AI Tools & Learning Resources

AI is reshaping how we build software. Whether you’re integrating LLMs into your app, writing better prompts, or just trying to keep up — these are the resources I’ve found most useful.

Courses & Documentation

• Anthropic Courses — Free interactive courses on prompt engineering, Claude API, and building with MCP. Hands-on Jupyter notebooks.
• Anthropic Learn — Official learning resources and guides from Anthropic.
• DeepLearning.AI Short Courses — 88+ short courses (1–2 hours each) on GenAI, LangChain, RAG, and more. Instructors from OpenAI, Anthropic, and Google.
• OpenAI Cookbook — Examples and guides for using the OpenAI API. Practical, code-heavy, and constantly updated.
• Epic AI — Kent C. Dodds’ course on building AI-powered apps with Model Context Protocol (MCP). Workshop-driven, hands-on.
• Google AI for Developers — Gemini API docs, tutorials, and quickstarts for building with Google’s AI models.
• Microsoft Generative AI for Beginners — 21 lessons to get started building with generative AI. Covers prompts, RAG, agents, and more.
• Prompt Engineering Guide — Comprehensive open-source guide to prompt engineering. Covers techniques, papers, and tools.
• Awesome Prompt Engineering — Curated resources for prompt engineering with GPT, ChatGPT, PaLM, and more.
• Awesome ChatGPT Prompts — Community-driven prompt collection. 146k+ stars. Great for discovering prompt patterns.
• Latent Space Wiki — The AI Engineer newsletter + top technical AI podcast. Deep dives into agents, models, and infra.

AI Coding Tools & Assistants

• Claude Code — Anthropic’s CLI coding assistant. Agentic, terminal-native.
• Everything Claude Code — Official Claude Code repo — agents, skills, hooks, commands, and MCP configs.
• Claude Code Safety Net — Plugin that catches destructive git and filesystem commands before they execute.
• System Prompts Collection — Full system prompts from Claude Code, Cursor, Devin, Lovable, Replit, and 20+ AI tools.
• Cursor — AI-first code editor built on VSCode. Inline completions, chat, and codebase-aware edits.
• GitHub Copilot — AI pair programmer from GitHub. Integrated into VSCode, JetBrains, and Neovim.
• Codeium / Windsurf — Free AI code completion and chat. Supports 70+ languages.
• Expo Skills — AI agent skills for working with Expo projects and EAS.
• Callstack Agent Skills — Agent-optimized React Native skills for AI coding assistants.

People to Follow

• Andrej Karpathy — Founding member of OpenAI, former Director of AI at Tesla. Makes deep AI concepts accessible. His YouTube lectures are essential.
• Simon Willison — Co-creator of Django, now focused on LLMs. Consistently the best practical analysis of AI tools and models.
• Andrew Ng — Founder of DeepLearning.AI and Coursera. Publishes The Batch newsletter and leads AI education globally.
• Swyx (Shawn Wang) — Co-founder of Latent Space. Coined “AI Engineer.” Writes and podcasts about the AI engineering stack.
• Lilian Weng — Head of Safety Systems at OpenAI. Her blog posts are graduate-level deep dives on transformers, RLHF, and agents.
• Jay Alammar — Visual explainers of transformers, BERT, GPT, and attention mechanisms. The best visual AI content on the web.
• Chip Huyen — Author of Designing Machine Learning Systems. Writes about MLOps, real-time ML, and AI infrastructure.
• Eugene Yan — Applied scientist at Amazon. Writes about recommendation systems, LLMs, and ML engineering in production.
• Hamel Husain — AI engineer and educator. Practical content on fine-tuning, evals, and building with LLMs.
• Jason Liu — Creator of Instructor library. Writes about structured outputs, function calling, and practical LLM patterns.

Engineering Blogs — AI Teams

• OpenAI Blog — Research updates, model releases, and technical deep dives from the GPT team.
• Anthropic Research — Claude model updates, constitutional AI, interpretability research, and safety work.
• Google DeepMind Blog — Gemini, AlphaFold, and frontier AI research from Google’s AI lab.
• Meta AI Blog — LLaMA models, open-source AI research, and multimodal AI from Meta.
• Hugging Face Blog — Open-source models, transformers library updates, and community research.
• LangChain Blog — Building AI agents, RAG pipelines, and LLM application architecture.
• Vercel AI Blog — AI SDK updates, streaming patterns, and integrating LLMs into Next.js apps.
• Replicate Blog — Running open-source AI models in the cloud. Practical deployment guides.

Newsletters

• The Batch — Andrew Ng’s weekly newsletter. Expert analysis of AI research and real-world applications.
• TLDR AI — Daily AI, ML, and data science updates in 5 minutes. 500k+ subscribers.
• Ben’s Bites — Daily AI newsletter with a founder-centric perspective. Product launches, tools, and funding.
• Superhuman AI — Bite-sized daily AI updates. 3-minute reads covering the most important developments.
• Import AI — Weekly deep dives into AI research and policy by Jack Clark (co-founder of Anthropic).
• The Neuron — Daily AI news and analysis. Practical and accessible.
• Ahead of AI — Sebastian Raschka’s newsletter on LLMs, deep learning research, and ML engineering.

YouTube Channels

• Andrej Karpathy — Neural networks from scratch, GPT deep dives, and “The busy person’s intro to LLMs.”
• 3Blue1Brown — Beautiful visual explanations of neural networks, linear algebra, and the math behind AI.
• Two Minute Papers — Latest AI research papers explained in 2-minute summaries. Accessible and exciting.
• Yannic Kilcher — In-depth AI paper breakdowns. Technical and thorough — great for engineers who want depth.
• AI Explained — Clear, well-researched explanations of AI developments. Good for staying current.
• Matt Wolfe — AI tools, news, and tutorials. Covers the practical side of AI for builders.
• Fireship — Fast-paced tech explainers. Great AI-related content in the “100 seconds” format.
• StatQuest — Josh Starmer breaks down statistics and ML concepts with humor and clarity.

Open Source & Tools

• Hugging Face — The GitHub of AI. Models, datasets, and spaces for running and sharing AI.
• LangChain — Framework for building LLM-powered applications. Chains, agents, RAG, and more.
• LlamaIndex — Data framework for LLM applications. Connect your data to large language models.
• Ollama — Run open-source LLMs locally. Simple CLI for Llama, Mistral, Gemma, and more.
• LM Studio — Desktop app for running local LLMs. User-friendly GUI with model management.
• Instructor — Structured outputs from LLMs using Pydantic. Makes function calling reliable.
• GitIngest — Replace ‘hub’ with ‘ingest’ in any GitHub URL to get a prompt-friendly extract of a codebase.
• Firecrawl — Turn websites into LLM-ready data. Web scraping optimized for AI.

Books

• Designing Machine Learning Systems — Chip Huyen — The practical guide to building ML systems in production.
• Build a Large Language Model (From Scratch) — Sebastian Raschka — Understand LLMs by building one step by step.
• AI Engineering — Chip Huyen — Building applications with foundation models. Covers the full AI engineering stack.

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AI moves fast, but the fundamentals compound. Start with prompt engineering, pick one API to build with, and go from there. Pair this with Developers & Engineers to Follow and Frontend Tools & Learning Resources for the complete picture.