You're explaining the settlement logic at 11pm, still dictating, and your voice tool hits the word cap. You switch to typing. The thinking flow breaks, and what was a continuous 20-minute explanation becomes a fragmented morning cleanup task.
The reason for the cap is structural: cloud speech-to-text costs money. Every word you dictate costs the company infrastructure time, bandwidth, model inference. So they meter it. Wispr caps its free tier at a daily limit. Willow does the same. Superwhisper too. The pricing is honest, but it assumes that voice dictation is a premium feature, not your primary writing modality.
But the work has shifted. If you're building with LLMs, your bottleneck isn't typing speed. It's explaining intent: design specs, prompt engineering, PR context, incident postmortems. All of that is long-form explanation, faster to voice than to compose in prose. Most developers still avoid voice tools because of the caps, and they lose the speed advantage.
What if the architecture didn't require the cap?
Whisper Runs Locally, So The Variable Cost Disappears
OpenAI's Whisper model (which powers most local speech-to-text setups) is small enough to run on your device. It's open-source, trained once, no inference cost per word. Recitey runs Whisper locally on your Windows machine, which means the only cost to the company is the initial model download, plus maybe some optional cloud rewrite service later. Zero variable cost per dictation.
Which means there's no business incentive to meter it. No word cap. No counter ticking down.
The Developer Moment This Unlocks
Marcus works on payment settlement at a fintech in Stockholm. His work is half-engineering, half-documentation: design proposals, PR descriptions, incident postmortems. Before, he'd voice-draft the first 500 words of a design doc, hit the word cap on his previous tool, then switch to typing. The cognitive switching cost was higher than just typing from the start.
Now he dictates the full design doc, 1500+ words, in one flow. No cap. The audio stays on his device (he refuses cloud transcription for code IP reasons, and Cursor's tab-complete reduces the rewrites anyway). He sends the first draft to GitHub, refines it based on feedback, moves on. The difference isn't raw speed. It's the absence of friction at the moment when he's thinking fastest.
It's Not About Dictation Speed, It's About Flow State
This is the real insight: voice dictation is fast for long-form explanation because you're thinking as you speak. Typing interrupts that. You compose, edit, second-guess, rewrite. Voice-to-text removes the recomposition step.
The cap kills that. You hit the limit mid-thought, switch to typing, and the flow state is gone. You're not just slower; you're switching modalities at the exact moment your thinking is clearest. That's a tax on your best work.
What You Trade Off
Running Whisper locally means:
- No cloud rewrite service on free tier (just raw speech-to-text)
- The audio stays on your device (no transcription reaches a server)
- No data collection on what you're dictating
- The quality is Whisper-large-v3, which is 96.3% accurate on LibriSpeech but less aggressive at grammar correction than some cloud services
The raw accuracy is genuinely good. The trade-off is that Whisper doesn't know about your personal jargon (you have to spell out rare terms), and it's not correcting obvious grammatical slips. That's why Recitey has an optional Pro tier for cloud rewrite: if you want the cleanup, you turn it on for 30 seconds of post-processing. But you're not paying per word, not capped on free dictation, and your code never leaves your device.
Who This Is Actually For
If you're a developer doing long-form explanation (design docs, specs, PR context, incident reports), and you're currently avoiding voice tools because you hit caps, this removes that constraint.
If you're paranoid about code IP and refuse cloud transcription, the local-first setup gives you the speed of voice without the privacy concern.
If you're already using Cursor or VS Code with voice dictation, you'll recognize the moment: explaining intent is faster than typing it. This just removes the friction when you explain something long.