Deep Thoughts

If you think a system prompt is just “giving the AI a persona”, Claude Code’s system prompt will upend that notion. It isn’t a few sentences. It’s a thousand-line template engine made of multiple layers, each responsible for something different: identity definition, behavioral constraints, tool documentation, project context, memory injection, skill loading. Some layers are static (identical every time); others are dynamic (injected in real time based on the project, the session, user preferences).

Last time we talked about the Think-Act-Observe loop — the skeleton of an AI coding assistant. A skeleton can’t work on its own; it needs muscle. In Claude Code, that muscle is tools. Without tools, the loop loses the Act between Think and Observe and degenerates into plain Q&A. With tools, the model can touch the real world: read your code, change your configs, run your tests.

If you dig through the Claude Code source, you will discover a counterintuitive fact: The core of the entire program is a while loop. Not a complex state machine, not an elaborate pipeline, not distributed coordination — just a plain while loop that does three things over and over: let the model think, let the model act, feed the results back. 1 2 3 4 5 6 7 8 9 while (user hasn't quit) { thinking = callLLM(conversation history + tool definitions) if (thinking.wants to call a tool) { result = executeTool(thinking.toolCall) conversationHistory.push(result) } else { output(thinking.reply) } } That’s it? The entire secret of an AI coding assistant lives in this loop?

I have a strong intuition: some companies in the tech industry right now are going through a full-blown AI psychosis, and inside those companies, rational conversation is simply impossible. I’m not talking about strangers — I’m talking about personal friends I deeply respect. That leaves me worried about how this ends. This fear has a specific source. I lived through the great MTBF (mean time between failures) vs MTTR (mean time to recovery) debate when infrastructure moved from traditional ops to cloud and cloud automation. Now those exact same arguments are resurfacing in a new costume — only the scope has widened from infrastructure to the entire software industry, and possibly to the whole world.

Everyone knows the standard Vibe Coding flow by now: write user stories → produce a PRD → AI generates the backend → AI generates the frontend → demo and verify. It works, but at delivery time the UI interaction experience often falls far short of expectations. Where does it go wrong? In the PRD itself. 1. The Fundamental Problem with PRDs # PRDs (Product Requirements Documents) were never that friendly to humans in the first place. Three reasons:

Immortality Is Just Eternal Loneliness # Today I saw a piece in the NT saying that China’s longevity industry is advancing at full speed and that people living to 150 is just around the corner. I searched the Chinese domestic news, and every story said people would only live to 120 — which turned out to be the press sparing the honored any embarrassment. Awkward.

The presentation slides at tech keynotes are always immaculate. Bathed in cool hues of cyan and clean white, tailored executives gesture smoothly beneath the spotlights, unveiling high-frame-rate b-roll of six-axis robotic arms dancing across cleanrooms and unmanned shearers cleaving coal seams in the bowels of the earth. The official press releases are draped in immaculate corporate dignity: “Zero-Harm Production,” “Automated Hazard Mitigation,” “Upgrading the Workforce Through Intelligent Infrastructure.” Applause ripples through the auditorium. Commentators celebrate a triumph of humanitarian progress: human beings will no longer be crushed under collapsing rock faces, nor will they gamble their marrow against the freezing crosswinds of high-rise scaffolding just to secure their next meal.

After reading the recent magazines and reports, my honest advice is this: while these next couple of years are still livable, seize the day and go enjoy yourself. Two assumptions: Suppose AI really does keep breaking through and takes over most people’s jobs. Society will inevitably enter a turbulent period of painful adjustment, and nobody knows what will happen. Suppose AI fails to break through and, in the end, cannot create that much value. Given the enormous leverage the whole world has piled onto AI today, the fallout when the bubble bursts would be no smaller than the 2008 subprime mortgage crisis. Nobody knows what will happen.