Winnie Xu

on the atlas — 105

• pdf6 savers
• To Get Good, Go After The Metagame - Commoncog1 savers
• In-context Learning and Induction Heads11 savers
• An Alchemist’s Notes on Deep Learning — An Alchemist's Notes on Deep Learning4 savers
• Learn to Read Korean in 15 Minutes1 savers
• jonvonkowallis.com/readers/CHIN5910/178-206-Eliot_Weinberger_%26_Octavia_Paz-Nineteen_Ways_of_Looking_at_Wang_Wei.pdf1 savers
• Debugging Reinforcement Learning Systems11 savers
• The Annotated S411 savers
• Introduction - SITUATIONAL AWARENESS: The Decade Ahead44 savers
• Tutorial - What is a variational autoencoder? – Jaan Altosaar1 savers
• Dario Amodei — On DeepSeek and Export Controls2 savers
• N-dimensional Rotary Positional Embeddings1 savers
• dennyzhou.github.io/LLM-Reasoning-Stanford-CS-25.pdf1 savers
• Exploring institutions for global AI governance - Google DeepMind1 savers
• Toy Models of Superposition28 savers
• kevin frans blog1 savers
• Dario Amodei — The Urgency of Interpretability17 savers
• Generative AI’s Act Two | Sequoia Capital5 savers
• Claude's extended thinking \ Anthropic1 savers
• Conceptual SpacesThe Geometry of Thought | Books Gateway | MIT Press1 savers
• o3-mini is really good at writing internal documentation2 savers
• Tips for Managing GSUs (Google RSUs) — EquityFTW1 savers
• [2402.14740] Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs2 savers
• What is Technology? - Letters to a Young Technologist18 savers
• Optimizing LLM Test-Time Compute Involves Solving a Meta-RL Problem – Machine Learning Blog | ML@CMU | Carnegie Mellon University3 savers
• Transformer²: Self-Adaptive LLMs1 savers
• Home | Santa Fe Institute7 savers
• Optimizing AI Inference at Character.AI8 savers
• Just know stuff. (Or, how to achieve success in a machine learning PhD.) · Patrick Kidger12 savers
• Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts | RLHFlow1 savers
• 2403.09611.pdf2 savers
• Introductions - jxnl.co1 savers
• Planning for AGI and beyond9 savers
• How Lossless Data Compression Works | Quanta Magazine3 savers
• An Interactive Introduction to Fourier Transforms5 savers
• To Understand Language is to Understand Generalization | Eric Jang4 savers
• More Is Different for AI8 savers
• Competitive programming with AlphaCode | DeepMind1 savers
• Analogies between Biology and Deep Learning [rough note] -- colah's blog2 savers
• How To Be Successful90 savers
• The Bitter Lesson74 savers
• An Opinionated Guide to ML Research50 savers
• Quantum computing for the very curious47 savers
• Mimetic - Brian Timar44 savers
• Defeating Nondeterminism in LLM Inference - Thinking Machines Lab37 savers
• Home | Levers For Progress36 savers
• A Mathematical Framework for Transformer Circuits36 savers
• HOWTO: Be more productive (Aaron Swartz's Raw Thought)33 savers
• LoRA Without Regret - Thinking Machines Lab32 savers
• On the Biology of a Large Language Model31 savers
• Geeks, MOPs, and sociopaths in subculture evolution30 savers
• Google "We Have No Moat, And Neither Does OpenAI"28 savers
• What 81,000 people want from AI \ Anthropic25 savers
• Towards Monosemanticity: Decomposing Language Models With Dictionary Learning23 savers
• Visual Information Theory -- colah's blog23 savers
• We Need a New Science of Progress - The Atlantic22 savers
• Letters to a Young Technologist22 savers
• ChatGPT Is a Blurry JPEG of the Web | The New Yorker22 savers
• Research Debt22 savers
• Zoom In: An Introduction to Circuits21 savers
• View article19 savers
• By default, capital will matter more than ever after AGI — LessWrong19 savers
• Tracing the thoughts of a large language model \ Anthropic18 savers
• Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet17 savers
• The Only Important Technology Is The Internet — Kevin Lu17 savers
• GPT-417 savers
• Why does DARPA work?16 savers
• Neural Networks, Manifolds, and Topology -- colah's blog14 savers
• LLM Powered Autonomous Agents | Lil'Log14 savers
• RLHF & Post-Training Course by Nathan Lambert14 savers
• A Recipe for Training Neural Networks13 savers
• the agony of eros: dating - by Ava - bookbear express13 savers
• jasmine sun's visit to china12 savers
• The Mom Test: how to talk to customers and learn if your business is a good idea when everybody is lying to you12 savers
• SF Privately Owned Public Open Spaces11 savers
• Scam Cities—Asterisk10 savers
• Yamauchi No.10 Family Office10 savers
• Kullback–Leibler divergence9 savers
• AGI and the EMH: markets are not expecting aligned or unaligned AI in the next 30 years — Basil Halperin9 savers
• Just Ask for Generalization | Eric Jang9 savers
• Reward Hacking in Reinforcement Learning | Lil'Log9 savers
• Blueprint for an AI Bill of Rights - The White House9 savers
• Explorable Explanations8 savers
• Noam Brown on X: "Implications of Large-Scale Test-Time Compute" / X7 savers
• Too much efficiency makes everything worse: overfitting and the strong version of Goodhart’s law | Jascha’s blog7 savers
• The Indy7 savers
• An Intuitive Explanation of Solomonoff Induction - LessWrong7 savers
• The "Basics" | Putting the "You" in CPU7 savers
• The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!) – Joel on Software6 savers
• How to Use t-SNE Effectively5 savers
• Prism: mapping interpretable concepts and features in a latent space of language | thesephist.com5 savers
• LLM Inference Performance Engineering: Best Practices | Databricks Blog5 savers
• What are Diffusion Models?5 savers
• H3: Language Modeling with State Space Models and (Almost) No Attention · Hazy Research5 savers
• Artificial General Intelligence Is Already Here4 savers
• Eliciting Latent Knowledge [Public] - Google Docs4 savers
• A minimal viable product for alignment - by Jan Leike3 savers
• Feature-wise transformations3 savers
• Thinking about High-Quality Human Data | Lil'Log3 savers
• Externalized reasoning oversight: a research direction for language model alignment — AI Alignment Forum3 savers