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Software Engineering: The Art of Thinking Out Loud (with AI)

A colleague said something to me recently that I keep coming back to: "Often, by the time you've finished articulating a complex problem for the AI, you've already solved it yourself." It sounds almost like a joke. You open a chat window, start typing out your problem in careful detail — and somewhere in the middle of the second paragraph, the answer appears. Not from the AI. From you. If you've worked with LLMs seriously, you've probably experienced this. And I think it points to something important about what is actually changing in our craft — something that goes beyond the usual conversation about automation and job displacement. The Rubber Duck, Promoted Developers have known for decades that explaining a problem out loud helps solve it. The classic technique involves a rubber duck: you place it on your desk, narrate your code to it, and the act of articulation forces you to confront the assumptions you'd quietly made. The duck never responds. That's not the point. The LLM is a rubber duck that occasionally says something useful back. But even when it doesn't — even when the response is generic or slightly off — the discipline of formulating the prompt has already done its work. You've had to be precise. You've had to strip away ambiguity. You've had to decide what actually matters. That process is not a workaround. It is thinking. The Inversion of the Workflow In the pre-AI era, the typical development workflow looked something like this: you had a rough mental model of the solution, you started coding, and you discovered the edge cases along the way. The code was exploratory. The thinking happened during the writing. With AI assistance, that workflow inverts. Vague inputs produce vague outputs — the model has no way to compensate for an underspecified problem. So precision becomes mandatory upfront. You have to think before you type, not while you type. This is a more demanding cognitive posture. It requires holding the full shape of a problem in your head be

2026-05-28 原文 →
AI 资讯

AI Agents Are Great at 80% of Our Code. The Other 20% Is Why We Still Need Seniors.

We let AI agents loose on a payment platform. They crushed the boring stuff. Then they silently broke the stuff that matters. A survey came out last week. 54% of all code is now AI-generated. Up from 28% last year. I read that number and thought: yeah, that tracks. We're probably in that range too. But here's the thing nobody's asking — which 54%? Not all code carries equal weight. A CRUD endpoint for fetching merchant details? Low risk. The webhook handler that transitions a payment from pending to complete ? That's someone's rent. Someone's payroll. Get that wrong and money moves where it shouldn't, or worse, money doesn't move at all. I'm the CTO of a payment platform. FCA-authorised, processing real money, real merchants, real consequences. We run NestJS microservices, Docker, Traefik — the usual stack. And we've been using AI agents aggressively for over a year now. I'm not here to tell you AI is dangerous. It's not. I'm here to tell you it's dangerous when you forget what it's actually good at. The 80% Where AI Agents Are Genuinely Brilliant Let me give credit where it's due. AI agents have made our team faster in ways that would have seemed absurd two years ago. API scaffolding. Generating service boilerplate. Writing Zod validation schemas. Spinning up new endpoints. Creating test stubs. Refactoring imports. Migrating patterns across repos. We run multiple microservices. When we need a new service, an agent can scaffold the entire thing — module structure, base configuration, Docker setup, Traefik labels — in minutes. What used to be a half-day of copy-paste-and-tweak is now a conversation. When we overhauled our env management across all repos, AI agents did the grunt work. They mapped every .env file, found naming conflicts, identified common variables, and generated a unified Zod schema. What would have taken a team days of grep-and-spreadsheet work took hours. For this 80% of the codebase — the predictable, pattern-following, structurally repetitive code

2026-05-28 原文 →
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