how to actually build with AI.

Author of 'AI Engineering' and 'Designing Machine Learning Systems'. Stanford instructor. Her writing is clear, practical, and grounded in real experience building ML systems. Posts about AI engineering best practices, model evaluation, and the realities of production AI. We're reading her book right now.

Creator of Datasette and Django co-creator. The most prolific blogger about practical LLM use. Builds tools, writes detailed posts about experiments, and documents everything. His blog (simonwillison.net) is a goldmine of practical AI insights. Follow for hands-on wisdom about building with LLMs.

NVIDIA senior research scientist working on foundation agents and embodied AI. Posts accessible explanations of cutting-edge research. His work on Voyager (LLM-powered Minecraft agent) and foundation models for robotics represents the future of AI agents interacting with the world.

Founder of LangChain, the most popular LLM application framework. Posts about agent architectures, RAG patterns, and LLM engineering. His perspective on what works and what doesn't in AI application development is informed by thousands of LangChain users. Follow for practical AI architecture insights.

Creator of Keras (now part of TensorFlow), Google engineer. Created the ARC benchmark for measuring genuine AI reasoning (not just pattern matching). Known for thoughtful, sometimes contrarian views on AI capabilities and limitations. Posts about what AI can and cannot do with intellectual rigor.

Author of 'Build a Large Language Model From Scratch' and 'Machine Learning with PyTorch and Scikit-Learn'. University of Wisconsin professor. Posts clear explanations of ML concepts and new research. His 'from scratch' approach to teaching aligns perfectly with learning by building.

The most famous indie maker on the internet. Builds and ships products incredibly fast, increasingly using AI. Created NomadList, RemoteOK, and PhotoAI (generating $1M+ in revenue). Follow for inspiration on building products quickly with AI. Shows what's possible when you combine AI tools with entrepreneurial speed.

Founder of comma.ai (self-driving) and creator of tinygrad (minimalist deep learning framework). Famous for jailbreaking the iPhone at 17. Streams live coding sessions building AI infrastructure. Provocative, technically brilliant, and entertaining. His work on tinygrad shows how AI frameworks work at the lowest level.