Tag: ai-native

Seven Clicks a Day, Forever

I built a three-skill pipeline so I’d never have to “Download transcript” again. The pipeline isn’t the point.

I record a meeting with myself every day.

It’s not a meeting. It’s me, at my desk (or in the car, or on a run), talking out loud to Teams for fifteen or twenty minutes about what I’m trying to do that day: the threads I’m pulling on, the half-finished decisions from yesterday, the things I’m worried about, the projects I need to start or finish, the things I’m excited about. I hit record, I talk, I hang up.

Then Teams transcribes it. And that transcript is gold. Because it isn’t notes I had to type. It’s me, thinking out loud at the speed of speech, in my own voice, with all the messy connective tissue that I never bother to write down. It is the single richest piece of context I can give to my agent for the rest of the day.

But to get it into the agent’s hands, I had to do this every time:

  1. Open Teams.
  2. Find the meeting in the chat.
  3. Click into the recap.
  4. Open the transcript pane.
  5. Click the three dots, click Download, pick .docx.
  6. Convert to txt so the agent can really consume it.
  7. Move the file from Downloads into the folder my agent watches.

Seven clicks. Every day. Forever.

That’s the kind of friction that kills a workflow before it has a chance to compound. I knew it. I felt it every morning. And every day I did those clicks anyway, because the payoff was worth it. Many days, more than once.

I’ve done this for months, and couldn’t build a way around it, until now.

The real point isn’t the meeting

I want to be honest about what this project actually was, because the surface description (“I automated Teams transcript downloads”) sells it short.

The point is not that downloading a Teams transcript is hard. It isn’t. It takes less than a minute.

The point is that a manual task done every single day, forever, is the single most expensive thing you can put in your workflow. Not because of the minute it takes. Because of the decision cost: the tiny moment every time where I have to remember to do it, choose to do it, and then mentally context-switch out of “I’m about to work” into “I’m doing data plumbing.” That decision cost is what kills the workflow. The transcript stops happening. The agent stops getting the context. The agent’s outputs get worse. I notice. I get frustrated.

The fix wasn’t a better agent. The fix was making the input arrive without me.

The constraint that shaped everything

The clean version of this is one Graph API call. GET /me/onlineMeetings/{id}/transcripts/{id}/content returns the .docx. It’s a one-liner.

It’s also blocked in my tenant. Admin consent on OnlineMeetingTranscript.Read.All is not happening. Nobody is going to grant that permission to one rando in a 200,000-person tenant. The answer was no, and the answer is going to stay no, and that’s fine; I’m not building a product, I’m building a workflow for me.

So the entire design had to assume that the only reliable way to get a transcript is to drive the Teams web UI like a human would. Playwright. Click the buttons. Wait for the download. Move the file.

Once you accept that constraint, the shape of the solution starts to come into focus.

Three skills, one pipeline

I broke the work into three pieces, each doing one thing, each independently useful.

Stage 1: /transcript-watcher polls my OneDrive Recordings folder every thirty minutes. Teams puts a new MP4 there every time a recorded meeting ends. When the watcher sees a new file whose name matches one of my watched series prefixes, it knows there’s a transcript to go get. It pings me in Teams (“I see a new recording for X, going to grab the transcript”) and invokes the next skill. The expensive thing (Playwright) only runs when there’s actual work.

Stage 2: /meeting-transcript is the Playwright driver. It opens Teams in a real browser, finds the meeting in the chat, opens the transcript pane, clicks Download, waits for the .docx, and saves it to a watched folder with a sensible filename. This is also the skill I can invoke directly when I want a transcript for a meeting that happened weeks ago.

Stage 3: /doc-watcher watches the folder. When a new .docx lands, it converts it to plain-text Markdown using Word COM (headless), pings me in Teams that it’s ready, and the original .docx never gets touched.

End to end: about thirty minutes typical, an hour worst case (the recording has to finish processing in OneDrive before stage 1 can see it). Zero clicks from me. If I want it right now (I usually do), I kick it off manually. Soup to nuts in three minutes.

The Markdown file lands in the same folder my agent reads at the start of every conversation. The agent sees this as ambient context. It knows what I was worried about. It knows what threads I’m pulling on. It knows what I’m trying to do that day. I didn’t have to tell it. I just had to talk.

The gotcha that confused me

One detail almost broke the whole thing.

The Recap picker in Teams shows the scheduled meeting time, not the actual recording start time. So my standing one-on-one with myself is scheduled at 18:45, but I actually hit record at 12:20, and the file the pipeline produced was named with 18:45. I do this more than once a day, so multiple files collided because they were all “scheduled at 18:45.”

The fix was to stop trusting the Recap picker and read the recording start time out of the chat thread instead. Three regex cases (English UI, localized variants, edge formatting) cover everything I’ve seen. The skill warns loudly if it falls back to the picker’s value, so I’ll notice if a fourth case shows up.

A workflow you can’t trust is a workflow you’ll abandon. Naming has to be right or none of the rest of it matters.

What this actually unlocked

The transcripts arrive. The agent reads them. I haven’t clicked “Download transcript” once.

What I didn’t expect was the second-order effect. Because the friction went to zero, I started recording more things: short voice memos between meetings, a five-minute postmortem after a hard call, a quick “here’s where I left this” before I close the laptop. All of it ends up as Markdown in the same folder. All of it becomes context.

The pipeline removed seven clicks. Removing those clicks made me record three times as often. The agent’s context window got an order of magnitude richer. Jevons’ paradox in action.

That’s the trade I want to keep making: find the daily friction, automate it down to zero, then watch the behavior on the other side of the friction explode.

The three skills are live

All three are published on SkillWorks if you want to lift them. They’re Clawpilot skills: install them with /install, configure your paths, and you’re running. They should work fine with GitHub Copilot, Copilot CoWork, or other systems with minor modifications. Ask your agent to adjust them.

  • Teams Transcript Pipeline: the full write-up (the why, the design, the dead ends)
  • /transcript-watcher: polls OneDrive for new recordings
  • /meeting-transcript: drives Playwright to pull the .docx
  • /doc-watcher: converts .docx to plain Markdown and pings you in Teams

Each one is independently useful. The doc-watcher in particular has nothing to do with Teams. Point it at any folder of Word documents and you’ve got a feed of plain-text copies that your agent can use.

Your turn

What’s your seven-clicks-a-day workflow? The one you’ve been doing forever because the payoff is worth it, but the decision cost is starting to fray?

Drop it in the comments. Half the time I see somebody else’s, I realize I have the same one and didn’t notice.

More of what I write lives at signalnotsentiment.com. Lessons from doing, not theorizing.

Phone in the Truck

Why I think we’re nowhere near as far along on the AI curve as it feels.

I’m old enough to remember the time before cell phones.

I had a phone on my desk at work. I had a phone at home, the one with the long curly cord, attached to an answering machine that I rewound by pressing a tiny tape down with my thumb. That was the communication stack. That was it.  Different numbers, different functions.

Then I got my first cell phone. And it wasn’t really a phone, not the way you’re picturing it. It was a handset bolted to the console of my Chevy S-10, right next to the gear shift. A cord ran from the back of it down into the dash for power. The buttons were on the back of the handset. You could pick it up and put it to your ear like a regular phone, or you could leave it cradled and hit speaker.

I thought I had arrived.

For the first time in my life, the dead hours of a commute became productive hours. Driving to a meeting, driving to a sporting event, driving home late, I could communicate. I told everyone I knew that this thing had changed my productivity forever.

It had. It just wasn’t what I thought it was.

Every stage looked like the peak from the inside

Then Nokia happened. The flip phone. The bar of soap phone. Auto-answer with a headset jack, which felt like science fiction, because now I didn’t even have to reach into my pocket. If it rang, I started talking. Productivity unlocked. Again.

Then keyboards. BlackBerry. Windows Mobile. Email in my hand. I remember thinking, this is it, this is the force multiplier. What more could you possibly need?

Then the iPhone. Apps. Then data, slow and unreliable at first, but a web browser in your pocket, which was a concept that had not existed before. Then real apps. Then Bluetooth, which we all made fun of until every single one of us was wearing it. Then video calls. Then maps that knew where you were. Then a bank in your pocket. Then a camera better than the one in my closet.

At every single one of those stops, I thought we had arrived.

I was wrong every single time.

The phone in my truck and the phone in my pocket today are not the same product. They are not the same category. They are barely the same species. And the version of me sitting in that truck in 1992 could not have described to you what the device in my pocket is today, because the words for it did not yet exist.

Now look at AI

I have a fleet of agents working for me right now.

One of them triages my inbox before I’m awake. One of them rewrites my drafts. One of them sits on my calendar and resolves conflicts before I see them, then sends me a morning report telling me what it moved and why. One of them builds dashboards on demand. One of them runs a weekly report on how many tokens I burned, how many artifacts got produced, and a rough estimate of how many hundreds of thousands of dollars of human effort would have been required to do all of that two years ago.

I am, by any reasonable measure, more productive than I have ever been.

And every time I demo a piece of this to someone, the reaction is the same. This is incredible. This is the future. We must be so close to the peak.

I keep wanting to agree with them. I have lived inside the productivity gain. I know what it feels like.

But I’ve been through this movie before.

We’re in the S-10

We are not at the iPhone moment of AI. We are not at the BlackBerry moment of AI. I don’t think we’re at the Nokia moment of AI.

I think we’re at the phone in the truck.

I think the version of AI my kids are going to use in a decade is going to be as unrecognizable to us, sitting here in 2026, as a modern smartphone would have been to me sitting in my truck in 1992. The words for it don’t exist yet. The form factor for it doesn’t exist yet. The social patterns for it don’t exist yet. We don’t even have the right complaints yet, and complaints are usually a leading indicator that a category is mature.

The exponential nature of this curve fools us. It feels like we’re moving so fast we must be near the end, when in reality the speed is the tell that we’re near the beginning. Things move fastest when they have the most room left to run.

The fact that the pace has compressed does not invalidate the pattern. It just means we’re going to see the next several stages inside a single career instead of across three of them.

So when somebody asks me where we are on the AI curve, I have a real answer now.

I’m in the truck. The cord goes into the dash. The handset is next to the gear shift. And I am absolutely certain I have arrived, in exactly the way I was certain the last four times.

Your turn

Where do you think we are on the AI curve? Phone in the truck? Nokia flip? BlackBerry? Early iPhone? Something later?

Drop your honest answer in the comments. Bonus points if you can name the feature you’re using today that will look as quaint in ten years as a handset bolted to a dashboard looks now.

Lessons from doing, not theorizing.