Marcus hits the word limit at 2,000 words on his cloud dictation tool. It's mid-design-doc, mid-thought, explaining the transaction settlement logic for a distributed payment system. He's got forty minutes between PR merges and the next standup, and the entire thinking process just stops. Now he's either typing the rest by hand, or fragmenting the doc into pieces he'll stitch together in the morning when his thinking will be cold and he'll have to reteach himself the architecture.
This is the actual friction: cloud dictation pricing is built on metering. Word caps, monthly resets, freemium upsells. They exist because cloud providers need to rationalize the variable cost of processing speech. Each word transcribed costs them compute. But developers don't think in bursts. You explain a system at the speed you understand it. A cap doesn't make you type faster. It makes you write less. And when the thinking is interrupted, it fragments.
The bottleneck shifted
The old frame was "developers type code all day." That's not the bottleneck anymore. In 2024, your bottleneck is prompt writing and intent specification. You're not just typing code. You're explaining what you want Claude or Copilot to generate. That's longer-form prose. That needs unbroken flow.
Think about a design doc. You're describing system architecture, trade-offs, edge cases, failure modes, monitoring strategy. That's not a list you can break apart. It's an explanation that builds. You can't fragment it without losing coherence. The same goes for PR descriptions where you're not just saying what changed, but why it changed and what problems it solves. Slack threads explaining a bug investigation. Incident postmortems. You're doing more talking now. More explaining. More prose. More voice work that benefits from an uninterrupted flow state.
Most cloud dictation tools cap the free tier between 600 and 2,000 words per month. Wispr Flow maxes out at 600 words. Superwhisper is uncapped but charges $8.49 monthly. Willow caps at 1,200 words per month. They're optimized for 2018 use cases: short voice memos, meeting notes, quick messages. Quick capture. They weren't built for the workflow where your voice is now doing the thinking, not just the note-taking.
Local processing removes the metering problem
Recitey runs Whisper locally on your device. No API call. No per-word cost. No cap. The model runs on your machine the same way your IDE runs on your machine. Zero marginal cost after you install it. It's the same structural model as a local code editor. The compute happens on your machine. You own the cost.
That changes what the free tier can actually be. There's no financial reason to limit it. You can use local Whisper as your default for design docs, PR descriptions, Slack threads, postmortems, code walkthroughs, whatever you want, as much as you want. The only constraint is your device's compute, not someone else's cloud infrastructure budget or profit margin.
This is structural. When a tool charges per word, it has an incentive to price caps low. When a tool has zero per-word cost, it has an incentive to remove them. Recitey's incentive aligns with your need to write uninterrupted.
Code IP stays on your machine
If you've worked with any payment system or regulated financial code, you know the conversation. It always comes up: "Does this get sent to the cloud? How long is it retained? Can we see the logs? What's your data retention policy? Do you train models on our transcripts?"
Local dictation means you don't have that conversation. Your code, your design, your intent specification, none of it leaves your machine until you paste it. Whisper runs locally. The rewrite that makes rough voice into clean prose runs locally if you choose. You choose what goes to the cloud. You choose what stays.
This matters especially if you're working on IP-sensitive code or regulated systems. It's not just compliance friction. It's peace of mind. Marcus specifically switched to Cursor over VS Code because Cursor's tab-complete meant fewer voice rewrites and less context needed. He won't use cloud transcription at all because of code IP concerns. That's not paranoia. That's structural. When your code is your differentiator, when your system design is your advantage, sending it to a cloud transcription API is a decision with asymmetric downside.
The trade-off is real
Local Whisper runs at about 96.3% word accuracy on LibriSpeech benchmarks. One error per 27 words on average. Cloud services like Google or AWS sometimes score higher on specific datasets, especially in controlled conditions. But for code-adjacent prose, design specs, API behavior, edge cases, system assumptions, failure modes, the difference is minimal and often undetectable. The local rewrite cleans most rough edges in under two seconds. By the time Marcus pastes it into Linear or his design doc, the transcription quality is indistinguishable from cloud.
The real reason to pick a cloud tool is latency and ecosystem lock-in. If you need real-time transcription below 100ms latency (live meeting notes, live standup subtitles, live translation), Whisper locally won't cut it. The round-trip time is too high. If you've already bet your workflow on Otter.ai's meeting integration or Speechify's audiobook pipeline or some other vertical play, the switching costs are real. Cloud tooling has other advantages. Latency. Ecosystem. Integration. Local tooling has advantages too. Privacy. Cost. Ownership.
For intent documentation and async voice work, which is what developers actually do in LLM workflows, local is faster. There's no network round trip. No vendor lock-in. No word cap that breaks your thinking mid-flow. No metering that incentivizes you to write less.
What actually changed in his workflow
When Marcus stopped hitting word caps, he noticed something: his design docs became coherent the first time. He didn't fragment them anymore. He didn't lose the train of thought mid-dictation. The prose was rough, no dictation output is clean, but the architecture was complete. The thinking was whole.
That changes how you write. When you can capture the whole thought in one voice memo, you don't lose the structure. You don't end up with three fragmented docs you have to stitch together and rewrite. You end up with one rough draft that needs polish, not one that needs reconstruction. The cognitive load shifts from "how do I split this idea" to "how do I clean this up." That's a different kind of work. One is fragmentation. One is refinement.
That's what an uncapped free tier unlocks. Not a feature. A workflow shape. The ability to think out loud without a financial penalty. The ability to write as fast as you think.
The difference is categorical
If you're skeptical of voice tools because you hit caps, if you're worried about code IP on the cloud, if you've tried Wispr or Superwhisper or Willow and felt constrained, the structural difference here isn't incremental. It's categorical. Local processing removes both the metering problem and the data problem simultaneously. One design choice. Two problems solved. The incentives are aligned.