Marcus, a backend engineer at a fintech in Stockholm, was 800 words into a design document at 11pm when his voice-to-text hit the cap. Developers are now writing design specs and prompts by voice instead of typing code, but most voice tools cap out at 500 to 600 words per day.
The Shift
Five years ago, developers typed code. Today, they type intent. In Cursor, Claude Code, GitHub Copilot, the keyboard is still full, but it's full of prompts. "Here's the database schema. I need you to write a migration that..." Voice is faster for long-form specifications. But most voice tools weren't built for people who dictate entire design documents in a single session.
This changes what "fast typing" means. It's not about syntax speed. It's about capturing a thought before it evaporates, the way you'd capture it on a voice memo, but directly into the place you need it: Slack, design doc, PR comment, GitHub issue. The voice tool needs to keep up with your thinking speed for 10, 20, 30 minutes at a time.
That is a different problem than transcribing a meeting.
Cloud Caps Make No Sense for Local Speech
Most premium voice tools work the same way. They run on the cloud. Wispr Flow costs $14 a month and caps your free tier at 600 words. Willow costs $12 and caps at 500. Superwhisper costs $8.49 with no published cap, but indie developers have hit limits. The pricing logic is straightforward: cloud transcription has variable costs. The company runs speech-to-text on their servers, so they charge per word.
But this logic breaks down if the speech-to-text doesn't have to run on their servers. For developers, there is a second reason to resist cloud transcription: code in your dictation. If you're explaining a database schema or a bug fix by voice, you might speak code snippets. Cloud transcription means that code leaves your machine. Not because it has to, but because the business model demands it.
Marcus ran into both problems. His word cap hit mid-design-doc, and he had to switch to typing. But even if the cap did not exist, he would hesitate. The code in his design docs is proprietary payment settlement logic. He uses Cursor instead of VS Code partly because Cursor tab-complete reduces the voice rewrites needed. But he would never dictate that code into a cloud service. This is not paranoia. It is architecture.
What Changes If You Move Transcription Local
Speech-to-text has been solvable locally for years. Whisper, OpenAI's open model, runs on your device with no cloud required. On any modern laptop, Whisper's accuracy is strong enough for real work. Whisper-large-v3 hits 96.3% word accuracy on LibriSpeech. That is above the threshold where fixing the occasional error is faster than typing from scratch.
If the speech-to-text runs locally, the variable-cost model disappears. No words, no metering, no cap. A tool can offer unlimited dictation on the free tier because the cost is compute, which scales with your device, not the company's infrastructure.
This is not a new idea, but it changes who will adopt voice tools. Developers are skeptical of SaaS pricing logic that does not match the actual tech cost. They will not pay for a cap that does not have to exist.
Recitey's Approach
Recitey runs Whisper locally on Windows. The free tier has no word limit. No counter. No surprise cutoff at 500 or 600 words mid-thought. The voice layer is free. Pro is for the rewrite. The cloud part polishes your rough draft into a clean sentence in under 2 seconds.
This is not positioning. It is the actual product. Local dictation costs almost nothing. Paying for the cloud polish, if you choose to, is optional and separate.
For Marcus, this means he can draft the full design doc by voice without stopping. He can dictate into his Cursor editor, into Linear tickets, into Slack threads. The voice runs on his machine. It does not disappear mid-thought. The mental model matches the technical model. And nothing leaves his device unless he explicitly asks it to.
The Trade-Offs Are Real
Running Whisper locally means you don't get the latest-model accuracy improvements from OpenAI's cloud infrastructure. A new version of Whisper requires you to download it. There is no automatic update. This is deliberate: your control over the model is the point.
Latency also depends on your machine. Whisper-large-v3 takes a few seconds on modern hardware. If you have an older laptop, it will take longer. For some people, this is a dealbreaker. If you want transcription to work the instant you stop speaking, or if you have older hardware, cloud might be the right choice. This is not a failure. It is an honest line between different needs.
The developers who choose Recitey are the ones for whom this trade makes sense: you prefer control and no cap over guaranteed performance and a monthly bill.