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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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Explains how to turn foundation models into applications, with attention to tools, planning, evaluation and the cost of running the system.
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Explains the surrounding software that lets a model take actions: agent loops, durable execution, memory and the context passed between workers.
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Connects document parsing and retrieval to structured workflows that apply business rules and update other systems.
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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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Connects AI products with crypto-market narratives, examining agent memory, product economics and the role of a project's token.
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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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Studies how models alternate reasoning with actions, then tests whether tool-using agents complete requests consistently across conversations.
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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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Connects digital art with onchain AI agents through AGNTSocial, and writes about the interfaces people use to interact with AI.
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