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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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Explores coding agents as repeatable systems, from the Ralph loop to a Lisp application that acquires new capabilities through conversation.
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Builds self-hosted agent systems and writes about persistent memory, task coordination and the path from an early experiment to the Sanctum stack.
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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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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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Persistent task tracking and coordination for coding agents that outgrow a single chat session.
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Writes about onchain markets and builds monitoring tools. His Code Vault collects trading scanners, wallet alerts and experiments with AI trading.
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