Agent

Prologue: A slack surface, and high density underneath In June I came back from Laos to Shenzhen. If you only look at the state, this was a slack month. What it looked like in practice — I would only come alive after dark, stay up to two or three a.m. every night, lulled to sleep by one short video clip after another. Waking up in the day to Shenzhen’s gray-white sky, I could not even gather the strength to leave the apartment. I told myself it was the weather, but inside I knew it was not just that. Every day I bargained with myself: tomorrow I will wake early, I will be disciplined, I will start working — and the next day I lost again to that version of me curled up in bed. The body felt empty, not tired-empty but hollowed-out-and-idling-empty. There were many bothers, but I could not point at any specific one. ...

Paste a URL, and in seconds an AI rebuilds it into a running, previewable, chat-editable modern React app. That’s the first impression of firecrawl/open-lovable — 27k stars, 5.2k forks, 94.9% TypeScript, a flagship open-source example built by the Firecrawl team. It targets the commercial product Lovable.dev (the README says outright “for a complete cloud solution, use Lovable.dev”) and sits in the brutally crowded “AI app generator” lane alongside Lovable, Bolt.new, v0, and Replit Agent. ...

“Software is eating the world.” — Marc Andreessen, 2011 “Now AI is eating software—and the question for the rest of us is: what’s left for one human, alone, in front of a screen?” — me, asking myself one night in 2026. Prologue: How Big Does One Person Need to Be? In February 2026, I ran my first complete overnight agent. I set a prompt, dropped it into Claude Code in a loop, and went to sleep. At 7 a.m. the next morning, what I saw on the screen was: 6 commits, 4 PRs, 3 auto-rolled-back failures, and a research brief I hadn’t even read myself. ...

“Most Agent projects die in the unmapped wilderness between PoC and production.” I wrote that line while reading through the Relay project documentation. Relay is an open-source AI Agent system for job searching — not a demo built on three lines of LangChain plus GPT-4, but a project with complete architectural documentation, 172 engineering tasks, a hybrid tech stack, and explicit counterexamples for every major design decision. It is not fully running yet. The Agent layer code is still being written. That is exactly why I think this article is worth writing: this is a system that has thought very deeply at the design level, and those deep thoughts — regardless of where this project ultimately lands — are valuable references for everyone doing Agent engineering. ...

“We are not really writing prompts. We are furnishing a room for the model — deciding what gets carried in, where it sits, when it gets moved out. The wording is just a sticky note on the desk. What we are actually doing is the interior work.” If you had asked me in 2024 “how do I use AI well,” I would most likely have talked to you about prompts: how to phrase instructions, how to set a role, how to give examples. But if you asked me the same question today, my answer would be completely different. ...

“The agent loop is 10 lines of code. Agent engineering is 100,000 lines of code.” The first time I read that, I paused — and the more I sat with it, the sharper it cut. It punctures the single biggest illusion in this whole field: people think building an agent means writing a good prompt and wiring up an LLM API. But the actual work of pushing a demo to production — of running safely, unattended, all night long — is 99% not in that loop. ...

This project is an ongoing effort to study AI open source projects one step at a time, building real-world skills by combining hands-on practice with AI tooling to tackle complex problems. Everything is documented here. Notion List I. Executive Summary LangChain has emerged as one of the leading frameworks for building applications powered by large language models (LLMs). This report provides an in-depth analysis of the LangChain open source project and its expanding ecosystem, evaluating its core technology, strengths, limitations, and future potential. ...