People & their work
Meet the people behind the posts.
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.
40 people
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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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Market theses, Solana and memecoin discussions, with a trading-journal interview and his own creator-project thesis to read first.
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Daily digital art, visual satire and studio events that connect internet imagery with a physical audience.
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Dogecoin's origins and network questions, paired with the dry internet humor of Shibetoshi Nakamoto.
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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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Video-led explanations of investment research, market theses and the creator's own process, alongside Kaizen.
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Essays that examine token-launch pricing, uncertainty and the competition for attention in crypto markets.
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Research on market mechanisms, adversarial blockchain execution and practical uses of cryptography.
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A founder's view of NFT communities, creative decisions and the challenge of building beyond a public persona.
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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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Art projects that turn familiar internet symbols into questions about verification, ownership and participation.
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Practical research on protecting private keys, planning recovery and testing the physical durability of seed backups.
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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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Circle's perspective on stablecoins, financial infrastructure and the purpose of Arc.
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An inside view of Base's network direction, upgrades and the people building on it.
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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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Firsthand proposals about rewarding social-network users and turning an NFT collection into a community project.
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A public thesis about memecoins, market cycles and the role of committed communities in sustaining attention.
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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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Telegram's product perspective and his own statements about TON's history, upgrades and future plans.
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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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Exploit analysis and security strategy that connect smart-contract flaws with infrastructure, people and incident response.
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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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Trading lessons and market commentary, with free Boot Camp material and the Blueprint education project.
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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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Long-form arguments about Ethereum's design, decentralization and the tradeoffs behind proposed protocol changes.
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Robinhood's company direction and first-hand product statements about markets, tokenization and access.
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Investigations that connect crypto thefts and scams to transaction trails, communications and public records.
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