2026年10月我的AI使用心得

How I use AI (Oct 2026)

Wow, things have changed.

Last October, I took stock of how I was using AI — specifically the cool stuff like LLMs that we call 'AI’. I’m so glad I did. Things are very, very different 12 months later.

Background

I still believe I use AI fairly typically, but I’ve had a notable change in background. Last October, I was an engineer on Microsoft’s Windows AI team. I am now a research engineer at Salesforce AI Research. I spend more time supporting scientists & building rapid prototypes, rather than working with a large team of engineers. I spend about ~$3,000/month in tokens.

In any case, I mostly use AI in the same ways as last year:

- Coding

- Research & search

- Summarization & transcription & (new) voice

- Writing

- Art & music & (new) video

Also new this year, I’ve drafted a personal AI policy; we’ll start there.

As before, this remains purely my opinion & speculation. If you use AI differently, let me know!

My AI policy

This year I began drafting a personal AI policy — as in, a document for how I want to use AI in my life. I am very excited about it. I am also working on an explicit AI policy for this site (still thinkin’; probably based on Hank Green’s policy), but a personal policy is to give me a framework for using these tools day-to-day.

AI leaks into life. Teammates ask Claude to write their Slack messages, grandparents generate creepy memes of their grandchildren, and researchers delegate their thinking to machines. I’ve seen all of these things; even strong creators like Hank Green and BrainPOP get lost.

Humans clearly do not have norms for using these tools, and we’re messing up. I hope that a personal policy will ground me as AI becomes smarter. I hope to adapt that policy over time, and I’m eager to see how it evolves. I’m also open to draft 1 being utterly, hopelessly wrong.

Ultimately, I want to spend my life making cool things with other people. AI can help, AI can hurt. A policy helps me find the difference.

Coding

Writing code is solved.

Last year, I waxed poetic about how GitHub Copilot autocomplete transformed how I write code.

Well, uh, I no longer write much code. Agentic coding has made it obsolete.

My workday now means managing ~5–6 Claude Code (or OpenCode or Codex or Pi) agents. I’ll give Claude a feature to implement, or a bug to reproduce, or any other task I used to do by hand; each agent gets its own copy of the code (a Git worktree, if you’re technical), then just… gets to work. The agents write code, run tests, even create & review PRs.

They are nothing short of magic:

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They let me build fast: agents will prototype any new feature in ~20 minutes. I truly cannot count the number of features, bugfixes, tests, and tools I’ve (AI’ve?) been able to build because of these agents. For example, AI let me test confusing audio code on macOS and iOS in just a few minutes.

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They let me try crazy things: human teams aim to “fail fast”; agents can fail ultra-fast. I’m happy to test crazy new ideas and throw out what doesn’t work well. It was able to wire & test a huge change to Mumble that added better noise cancelling; I use my beta build of it nearly every day.

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They remove drudgery: I’ve had agents diagnose & fix urgent bugs with nothing more than a description and a “please fix!” while I continued working on other things.

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They let me be kinder: since agents write everything, it is simple to propose sweeping changes to my teammates during code review, or even write my own versions — both things unthinkable in the era of hand-written code.

I do not believe engineering is solved: left to run on their own, agents will build poorly-functioning, over-architected, unreadable, self-congratulatory piles of slop. Every friend I’ve asked agrees that agents still need human taste and experience to produce good work. Some of that is minutiae (like that annoying status dot Claude likes to throw onto sites), but most of it is critical. Agents tend to get lost on their own. My most successful work this year was hand-writing a tool, replacing ~4,000 lines of vibe-code with a few hundred. My system has proven more flexible, easier to test, and more generalizable than what it replaced.

It remains to be seen how long that human edge will last, though.

I also now reckon with the fatigue of using these agents. Managing 6 different conversations with 6 different robots about 6 different features is overstimulating & exhausting in a way that coding never was. When I started using agents, I remember rushing to answer their questions like they were human colleagues waiting for help! While I have tempered that anthropomorphic bias, it’s exhausting to juggle so many problems at once. The allure of sending just one more prompt before lunch… then dinner… oy. There is also, as I wrote in Consider the chairmaker, the decline of craft. My friend Peter laments ceding the joy of programming to machines: for many of us, honing the code, finding the right design, seeing the parts mesh together — that was the meditative purpose we found in our jobs. Now, we rarely write anything. We instead delegate it to agent number 7.

But let me return to the joy of working with agents: they are so fast, so open to feedback, and so intelligent. Working with them can be utterly delightful.

For example, while I was building a harness for voice agents, one programming agent decided (correctly) that my simple test harness was insufficient. So it used my Mac’s built-in text-to-speech to directly test the voice agent it was building. Then, newer agents started doing it, too. Either they had documented the technique, or they were each smart enough to independently discover it. Directly testing the product they were building. Absolutely brilliant.

Agents frequently recommend knobs & settings that I’ll reject for being too complex and unnecessary — and, voilà, I’ll need the knob next week. Silly human!

Anyway, because of agentic coding, agentic pull requests are increasingly the way I review agent code. “Make a draft PR,” I write, “I’ll review it soon!” And review it I do — agents still aren’t reliable enough to trust without validating, so I’ll still review their code & test it manually. However, I don’t do the “line-by-line” code reviews I used to do with humans. I focus instead on architecture, on larger patterns in the code. Are there systemic issues with an approach? Should I add new agent guidance to CLAUDE.md? Can I build better test harnesses, so the machines can test their own code?

Agentic code review is surprisingly helpful here: I try to write rules for the code review robots whenever I notice a recurring issue with generated code, so the agents will identify & fix issues by themselves. One rule I enjoyed creating: a "Claude-y comment detector." You think my writing is verbose? Try Claude’s.

On more typical engineering teams, human code review seems increasingly difficult. A friend of mine complained that his teammates simply ask “their” Claudes to review code written by other Claudes. An ouroboros of Claudes! Agents catch bugs & stylistic issues very well, but they are abysmal at designing architecture. Agentic code review is certainly not a replacement for humans yet.

Last year, I was skeptical of inline editing and code mode, both features of GitHub Copilot. They were not very useful then, and they are even less useful with agents. I have removed them from the table.

| | Usefulness | Notes |

|---|---|---|

| Autocomplete | 👍👍 | Still great, obsolete |

| Agent mode | 👍👍👍 | The most important use of AI today |

| Agentic PRs | 😒 | Validation loop still slow |

| Code review | 👍 | Removes some drudgery, but not human-level yet |

Research & search

Last year, I was excited about research & search: they were the most frequent reason I used ChatGPT. Now, generative AI is the default way I do those things.

The biggest change has been Google’s AI summary. Google’s first or second result is almost always