It's 11pm. You're designing the settlement flow for the payment system. You're in that rare state where the explanation is coming out perfectly. The async payment retry logic, the reconciliation flow, the edge case handling. It's all there, and you're saying it out loud to get it written down. Then the transcription tool hits its word limit and stops listening.
The bottleneck shifted, but the tools didn't
Three years ago, the constraint was typing speed. Write Python, write SQL, write the thing. Voice dictation made sense as a novelty for people with RSI. But the work changed.
Now you spend half your day writing prompts, design docs, and async explanations that feed into Claude or Cursor. You're still at the keyboard with the same hardware and same desk. But you're typing intent and context now, not code. The bottleneck moved from "how fast can I write" to "how clearly can I explain what I want built." Voice suddenly makes sense again, because speaking is faster for long-form explanation than hunting for the right words while typing.
Most voice transcription tools still pretend you're recording a voice memo, not drafting a document. They cap the free tier. Wispr Flow stops you at their monthly limit. Superwhisper charges $8.49/month. Why? Because the model they use, usually cloud-based, costs money per minute of transcription. So they meter it.
The word cap arrives mid-paragraph
That's where the friction starts. You're explaining a complex retry handler. You're getting the nuance right. Then the tool stops listening because you've hit the monthly quota. You switch back to typing. Or you stop the thought and restart. Or you continue the doc tomorrow. All of them destroy the flow.
The secondary friction is privacy. Your design doc includes internal API signatures, error codes, database partition logic, and service names. Running that through a cloud transcription service, even a good one, means code details leave your machine as audio. You care about that. You're not running your IP through Anthropic's servers. You wouldn't send it to OpenAI either, so why would you send it to a transcription API?
Local speech recognition is not new
Whisper is open source and runs locally. Apple's dictation has been native since 2012. Windows has had Voice Typing for years, though it's brittle and only works in certain apps. The missing piece was a tool that would run Whisper locally on Windows, expose it to every app (Cursor, Slack, Linear, browsers, terminal), and not meter you.
Recitey runs Whisper locally on your device with zero variable cost. No word counter. No monthly cap. No cloud transcription of your code. You dictate, it transcribes in near-real-time, and the audio never leaves your machine.
The clarity problem, then the rewrite
Your first-draft voice dictation is rough. You say "uh" and repeat yourself. You say "the thing" when you mean "the retry handler." You trail off mid-sentence. That's fine for notes. It's not fine for a design doc.
The tool shouldn't make you choose between flow and polish. Recitey separates them. Whisper handles the capture, local and uncapped. The rewrite, cleaning up the rough draft into a clear sentence, is optional and uses a cloud model, but only when you ask for it. You get the bottleneck solved (voice for the long-form thinking) without paying for cloud transcription you don't need.
Why the capped model persists
The pricing isn't about the tech. Whisper-large-v3 is accurate, hitting 96% word accuracy on English speech. The compute to run it locally is minimal. The cap exists because cloud dictation is distributed as SaaS with a margin structure. $14/month for unlimited. Or free with limits. The company pockets the difference between cost and price. Recitey doesn't do that. Local Whisper has no recurring cost. You pay once for the tool. It works forever. No meter. No cap. No surprise limits when you're mid-explanation.
You get the clarity you need, on your schedule
Marcus, a backend engineer at a Series B fintech, used to restart his design docs because the cloud dictation cap arrived mid-explanation. After switching to Recitey, he can dictate the full architecture into Notion at 11pm without hitting a limit. If he wants to clean up the prose, he uses the cloud rewrite. If the first draft is good enough (it usually is for internal docs), he just pushes it. No vendor meter running in the background. No code IP on a transcription server.
The shift in workflow is real. Explaining intent to a model is faster by voice. But the tool has to support that workflow. Uncapped. Private by default. Working in the apps you already use. Local-first wins here not because it's philosophically pure, but because it solves the actual problem: you need to finish your explanation.