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 →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 […]
Read More →If you’ve been using AI coding tools long enough, you’ve probably noticed that they all start to feel the same. Cursor, Claude Code, Codex, Amp — they’re converging on a set of common features: plan modes, permission popups, sub-agents, MCP integration, to-do tracking. More surface area, more abstraction, more decisions made for you. Pi bets against […]
Read More →Python still owns the models. TypeScript ate the application layer. Here’s why — and what it means for what you build next. Table Of Contents The framing nobody puts on the cover If you read tech headlines in late 2025 and early 2026, you’ve probably seen the claim that TypeScript “overtook Python” or “won AI.” […]
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