I was scrolling the acquisition report for Brow, my Mac app, when I hit a row that wasn't there a few months ago.
chatgpt.com
Real people, real clicks. Somebody asked ChatGPT something like "best app to manage menu bar icons on Mac" or "a good Spotlight alternative for macOS," and the answer came back with Brow in it, by name, with a link.
For context: Brow is a Mac launcher that also tidies your menu bar, keeps your clipboard history, and bundles a stack of small utilities. So those are exactly the questions I'd want it surfaced for. I just never expected a model to do the surfacing.
I never pitched ChatGPT. No form, no ad slot, no deal. The model picked Brow on its own.
The numbers
30 downloads in 5 days, attributed to ChatGPT. Zero before this spring.
Two things matter more than the size.
It went from a flat zero, for the whole life of the site, to a number that now shows up every week. Nothing to something is the only jump that counts.
And these people convert. A Google click is still shopping. A ChatGPT click already got told "use this one" by an advisor they trust, so they land sold. A surprising share install. Cold search traffic doesn't behave like that.
How to test it without fooling yourself
You can probe the model, but only from a logged-out incognito session. No account, no memory, no custom instructions.
Ask from your own ChatGPT and it already knows you, so it tells you what you want to hear. A clean session answers cold, the way it would for a stranger who's never heard of you. That's the only answer worth trusting, and it's the first thing people get wrong when they "test" whether AI recommends them.
We took it further and built an internal tool that runs a batch of realistic prompts on a schedule and logs two things: which apps get named, and which source URLs the model cites to back the pick. So we watch which resources actually get picked up instead of guessing. The early patterns are interesting. I'll write them up once there's enough signal to say something solid.
What actually moves it
The model learned about Brow from the open web: the site, the App Store page, a few blog posts, the odd Reddit thread. It compressed the internet's opinion of Brow into a sentence and handed it back. So "optimizing" is old-school:
- Say plainly what the app does, on a page a crawler can read. The model can only recommend Brow as a "menu bar manager" or "Spotlight alternative" if the web already ties those words to it.
- Earn real mentions. Reviews, threads, word of mouth. That's the training signal, and there's no shortcut around humans liking it first.
- Don't keyword-stuff. Models smell it, and those visitors bounce hardest.
Then the plumbing, because the model only knows what got indexed:
- IndexNow via Cloudflare (one toggle, still in beta) so content changes hit the index immediately instead of waiting for a crawl.
- Bing Webmaster Tools + Search Console, both domains, to see real queries and confirm pages are even indexed.
- Study competitors' results, not their rankings. What queries they own, what rich snippets and sitelinks they pull, what their landing pages look like.
- Review domains. Sites with "reviews" in the domain name rank hard and feed the model dense social proof. Getting Brow onto the right ones is on the list.
Why a small number is worth a whole post
Thirty downloads won't change my month. But how people find software is splitting in two: the old pile of blue links, and a model deciding whether to say your name, trained on data you can't see, handing you back one anonymous row after the fact.
Most people find out it stopped saying their name by accident, months later. We stopped leaving that to chance and started watching the prompts directly. We can see it move now, and there's a lot more to say once the monitoring has run a while.
More soon.
(Brow is a Mac launcher, menu bar manager, and clipboard tool: macbrow.app)
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