appendix A / sources · continuously updated

Sources Worth Following

This field changes every week, but the "sources worth following over the long run" are actually quite stable. Below is a curated list—each entry explains why it's worth following and what stage it suits, rather than a random pile of links. Items marked ★ are the ones to follow "if you only follow three to five."

Personal blogs: high signal-to-noise, in-depth content

• ★ Andrej Karpathy + older blog —— Formerly of OpenAI/Tesla. The standard-bearer for the "implement from scratch" teaching method; both his articles and videos place enormous emphasis on first principles. His nanoGPT and llm.c repos are themselves teaching material (used in Chapter 6).
• ★ Lil'Log (Lilian Weng) —— Former VP of Safety Research at OpenAI. Every post is a systematic survey of some direction (agents, diffusion models, RLHF, adversarial attacks), rigorously cited—the best starting point when you "want to systematically understand a subfield." This course's Chapter 11 draws heavily on her agent survey.
• ★ Spaces.ac.cn (Su Jianlin / 苏剑林) —— The most hardcore ML blog in the Chinese-speaking world, bar none. The originator of RoPE positional encoding. Extremely meticulous derivations; the mathematical details in Chapters 6 and 7 can be cross-checked against his article series.
• Chris Olah —— Anthropic co-founder and a pioneer of interpretability research. "Understanding LSTM Networks" is one of the most-cited blog posts of all time; the Distill.pub he helped create (now discontinued, but the archive is extremely valuable) defined the benchmark for the "interactive paper"—this course's interactive philosophy comes in part from here.
• Sebastian Ruder —— Maps out research threads in NLP; his survey of optimizers is a classic.
• Sebastian Raschka —— Author of "Build a Large Language Model (From Scratch)." Each issue of his newsletter explains the latest papers clearly and thoroughly—one of the top picks for keeping up with LLM research progress.
• Eugene Yan —— Applied scientist at Amazon, sharing engineering experience on "bringing LLMs into products" (evaluation, RAG, pattern summaries), with a hands-on bent.
• Simon Willison —— Co-creator of Django, posting near-daily lab notes on LLM applications. The fastest personal source for keeping up with "what's happening at the tooling layer."
• Chip Huyen —— Author of "Designing Machine Learning Systems" and "AI Engineering," offering a perspective on ML systems design and AI engineering.

Courses and books: the backbone of systematic study

• ★ "Dive into Deep Learning" d2l.ai (Mu Li et al.) —— Free, bilingual (Chinese/English), balancing code and math, with notebooks you can run directly. The number-one backbone textbook for Chinese learners. The companion Bilibili video series "Learn AI with Mu Li" includes a "paper deep-read" series that goes through classic papers passage by passage.
• ★ Stanford CS231n (vision/CNNs) and CS224n (NLP/Transformers) —— The two most influential university courses of the deep learning era, with lecture notes and assignments fully public.
• fast.ai —— A top-down "get it running first, then go deep" teaching method, suited to people with an engineering background who want to quickly build intuition.
• Hugging Face Learn —— Free, hands-on courses on LLMs, RL, agents, and diffusion models, kept closely in step with the latest ecosystem.
• "Deep Learning" (Goodfellow et al.) —— The classic reference for theoretical foundations, free online. If it's too tough to read cover to cover, use it as a dictionary.
• Sutton & Barto, "Reinforcement Learning" —— The RL bible, free PDF. Worth reading the first few chapters before studying RLHF in Chapter 8. Sutton's short essay "The Bitter Lesson" is required reading for understanding the "scale vs. human priors" debate—only two pages long.
• Chip Huyen, "AI Engineering" (2025) —— A full-stack view of turning models into products: evaluation, data pipelines, deployment, monitoring. A systematic extension of the hands-on thinking in Chapters 8/9/13 of this course, and the first book for those transitioning into AI engineering.
• Iusztin & Labonne, "LLM Engineering Handbook" —— A hands-on handbook spanning prompt engineering, fine-tuning, RAG, evaluation, and production patterns—a good reference when building your second complete project after finishing this course.

Video channels

• ★ 3Blue1Brown —— The gold standard for visualizing mathematical intuition; the neural network series is the assigned viewing for Chapters 1, 3, and 6 of this course.
• ★ Andrej Karpathy —— The "Neural Networks: Zero to Hero" series: from hand-coding backpropagation to building GPT from scratch to "Deep Dive into LLMs"—the best LLM implementation tutorials anywhere.
• StatQuest —— Breaks down statistics and classic ML piece by piece, BAM! Great for shoring up the basics.
• Welch Labs —— Beautifully produced deep dives (AlphaFold, diffusion models, the history of machine learning).
• Yannic Kilcher —— Fast paper walkthroughs with sharp commentary, keeping up with the research frontier.
• Learn AI with Mu Li (Bilibili) —— The paper deep-read series goes through classic papers like Transformer, GPT, and CLIP passage by passage in Chinese, at extremely high quality.

Papers: how to keep up, and where

• arXiv (cs.CL / cs.LG / cs.CV) —— The first-hand source for papers. Scrolling it directly will drown you; better to pair it with the filters below.
• ★ Hugging Face Daily Papers —— Community-voted daily hot papers, the "paper filter" with the best signal-to-noise ratio.
• Papers with Code —— Find SOTA and the corresponding code implementations by task.
• alphaXiv —— A discussion layer over arXiv papers, where you can see authors answering questions.
• Semantic Scholar —— Citation networks and aids for literature review.

Leaderboards and evaluations: which models are actually strong

• ★ LMArena (formerly Chatbot Arena) —— Elo rankings from blind, human head-to-head testing—harder to game than a single benchmark, but note that it measures "human preference" rather than the ceiling of capability.
• LiveBench / SEAL —— Capability evaluations designed to resist contamination (questions are periodically updated/kept private).
• Artificial Analysis —— Side-by-side comparisons of price/speed/quality across models, indispensable for model selection and cost accounting (Chapter 9).
• SWE-bench —— Ability to fix real GitHub issues, a hard metric for agentic coding (Chapter 11).

Newsletters and podcasts: a regular feed

• ★ Import AI (Jack Clark) —— A weekly curation by the Anthropic co-founder, with a technical + policy perspective; the sci-fi vignette at the end is an Easter egg.
• The Batch (Andrew Ng) —— Weekly headlines plus Andrew Ng's opinion letter.
• AI News (smol.ai) —— Automatically aggregates daily discussion summaries from Discord/Reddit/X—see "what everyone's talking about today" in one place.
• Latent Space —— A podcast + newsletter from the AI engineer's perspective, with very high-quality interviews of frontline practitioners.
• Interconnects (Nathan Lambert) —— Insider, in-depth analysis of RLHF and open-source model training; the best source to follow for the topics of Chapter 8.
• TheSequence —— Biweekly deep dives for ML engineers and infra teams: LLM systems design, AI infrastructure, MLOps, leaning toward the engineering implementation layer (further reading for the topics of Chapters 7/9).

Labs and official blogs: first-hand information

• Anthropic Research and the Engineering Blog —— Interpretability, Constitutional AI, and practical agent-building (Chapter 11 cites its agent design articles).
• OpenAI Research · Google DeepMind · Meta AI —— The release pages of the three leading frontier labs.
• Qwen Blog · DeepSeek News —— Technical reports from the two leading open-weight models; they often disclose far more training detail than closed models, making them real teaching material for the content of Chapters 7 and 8.
• Hugging Face Blog —— A bellwether for the open-source ecosystem, often with in-depth long-form pieces that explain new techniques thoroughly (e.g., the FineWeb dataset report and the Ultra-Scale Playbook handbook on distributed training).

Communities

• r/LocalLLaMA —— The most active community for running models locally / fine-tuning / quantization; the daily discussion of Chapter 13's hands-on topics (MLX, LoRA, choosing small models) all happens here.
• Hugging Face Hub —— The GitHub of models/datasets/Spaces; just watch the trending list to sense where the ecosystem's heat is.
• GitHub Trending —— New things at the tooling layer usually surface here first.