The engineering workflows that Cursor and Claude Code enabled are fundamentally different from the ones that came before. Your hands are still on the keyboard, but most of the words you're typing are not code anymore. They're context. Specification. Explanation. You're writing prompts the way you used to write code.
For Marcus, a backend engineer at a Series B fintech in Stockholm, that shift happened gradually. He started using Cursor last year to speed up settlement logic. What surprised him was not the speed gain but the type of work that suddenly took more time. Design docs at 11 PM. Slack threads explaining why a bug happened. PR descriptions where he's walking the reviewer through the intent, not just the syntax. Voice writing seemed like a natural fit for that workflow.
It was, until he hit the 500-word ceiling on Wispr Flow's free tier mid-design-doc and had to switch back to typing.
The Cost Structure That Creates Caps
Wispr, Willow, and Superwhisper all cap their free tiers. Wispr at 500 words per month. Willow at 1,000. Superwhisper at an unclear limit with a 2,000-character-per-day throttle. The reason is straightforward: cloud transcription has variable infrastructure costs. Someone has to pay for the compute that transcribes your speech. So you meter it.
Recitey's free tier is different because the cost structure is different. Whisper runs locally on your device. Zero cloud compute. Zero variable cost per transcription. That means there's no economic pressure to cap the free tier. No metering necessary. No word counter. No artificial ceiling.
This sounds like a small feature difference. It is not. It changes what voice writing is actually for.
When the Cap Kills the Workflow
Marcus's experience illustrates why. He's designing a distributed state machine for payment reconciliation. The logic is not complex, but the reasoning is intricate. Why this order of operations. Why this particular consistency model. What trade-offs are being made. Voice is faster than typing for that kind of explanation. You speak at 150 words per minute. Typing is 50-60 on a good day.
But voice only works if you stay in the flow. You're explaining something complex. Your brain is in the problem space. You're articulating trade-offs as you think them. If the tool cuts you off at 500 words, you don't finish the thought. You lose the context. You have to switch back to typing, which breaks the momentum, and now the second half of the explanation is fragmented and less coherent.
By the time Marcus got the warning on Wispr that he'd hit the limit, he was three-quarters through the design doc. He switched to typing. The design doc took another hour. The thinking was interrupted.
The Privacy Layer
There's a second reason Marcus doesn't use cloud transcription: code IP. His codebase is proprietary. Settlement logic is competitive. The last thing he wants is speech context of his financial system being stored on someone else's servers, even transiently.
Local Whisper solves that structurally. The audio doesn't leave your device. The transcript doesn't get stored in some cloud service's audit log. You dictate. The local Whisper model processes it. You get text. Done.
For developers working with proprietary code, and that's most developers working for fintech, healthcare, defense, enterprise SaaS, cloud transcription is not a feature. It's a blocker.
What Actually Changes
When the cap disappears, three things shift.
First, voice writing becomes something you can actually test for your workflow. You're not testing it within artificial constraints. You're testing whether explaining intent out loud is faster than typing it. For design docs and specifications, the answer is usually yes. For short Slack messages, maybe not. But you get to run that experiment at scale, not with a 500-word training wheel.
Second, the thinking flow continues. You finish your thought. You finish your explanation. The tool doesn't interrupt you mid-momentum. Marcus's 11 PM design sessions now run 30-40 minutes instead of 60 to 90, because he's not context-switching from voice to typing halfway through.
Third, you're working with a local model, which means no latency beyond the speech-to-text processing itself. Whisper-large-v3, the model Recitey uses, achieves 96.3% word accuracy on LibriSpeech and runs locally on modern Windows machines without any noticeable lag.
The Structural Difference
Wispr Flow is a good product for people who want to test voice writing with guardrails. Willow works fine if you're dictating short messages and Slack threads. Superwhisper is built for the Mac ecosystem and does one thing well.
Recitey's free tier is structured for developers who have already decided voice makes sense for their workflow and want to work without constraints. No metering. No word counting. No cloud uploads of your code context. The cost structure is inverted, which inverts the pricing model.
That's why the free tier is uncapped.