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Product designer and entrepreneur; author of Mobile First and Web Form Design; formerly a product director at Google; now building AI products.

• Ask LukeW: A New Retrieval System

  The Ask LukeW feature on my Web site has been answering people's product design questions using my writings, talks, images, and videos for over three years. During that time, I've seen people ask lots of different kinds…

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Co-founder of Modem and a founding engineer at Sentry, where he went on to be VP of Engineering. Co-author of Third-party JavaScript, and co-host of the State of Agentic Coding podcast with Armin Ronacher.

• 👋 New blog post: "How coding agents read files – and how to write for them" In it I explain how code optimized for agent-discovery can lead to major token savings on search + retrieval, even with frontier models like GPT-5.6 Sol Plus other important findings ⬇️

  coding agentsOpenAIRelated

Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.

• Training an LLM-RecSys Hybrid for Steerable Recs with Semantic IDs

  An LLM that can converse in English & item IDs, and make recommendations w/o retrieval or tools.

  LLMsRelated

Machine learning engineer and consultant focused on RAG and retrieval systems. He writes about applied AI engineering at jxnl.co and is the author of the instructor library.

• The 12% RAG Performance Boost You're Missing (Ayush, LanceDB)

  Practical approaches to enhancing retrieval quality through fine-tuning, re-ranking, and understanding trade-offs in RAG systems

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• Text Chunking Strategies for RAG Applications

  Technical session with Anton from ChromaDB on text chunking fundamentals, evaluation methods, and practical tips for improving retrieval performance

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• Why Grep Beat Embeddings in Our SWE-Bench Agent (Lessons from Augment)

  Insights from Colin Flaherty on building autonomous coding agents and how agentic approaches reshape retrieval-augmented generation systems.

  coding agentsRelated

Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.

• Stumbled on the first(?) RAG in NarrativeQA from 2017. Because books & movies were too large for LSTMs to do Q&A on, they embedded 200-word chunks and retrieved similar snippets to answer questions. "Chunking and cosine similarity retrieval is so 2017."

  arxiv.org

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Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.

• Can't wait for when I can vibe code a production recommender system. Until then, here's some system designs: • Retrieval vs. Ranking: eugeneyan.com/writing/syst... • Real-time retrieval: eugeneyan.com/writing/real... • Personalization:

  eugeneyan.com

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AI research engineer working on large language models. He writes the Ahead of AI newsletter and is the author of Build a Large Language Model (From Scratch).

• RAGs — RAGs: Simple implementations of Retrieval Augmented Generation (RAG) Systems

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Machine learning engineer and independent AI consultant. He writes about LLM evaluation, tooling and applied ML at hamel.dev, and previously worked on machine learning at GitHub.

• An Open Course on LLMs, Led by Practitioners

  Today, we are releasing Mastering LLMs, a set of workshops and talks from practitioners on topics like evals, retrieval-augmented-generation (RAG), fine-tuning and more. This course is unique because it is: Taught by 25…

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Member of technical staff at Anthropic. He has led ML/AI teams at Amazon, Alibaba and Lazada, and writes about LLMs, recommender systems and engineering at eugeneyan.com.

• discord-llm — Experimenting with LLMs to Research, Reflect, and Plan (LLM assistants, retrieval, and Discord integration)

  LLMsRelated