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I burned $13.5k of AI tokens (at list price) building a Mac app. Revenue so far: $0.

I burned $13.5k of AI tokens (at list price) building a Mac app. Revenue so far: $0.

Numbers first, because that's what I'd want from this post:

  • Revenue: $0. Users: roughly zero. A handful of downloads so far.
  • Build time: 31 active days across 7 weeks, evenings around a day job. I fire off a task when I finish work, then knuckle down from about 9pm to 2am. Up again at 7:30.
  • Lines of code I've written myself: 0.
  • Claude tokens: 61 million output tokens (41M of that on Opus), plus 15.7 billion cache reads. At API list prices that's about $13,500 of usage. I paid for it through a $200/month Max subscription, plus Codex and a SuperGrok plan I've since run dry.
  • The app is live: a native Mac dictation app, $19 one-time after a free window, everything on-device.

So: a working product, a five-figure token bill at sticker price for a $19 app, and nobody using it. Here's the part worth arguing about.

I don't write the code. I run the team.

Every change ships through a team of AI agents: one builds, one reviews, one runs QA gates. Nothing I type ends up in the repo — my job is specifying what to build, reading diffs hard (much more in the early weeks than now), and being the one gate that can't be automated: I live-test the actual build with my actual voice before anything counts as done.

The review step is the part I'd defend at midnight: I make models from different labs argue. GPT-5.5 and Claude adversarially review the same diff, and they are ruthless with each other in a way neither is with its own work. Watching two frontier models fight over whether an audio buffer is being handled correctly is the closest thing this project has to a senior engineering culture.

What went wrong (a partial list)

The agents fought each other before the reviewers did. Early on I had multiple agents working the same checkout in parallel. Each one saw the others' edits as unexplained changes — and helpfully reverted them. Work just vanished. The fix was embarrassingly simple: tell each agent the others exist. Later I moved parallel feature work into separate worktrees entirely. I use worktrees every day at my day job; not starting there was my mistake, not theirs.

Days lost to a language argument. I wanted the HUD's visuals written in Rust and compiled to WASM. The agents kept "helpfully" doing it in JavaScript. I'd specify Rust; they'd bounce back to JS. It took multiple days of increasingly explicit instructions to make it stick. Agents don't share your architectural taste — they have to be told, in writing, repeatedly.

$40 of Kimi K3 for zero merged changes. New model launched, I gave it a shot, and the QA agent spent the credits driving browser automation. I got an audit document and nothing shippable. Not every model slots into an existing process.

The all-nighter the deprecation caused. The day before a model I leaned on was scheduled to lose access, I burned through 80% of a week's subscription quota in 24 hours getting everything I could out of it. Building on other people's models means their calendar is your calendar.

What it's actually cost

  • $200/month Claude Max, plus the Codex and SuperGrok subscriptions
  • $40 in Kimi tokens (see above)
  • $50 for a PeerPush listing — which, I'll admit, worked: #2 Product of the Day yesterday, 20 upvotes
  • ~5 hours most weeknights, and one week lost to Google quarantining my site because I'd skipped a notarization step (story for another post)

And what that bought: a shipped v0.6.0, ten blog articles, eleven free browser tools as top-of-funnel — and 210 visits and 560 page views in the last 30 days, a handful of downloads, zero sales. The product exists. The business doesn't, yet.

What I want from you

If you write all day on a Mac, I want you as user #1 through #10 — and I'll make it worth the detour: the first ten people who try Clabrate and give me honest feedback on the actual app get the $19 license free. Honest means what broke, what felt slow, what you'd never use — not encouragement. There's a free window to test in: hold a key, talk, clean text lands in whatever app you're in — or hand a task to the assistant and let it drive the Mac. Nothing leaves your machine. What you hate, I'll fix, with receipts, in next month's numbers post. I'll keep publishing everything: traffic, downloads, revenue, token spend.

And the question I actually want to argue about: I ship code I never wrote, reviewed by models arguing with each other, gated only by me using the build every day. Where's your line? Would you ship this way — and if not, what's the check you wouldn't hand over?

on July 19, 2026
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    Hey brenton, brutal story on burning $13.5k of AI tokens at list price while running a team of AI agents for Clabrate with $0 revenue. Unhandled agent loops and redundant context reloads drain backend budgets silently, and the drop-off right before the final decision page makes it even tougher. I built an async token leak defense and funnel audit blueprint to put a hard barrier on API overcharges: https://contra.com/s/Xs7WYN7c-llm-token-leak-and-checkout-friction-audit-24h-async?r=trs_research

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    The AI-assisted development story is fascinating, but I think the bigger question is whether your real product is dictation software or confidence that users can trust an AI-native workflow every day. I'd keep validating whether people buy Clabrate because it saves them time, or because it consistently feels more reliable than the alternatives they already have.

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      confidence that users can trust an AI-native workflow every day.

      This line drew me in. I have been so deep in ai for so long now. I used to work at Canva and now work with evals so I think I am in a bit of a bubble when it comes to ai technology.

      When it comes to having Clabrate consistently feel more reliable than what they have is tough as I know quite a few products are out there. I did spend a fair bit of time nailing the performance so it does feel instant. For me the killer feature isn't even the dictation. I love the assistant, e.g. I have a flow where I can rearrange windows or start claude in named terminals or even ask questions on what is on my screen e.g. I used it to diagnose some issues in cloudflare and it stepped me through what I needed to do.

      Demo's seem to be calling as there is a lot going on in it nowadays. I really appreciate what you said as it gives me things to ponder.

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        Appreciate the context.

        The shift you're describing from a feature to a trusted workflow is exactly the interesting part, but I don't think I'd do it justice in a public thread.

        If you're open to it, what's the best email to reach you on?

  3. 1

    The adversarial review pattern between Claude and GPT-5.5 is clever — but I think it's solving the wrong problem at this stage. You've spent $13.5k getting the build bulletproof. Meanwhile, speechara_ai and Pinstark are right: 210 visits means the landing page isn't doing its job yet.

    The agent team cost was actually highest where it mattered least — perfect code architecture vs "does anyone want to pay $19?" Those five hours most nights would've been better spent A/B testing headlines, landing page copy, or talking to ten Mac writers directly about what keeps them typing instead of dictating.

    The $0 revenue isn't a consequence of your build process; it's a signal that the product/market fit question isn't answered yet. Which is fine at v0.6 — but it means the token spend was really an engineering exploration that masqueraded as product building.

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      it's a signal that the product/market fit question isn't answered yet.

      I like this line. Also, you are not wrong on what you are saying. Right now I am just trying to get the downloads. I have built the tool for myself so far so I am sure it has evolved into a fair amount of complexity. My goal over the next week is to create a bunch of demo videos to really simplify the learning experience around it and hopefully that can increase downloads by throwing the video showcase front and center on the home page.

      Those five hours most nights would've been better spent A/B testing headlines, landing page copy, or talking to ten Mac writers directly about what keeps them typing instead of dictating.

      Potentially correct, however, different skill sets. I built the project for myself first and to sell it second. I am at that point now where I am happy with it's current features, tweaking things here and there now until customers start asking for things.

      talking to ten Mac writers directly about what keeps them typing instead of dictating.

      really appreciate this feedback, I will reach out on reddit to see what peoples hang ups are with trusting dictation today.

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    The part I’d keep is the daily live test with your own voice. For a dictation or speech product, a green QA suite can still miss the one hesitation, correction or latency spike that makes a person abandon it in real work.

    But I would separate two questions now: “can an agent team ship a working build?” and “which person has an urgent enough job to pay $19?” Your first ten tests should start with a real sentence they typed today, why the current workflow is slow, and what a successful result would let them stop doing. That makes the feedback useful for both product quality and paid intent.

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      yeah that makes sense, so far the QA has just been me using it in my day job and working on it in the evenings.

      I looked into the different STT models and decided to use parakeet V3 as it is multilingual. I had the agents build out a pipeline to chunk the stream so it is able to work in a streaming manner rather than waiting for the audio to finish and then doing the speech to text transcription.

      “can an agent team ship a working build?”

      This question is easy to answer for me. Agents can ship a working build and refactor / rework existing code with the right prompting. Early on in this project I hit issues with agents because they started to step on each others toes even when working in places that they don't touch the same files, they saw the other agents changes.
      I start a project from a place where I review everything to ensure the foundation is solid. Then once the core is solid it's easy to fan out in different branches – I just missed the exact point where I needed to do this.

      “which person has an urgent enough job to pay $19?”

      This is the question I need to ask myself. To be honest I am that customer as a software engineer that works a lot with agents. I don't pay for products usually but in this case if I played around with another tool that does what this does I wouldn't blink an eye.

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    peer just-launched too. the ai-agent-team stuff is fun to read but it's not what shipped $0. 210 visits + no sales is a landing page not clearing $19 on cold traffic. nothing in your build pipeline fixes that.

    also, ten free licenses will give you ten downloads from this thread but they won't validate paid intent. ask each person to name the specific job they'd hire it for before you send the key, otherwise you're recruiting people who'd never have paid $19 anyway.

    of the 210 visits, do you know which pages they land on, and does the drop-off happen before or after the pricing block?

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      ten free licenses will give you ten downloads from this thread but they won't validate paid intent.

      so true... Thanks for that insight. Majority bounce from the home page. Pricing I have on a separate page so definitely need to focus on that. The free tools pages like audio splitting, typing test etc. don't seem to drive traffic to the downloads page.