A claim has been making the rounds in AI coding circles: stop prompting your coding agents and start designing loops that prompt them for you. Like most things, it gets repeated far more than it gets explained. This is the practical version — what an agent loop is, why it matters, and what one actually […]
Read More →A hands-on introduction to Flue, an experimental TypeScript framework for building server-side LLM agents. Covers the agent/harness/session model, three working examples from a simple translator to a container-backed coding agent, and an honest look at the trade-offs — so you can decide whether Flue fits your stack before you commit. Table of Contents You’ve decided […]
Read More →You’re deep in a refactor, you hand off a task to your AI coding agent, and you walk away to get coffee. When you come back, your project directory has been restructured — but so has everything else. The agent followed a chain of reasonable-looking steps that ended somewhere you never intended. This isn’t a […]
Read More →Your agentic app just ran a search. The tool returned 500 results as JSON. Your agent appended all of it and fired off an API call — 45,000 tokens to answer a question that needed maybe 4,500. Tejas Manohar, a senior engineer at Netflix, hit this problem every day. He was running out of tokens […]
Read More →You give your AI coding agent a task. It gets to work. Thirty tool calls later you have code — and it’s not what you needed. The agent understood the words but missed the intent. It made a dozen small decisions that individually seemed reasonable, and collectively built the wrong thing. This isn’t a capability […]
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