The work changed, but the tools didn't. You're writing specifications now, not taking voice notes. Long-form explanation of business logic, API flow, customer context, design decisions. This is different from the note-taking tasks that cloud dictation tools were designed for, and the mismatch shows when you hit the word cap at 11pm, mid-paragraph, and have to switch back to typing.
The Workflow Shifted
You used to write code. Typing was the bottleneck. Fast fingers, fast feedback loop. Then LLMs made everything change. The bottleneck shifted. Now you're writing intent for Cursor, Claude Code, Copilot. You explain what you want built, and the model builds it. Same keyboard, much more prose. Much more explanation.
A design doc for payment settlement logic is 2000 to 3000 words when you're writing it by voice. You're explaining the flow, edge cases, how it integrates with Stripe, what happens if a webhook fails, why the team chose this approach over that one. A PR description isn't "fix: null pointer" anymore. It's three paragraphs explaining the refactor, why it matters, what it unblocks. Slack threads on incident investigation become small essays by voice. You're walking your on-call colleague through the error stack, the business impact, what you've tried.
Voice is genuinely faster for this work. You think out loud, explain the flow as you'd explain it to a colleague standing at your desk. No time wasted fitting thought into typing rhythm. No mental translation from speech to written prose. It works until you hit the word counter on the tool you're using.
Why Cloud Tools Cap You
Wispr Flow charges $14 per month and caps the free tier at 1000 words daily. Superwhisper is $8.49 standalone. Most premium voice tools put a word counter front and center because they were optimized for a different task entirely. The cap made sense when the primary use case was field notes, voice memos to yourself, short-form transcription for podcasters. A few hundred words per recording. Quick capture. Move on.
But that's not the task that shifted when LLMs entered the workflow. You're not capturing ideas for later. You're drafting technical specifications at speaking pace, explaining architecture to your future self, creating records that other engineers will read and act on.
The word cap is a legacy design choice. It's still there because most voice transcription services run in the cloud and charge variable cost per minute or per word. They have to protect their margins. Cap the free tier, charge the heavy users. Standard SaaS. But it breaks when the use case changed.
What Local Processing Means
Recitey runs Whisper locally on your device. This changes the cost model entirely. There is no variable cost per transcription, no word counter, no cap. You can dictate a 5000 word design doc, and the tool doesn't care. The speech-to-text is done, locally, on your machine. Real-time, zero additional cost.
This inverts how most transcription pricing works. Most tools charge per word or per minute because the transcription happens in the cloud. Variable cost. Their free tier is limited so they don't go broke on compute bills. The economics force the cap.
When transcription runs locally, that variable cost disappears. The limiting factor isn't cloud compute. It's your own device. Your laptop runs it offline. Battery cost is trivial. You've already paid for the CPU. The audio never needs to leave your machine.
The Code IP Angle
Marcus is a backend engineer at a fintech in Stockholm. He's been writing design docs about payment settlement logic by voice. He's also rejected every cloud transcription tool he's tried in the last year. Why?
When you're dictating architecture, you're speaking business logic, API details, regulatory constraints, customer payment flows. Not every engineer wants that audio uploaded to cloud servers, even temporarily. Not every company's legal team signs off on it.
Code IP concerns are not hypothetical for a regulated business. They're not paranoia. Marcus uses Cursor specifically because the tab-complete feature reduces the number of times he has to repeat himself in voice, which means fewer rewrites. He refuses cloud-based transcription tools because you cannot unsend audio that contains your company's architectural decisions and customer data.
Local processing means nothing leaves the device. The audio stays in your working context. The disk, the RAM, the machine. No cloud service sees the content. No SaaS company logs the transcript. No AI model trains on your specification. This is the difference.
How Pricing Actually Works Now
This is the trade-off Recitey makes. Free tier is local Whisper, uncapped, no meter, no word counter. Pro tier is for cloud rewrite, not for the transcription itself. The polish, the grammar pass, the structured output to Slack or Notion or GitHub. That's where cloud time runs. That's where you choose to pay.
Most SaaS inverts this. Charge for the basic utility, gate the premium features behind a paywall. Recitey inverts that model. The base dictation is free, uncapped, local. The optional polish is paid.
This works because the economics are backwards from note-taking software. Note-taking is cheap to deliver. The value is in the UX, the sync, the organization. Transcription used to be expensive to deliver (cloud compute), so the cap made sense. Local transcription is expensive to build once, then free to deliver at scale.
Who This Actually Is For
This framework works if you're writing intent, not notes. If you're dictating voice memos to capture ideas for later, the free tier of most cloud tools is sufficient. You hit maybe a few hundred words per day. Recitey isn't cheaper for that use case.
But you're a backend engineer dictating design docs, PR descriptions, Slack explanations of technical work. You've felt that word cap cut your thought short. You've lost the momentum switching back to typing mid-idea. You've wondered why cloud transcription tools were designed with poets and journalists in mind, not developers writing specifications for LLM models to execute.
The workflow shifted from typing code to writing intent. The tool should follow that shift. That's where the difference is.