People & their work
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Crypto and AI, through investigations, research, working products and culture. Get to know the person, open a useful piece of their work and follow the subjects that interest you.
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40 people, with original work and projects to explore.
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Practical ways to plan, inspect and test software made with coding agents, from a small change to a team's development process.
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The product and infrastructure thinking behind Replit's move from a coding editor to building software through natural language.
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Explains how language models change software development, from his original description of vibe coding to experiments where an agent changes training code and measures the result.
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Explains the design of a coding agent and shares how he uses it in everyday work, including project instructions, reusable commands and ways to verify a change.
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Created Keras and ARC, and examines how to distinguish learned task performance from the ability to solve unfamiliar problems.
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Explores coding agents as repeatable systems, from the Ralph loop to a Lisp application that acquires new capabilities through conversation.
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Blockchain and AI design, from coauthored machine-learning papers to NEAR's approach to programmable money.
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Coauthored research on prompting models to produce intermediate reasoning steps and on testing whether short factual answers are correct.
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Connects reasoning and reinforcement learning methods to their failure modes, including models that improve an evaluator's score without completing the intended task.
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Post-training researcher; co-founder and executive director of Trillium Labs
Nathan Lambert
@natolambert
Explains how training examples, preference comparisons and verifiable rewards turn a pretrained language model into a more useful assistant.
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Researches reasoning, reinforcement learning and multi-agent systems, with earlier coauthored work on poker search and language-guided negotiation.
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Publishes detailed accounts of building applications with coding agents and the tools that grew out of that work, including OpenClaw and Oracle.
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A solo developer's account of building products with coding agents, remote development machines and interactive previews.
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Explains how a small app can connect AI services to a specific user problem, and how to demonstrate the result.
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Makes model research inspectable through annotated code and training reports, from a Transformer implementation to the training and evaluation of a coding model.
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Explains language models through small implementations, connecting tokens and attention to the code for building, training and adapting a GPT-style model.
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Turns experiments with coding agents into explanations of what to build, how to inspect the result and how to keep learning from code a model wrote.
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Studies how model behavior develops during training, using public checkpoints and controlled experiments on memorization and capabilities.
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Persistent task tracking and coordination for coding agents that outgrow a single chat session.
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Connects open model research and software to questions about what an AI benchmark measures, with work on model compression and scientific reasoning.
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Studies how model algorithms and GPU hardware fit together, with coauthored explanations of faster attention and alternatives to a growing attention cache.
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