Learning & community
Where to keep up and go deeper: newsletters, podcasts, people to follow, courses, foundational papers, events, and communities.
Newsletters and blogs
Writers and publications covering AI engineering, research, and industry economics.
- Chip Huyen - Essays on ML systems, AI engineering, and shipping models to production.
- Eugene Yan - Applied scientist writing on ML systems design, LLM evals, and recommender systems.
- Hamel Husain's Blog - Practical writing on LLM evaluation, fine-tuning, and shipping ML products.
- Han-Chung Lee - Notes on ML engineering, evaluation, and compound AI systems.
- Latent Space - Practitioner-focused newsletter and community covering AI engineering.
- Lilian Weng - In-depth technical posts on agents, RL, diffusion, and LLMs.
- One Useful Thing - Ethan Mollick's essays on how knowledge workers are actually using frontier models.
- Sebastian Raschka - "Ahead of AI": deep dives on LLM training, architectures, and research.
- SemiAnalysis - Dylan Patel's research on AI hardware, data centers, and the semiconductor supply chain behind frontier compute.
- Simon Willison's Weblog - Detailed technical notes on LLMs, tools, and prompt engineering from the creator of Datasette.
- Tensor Economics - Analysis of the economics of AI compute, inference costs, and model serving.
- Where's Your Ed At - Ed Zitron's newsletter with skeptical, deeply reported takes on AI hype, economics, and Big Tech.
Podcasts
Long-form conversations with the researchers and founders building frontier AI.
- Dwarkesh Podcast - Long-form interviews with researchers and founders on where AI is heading.
- Latent Space Podcast - Conversations with AI engineers on the systems, tools, and tradeoffs behind production apps.
- No Priors - Elad Gil and Sarah Guo interview the builders of frontier AI companies.
- The Cognitive Revolution - Deep technical interviews on frontier model capability and alignment.
People to follow
Researchers, builders, and educators worth following for signal on where AI is going.
- Andrej Karpathy - Founding OpenAI member and former Tesla AI lead whose talks and threads explain LLMs from first principles.
- Andrew Ng - DeepLearning.AI and Coursera founder posting pragmatically on applied AI, agents, and education.
- Boris Cherny - Creator of Claude Code at Anthropic, sharing tips and thinking on agentic coding.
- Chip Huyen - Author of "AI Engineering" and "Designing Machine Learning Systems" writing on shipping ML and LLM systems to production.
- Clément Delangue - Co-founder and CEO of Hugging Face, championing open-source AI and the model-sharing community.
- François Chollet - Keras creator and ARC-AGI co-author on reasoning, generalization, and where deep learning falls short.
- Jim Fan - NVIDIA senior research lead covering embodied AI, robotics, and foundation agents.
- Lilian Weng - Former OpenAI research lead whose long-form posts are canonical references on agents, RL, and diffusion.
- Sebastian Raschka - Author of "Build a Large Language Model (From Scratch)" who breaks down model internals and training.
- Yann LeCun - Turing Award laureate and deep-learning pioneer arguing for world models and open research.
Courses and learning
Hands-on courses and guides for learning to build and understand LLMs from the ground up.
- Claude Code Best Practices - Anthropic's engineering guide to agentic coding workflows and getting the most out of Claude Code.
- Claude Code Documentation - Official docs for Anthropic's terminal coding agent, covering setup, workflows, hooks, MCP, and the SDK.
- DeepLearning.AI Short Courses - Free hands-on courses on RAG, agents, evaluation, and multimodal systems.
- LLM101n - Andrej Karpathy's course building a Storyteller LLM from scratch, end to end.
- nanochat - Karpathy's minimal full-stack ChatGPT-style pipeline (tokenizer, pretraining, SFT, RL, inference) in one clean codebase.
- Neural Networks: Zero to Hero - Andrej Karpathy's video course that builds backprop, GPTs, and tokenizers from scratch.
- Stanford CS336 - Stanford's "Language Modeling from Scratch" course spanning tokenization, architecture, training, and systems.
- The Rise of the AI Engineer - The essay that named the discipline and remains a canonical reading list.
Foundational papers
The research papers that define modern LLMs: architecture, scaling, reasoning, and post-training.
- Attention Is All You Need - Vaswani et al., 2017. The transformer: self-attention, multi-head attention, and positional encoding, the architecture everything else builds on.
- Chain-of-Thought Prompting - Wei et al., 2022. Showed reasoning can be elicited by prompting alone; the conceptual seed for later reasoning work.
- DeepSeek-R1 - Guo et al., 2025. Reasoning behavior emerging from reinforcement learning (GRPO) rather than prompting or SFT; the current post-training frontier.
- FlashAttention - Dao et al., 2022. IO-aware exact attention that treats the GPU memory hierarchy as the real bottleneck, which is why long context and cheap inference are practical.
- Kimi k1.5: Scaling RL with LLMs - Moonshot AI, 2025. The other major RL-reasoning report alongside DeepSeek-R1, with more long-context and infrastructure detail; together they define the reasoning-model recipe.
- RULER - Hsieh et al., 2024. Benchmark measuring the real usable context length of long-context models beyond simple retrieval.
- Training Compute-Optimal LLMs (Chinchilla) - Hoffmann et al., 2022. Showed token count matters as much as parameter count for a fixed compute budget, revealing most prior models were badly undertrained.
- Training LMs to Follow Instructions (InstructGPT) - Ouyang et al., 2022. The post-training blueprint, SFT then RLHF, that turned a text predictor into an assistant.
Events
Conferences and hackathons for practitioners and researchers shipping AI.
- AI Engineer Summit - Annual conference for practitioners shipping AI into production.
- dotAI - AI conference for builders and developers, gathering full AI teams and top researchers.
- Gen AI Days - Conference on scaling generative AI systems in production, with real-world implementation talks.
- ICML - International Conference on Machine Learning, a top-tier ML research venue.
- NeurIPS - Conference on Neural Information Processing Systems, the largest ML research conference.
- NVIDIA GTC - NVIDIA's GPU Technology Conference, a major event for AI compute, research, and tooling.
- Shift Hackathon - 48-hour generative-AI hackathon.
Communities
Forums and subreddits where AI builders and enthusiasts share and discuss.
- r/artificial - General artificial-intelligence discussion community.
- r/ArtificialInteligence - Large general AI community (the subreddit's name is spelled this way).
- r/ChatGPT - One of the largest AI subreddits, covering ChatGPT and consumer AI.
- r/Claude - Community discussing Anthropic's Claude models and apps.
- r/ClaudeAI - Community for Claude and Claude Code users sharing workflows, prompts, and tips.
- r/codex - Community around OpenAI Codex and its coding workflows.
- r/cursor - Community for the Cursor AI code editor.
- r/LocalLLaMA - Running, fine-tuning, and evaluating open-weight models locally.
- r/MachineLearning - Long-running research-focused subreddit for ML papers and releases.
- r/mcp - Community around the Model Context Protocol and MCP servers.
- r/midjourney - Community for the Midjourney image generator.
- r/MistralAI - Community around Mistral's open-weight models and products.
- r/OpenAI - Community tracking OpenAI's models, products, and announcements.
- r/singularity - High-traffic subreddit debating AGI progress and frontier AI news.
- r/StableDiffusion - Hub for open image-generation models, workflows, and tooling.