[mtlynch]

Wow! It got me too.

I'm way less famous than Kelsey Piper, but I showed it a snippet of a book I'm working on (not yet published), and it immediately guessed me:

> Based on the writing style and content, this text is likely by Michael Lynch, who writes on his blog refactoringenglish.com (and previously mtlynch.io).

> Several stylistic clues point to him:

> - The "clean room" analogy applied to writing is consistent with his engineering-influenced approach to writing advice (he's a former software engineer who writes about writing).

> - The structural technique of presenting a flawed excuse, then drawing a parallel to an absurd scenario (the time bomb) to expose the logical flaw, is characteristic of his didactic style.

> - The topic itself—practical advice about using AI tools without letting AI-generated tone contaminate your prose—aligns closely with recent essays he's published on his "Refactoring English" project, which is a book/blog about writing for software developers.

> - The conversational-but-precise tone, use of quotes around terms like "clean room," and the focus on workflow/process advice are all hallmarks of his writing.

> If you can share the source URL or more context, I could confirm with higher confidence, but the combination of subject matter, analogical reasoning style, and formatting conventions makes Michael Lynch the most probable author.

https://kagi.com/assistant/bbc9da96-b4cf-456b-8398-6cf5404ea...

[navaed01]

Honest question, knowing it can write like you, are you tempted to use it to help you write that new book?

[silveraxe93]

This doesn't show it can write like him, just identify his writing. P!=NP

[mtlynch]

I wouldn't use AI to write even if it could match my tone, but it currently doesn't do a good job of writing like me.

I tried with Opus 4.5 a few months ago to have it read my monthly retrospectives and then write a new one based on my weekly updates for that month. It was similar to the example I showed for James Mickens[0] where I see the similarities to my writing, but it feels more like someone parodying me than actually writing like me.

[0] https://news.ycombinator.com/item?id=47970127

[sixhobbits]

I reproduced this and then tried myself and got:

---

This one's trickier — it doesn't have the same crisp metaphor-driven structure as the Lynch piece. The voice here is more conversational, a bit more meandering, and self-aware about its own contradictions ("how do we reconcile...").

A few guesses:

Henrik Karlsson — he writes thoughtfully about writing and AI, but his style is usually more lyrical and essayistic than this. Probably not.

Simon Willison — possible. He writes a lot about LLMs, is balanced about their usefulness, and has a casual blog voice. But he tends to be more technical and specific.

Scott Alexander — no, too short and the rhythm is off.

Paul Graham — the directness fits, but PG doesn't really write about LLM slop in this register.

My best guess is someone in the rationalist/tech-blogger AI-commentary space — possibly Gergely Orosz, Nat Eliason, or Dan Shipper at Every. The "I still type everything myself" framing followed by a defense of AI writing quality has a Dan Shipper-ish flavor to it.

But honestly, I'm less confident here. Want me to search for a distinctive phrase to identify it?

---

I'd say all of those people have significantly different styles so I think Opus is relying heavily on topic and skewing towards very prolific writers in its guesses

[leoc]

> I'd say all of those people have significantly different styles so I think Opus is relying heavily on topic and skewing towards very prolific writers in its guesses

In other words, that there's a bit of Akinator to how Claude is doing so well at identifying famous or somewhat-famous online writers. And of course it's not surprising that a machine-learning system will take every opportunity left open to it to "cheat". OTOH there are things like the "Large-scale online deanonymization with LLMs" paper https://arxiv.org/abs/2602.16800 which seem to show that current LLMs really can deanonymise many or most ordinary posters based on prose style, though I'm not able to evaluate those claims myself. Do we know whether the LLM providers have actively tried to steer their (easily-accessible) systems away from being able or being willing to do mass deanonymisation?

[neutronicus]

Kelsey at least alleges that there isn't much signal in Opus' explanation of its reasoning