You've started explaining the payment reconciliation logic to your model. Three paragraphs in, you hit a wall. Not a logic error. A word counter. The cloud dictation tool caps the free tier at 600 words per month, and you've crossed it. You switch back to typing, but the momentum breaks. The thought was three minutes ahead of your fingers. By the time you type it out, the design doc reads like a rough draft instead of the explanation you'd just articulated.
This is the new developer bottleneck. Not typing speed. Explaining intent to the model.
The LLM workflow shifted what "writing" means
Before copilots existed, developers and architects wrote less. You wrote commit messages, PR descriptions, maybe a design doc per quarter. The keyboard was for code, not prose.
Now you're writing intent into Cursor, Claude Code, GitHub Copilot, Slack threads explaining a bug, Linear comments walking through a decision, Notion docs at 11pm when the architecture finally clicked. Same keyboard. Three times as many words. Words matter now because they shape what the model builds.
Voice is faster than typing for this kind of explanatory writing. You can speak a design decision in three minutes. Typing it takes twelve. But cloud dictation tools treat voice like a novelty, capped free tiers, metered word counts, pricing that assumes you're transcribing meeting notes, not building specifications.
The pricing model assumes the wrong bottleneck
Wispr Flow charges $14/month for unlimited dictation. Willow is $12/month. Superwhisper is $8.49. All of them cap the free tier hard: Wispr at 600 words per month, Willow at 500 words per message, Superwhisper at 1000 words per month. They metered the access because cloud transcription has variable costs per request.
They're not wrong about the costs. Cloud API calls do cost money at scale. But they're solving for the wrong problem. They're optimizing for transcription, the act of turning speech into text, when what developers actually need is a tool that doesn't interrupt the thinking.
Recitey runs Whisper locally on your device. No API calls. No word counter. No monthly reset. The speech-to-text happens on your machine; zero variable cost per word. That's a structural difference, not a feature parity play.
What no cap actually means in practice
Last Tuesday, Marcus, a backend engineer at a Stockholm fintech, started a design doc at 11pm. Payment settlement reconciliation. He explained the flow, the current state, why it breaks under concurrent writes, the fix. Six minutes of talking, 2100 words dictated. No cap hit. No interface interruption. The doc was rough, but complete. The thinking was preserved.
With Wispr, he would have hit the limit three times and lost momentum each time. His choice becomes: stop mid-thought to type, or wait for the monthly reset. With a local tool, the thinking just flows.
That matters more for code IP than for anything else. Marcus refuses cloud dictation because payment settlement code is sensitive. The moment transcription leaves your machine, you've sent a recording and its transcript to someone else's servers. With local Whisper, the speech-to-text never leaves your device. The audio, the transcribed text, the rewrite logic, all of it stays on your machine until you choose to share the final output.
Local Whisper is not the full story
Local speech-to-text is accurate, but it is not perfect. Whisper's base model hits about 96.3% accuracy on standard benchmarks, but in practice, technical prose, especially code names, variable names, nested concepts, comes through rough. "Async retry logic" might become "a sync retry logic." You'll catch it when you read it, but you will read it.
That is what Recitey's Pro tier is for. Not the transcription. The rewrite. After you speak, you get a polished version in under 2 seconds: cleaned grammar, better flow, context-aware phrasing. The bottleneck shifts from "will I hit a word limit" to "does this read like I meant it."
The other trade-off: works everywhere or optimized for one editor
Cloud-only tools often lock into a specific IDE or agent. They optimize for Cursor, or Copilot, or a web interface. Recitey works across everything, Slack, email, browsers, GitHub, Linear, Notion, your terminal, your IDE, because it runs at the system level and feeds text through the clipboard. You're not locked into one tool ecosystem.
That matters when you work in Cursor for code but also write docs in Notion, explain bugs in Slack, comment on PRs in GitHub. One dictation tool, everywhere you write. Marcus uses Cursor specifically because tab-complete reduces the number of times he has to restate an intent to the model. With Recitey across all his tools, he gets that same efficiency everywhere.
Local-first is not for everyone
This is not a replacement for Otter.ai if you're transcribing meetings and need accurate speaker identification. It is not better than Willow if you primarily speak notes to yourself and don't care about world-class rewrite quality. Dragon NaturallySpeaking still leads for heavily accented speech and domain-specific language training.
Recitey is for the developer who writes more than they code now. Who loses momentum when they hit a word cap. Who cares where their code explanations live. Who gets 11pm design-doc clarity and wants to preserve that clarity without friction.
The free tier gives you unlimited local transcription. Pro gives you the one-second rewrite. Both run on your machine. No word counter. No monthly reset. No API call surprise.
That is the difference between a tool that respects your workflow and one that respects its own infrastructure costs.