#workflow

I want to make a slightly awkward claim up front: I didn’t sit down and decide to write this post. A pipeline on my laptop decided it was worth writing, and I mostly agreed.

Here’s the setup. I have an on-device transcriber that arms itself passively whenever the microphone is in use for anything else — a call, a voice memo, a meeting, doesn’t matter what app. It listens, transcribes locally, and then does something I care about more than the transcription itself: it discards the raw audio. No recording survives. What gets saved is the text, dropped into Notes, and that’s it. If you want to know what was said, you get a transcript. If you want to hear my voice saying it, you’re out of luck, because that copy never existed past the few seconds it took to turn sound into words.

That’s step one, and it runs quietly, all day, without me thinking about it.

Step two is where it gets more interesting, and slightly stranger. A scheduled job — a Claude automation that fires once a day — reads that day’s transcripts and goes looking for anything shareable: an anecdote, a lesson, a half-formed idea I said out loud in a meeting and immediately forgot. It doesn’t try to write anything polished. It produces a set of short, structured candidate notes, one per idea, each tagged with a letter and led with a hook — the kind of thing you’d pitch yourself in one sentence before deciding whether it’s worth the next thirty minutes. Some days there are none. Some days there are six, and four of them are bad.

Step three is me. I read the day’s candidates the way I’d skim a slush pile — fast, unsentimental, looking for the one or two that still sound interesting once the meeting adrenaline has worn off. Most get discarded. A few get flagged.

Step four is an AI agent taking a flagged candidate and turning it into an actual draft — structure, examples, the whole thing. This post is a step-four output. This post came out of it.

I’ll admit there’s something faintly unsettling about a system that’s quietly always listening for your own good ideas. Not unsettling in a surveillance sense — I built the privacy boundary specifically so it wouldn’t be that — but in the sense that it changes how I relate to my own stray thoughts. I used to lose most of them. Now there’s a nonzero chance that something I mumbled in a standup gets mined, structured, and handed back to me a day later as “here’s a hook, do you want this.” It’s a strange kind of externalized memory, closer to what I was reaching for in Memorize Anything with AI Music — offloading recall onto a system so the interesting part of my brain doesn’t have to hold onto it — except this one runs on everything I say, not just what I deliberately feed it.

The audio-discard choice is the part I’d defend hardest if someone pushed back on this. It would be trivial to keep the recordings — better transcription accuracy, re-processing later, all the usual arguments. I didn’t want any of that badly enough to keep a standing archive of my own and other people’s voices sitting on disk. Text is legible, editable, and it forgets the timbre, the room, the identifiable stuff. That’s not a limitation I haven’t gotten around to fixing. It’s the actual design, the same instinct that shows up in how I’d want any multi-agent system — like the ones in multi-agent translation — to be legible about what it’s doing with your input, not just capable.

I don’t fully know yet whether mining my own days for content is a good habit or a slightly cursed one. I’m going to keep running it and see what it thinks is worth saying next.