Should I vibe code
Real-time AI noise cancellation and meeting voice enhancement
RNNoise in a notebook is the demo. A virtual mic that survives the next macOS update is the product.
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Their verdict, the Core price and the build-time estimate come from their entry, MIT-licensed. Checked 2026-08-04.
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Our verdict, the regret score and everything below it. Editorial and unsponsored — nobody can pay to be moved.
The honest answer
why the verdict is what it is
The model is the part you can get for free. RNNoise and DeepFilterNet exist, they work, and running one over a WAV file on a Saturday afternoon is a genuinely impressive demo. Krisp is not selling the model. It is selling a virtual microphone that every conferencing app on your machine sees, running inside a real-time audio callback with a budget measured in single-digit milliseconds, surviving the moment your AirPods connect mid-call and flip the sample rate. That layer is a signed system extension on macOS and a driver on Windows, it breaks on OS updates rather than on your code changes, and its failure mode is silent: you sound perfect to yourself and terrible to everyone else, and nobody on a customer call is going to tell you.
What actually breaks
not "if". the specific failures.
- The virtual device, not the model — a CoreAudio HAL plugin or a Windows audio driver is a signed, privileged component that stops loading after an OS update you did not choose the date of
- The latency budget: a real-time callback wants your processing done in a few milliseconds, and a model that takes twelve turns every sentence into a stutter you cannot hear on your own end
- Device changes mid-call, when headphones connect and the sample rate jumps, and the graph either resamples badly or drops out entirely
- Over-suppression, which eats the first syllable of every sentence, flattens laughter into a click, and removes the second person in the room as if they were noise
- The silent failure mode — the output only exists on the far side of the call, so a regression can live in your setup for six weeks before somebody mentions you sound underwater
- A debug buffer written to disk "just to check the output", which quietly turns a noise filter into a call recorder holding other people's voices
Is that you?
the verdict is a default, not a law
- You are cleaning up files you already recorded, offline, and listening back before you publish
- It is your own microphone, your own machine, and there is a one-click bypass back to the raw device
- You treat it as an experiment and keep the unprocessed audio
- Nothing you do with it is in the path of a call you cannot afford to lose
- It sits between your microphone and a call that matters, with no way to switch back mid-meeting
- You are installing a driver or system extension on anybody else's machine
- Any processed or raw audio is written to disk, even temporarily, even for debugging
- You have not tested what happens when the input device disappears halfway through
If you build it anyway
the checklist, then the prompt that enforces it
- Build the offline file-to-file version first and get the quality right there. If it does not sound good on a WAV, real-time will not save it.
- Never become the only path to the microphone. Ship a bypass that returns the raw device instantly, and make it reachable while a call is running.
- Measure round-trip latency before you tune quality, and treat any frame that misses the callback deadline as a bug rather than a glitch.
- Write no audio to disk. Not for debugging, not for a crash report, not for a quality metric — a filter that keeps buffers is a recorder, and the other people on the call did not agree to that.
- Handle device change and sample-rate change as the normal case, not the error case. It happens every time headphones connect.
- Test the thing that actually catches you: record the far side of a real call and listen to yourself, because your own monitor path never shows the artefacts.
I am building real-time microphone noise suppression that other apps can
select as an input device. The model is the easy part; the audio device layer
and the silent-failure mode are what matter. Work in this order and refuse to
reorder it.
1. First build offline: a command that takes a WAV and writes a cleaned WAV,
with a measurable quality metric. Do not touch a device API until that
sounds right on my own recordings.
2. Then measure latency honestly. Print per-frame processing time against the
callback deadline and treat any overrun as a failure, not a warning. Tell
me the real budget for my buffer size and sample rate before we pick a
model.
3. No audio ever touches disk. No debug dumps, no crash-report buffers, no
"temporary" files. If I ask for a recording to compare quality, say no and
explain that this app would then be holding other people's voices.
4. The processing path must have a hard bypass that returns the raw device
immediately and is reachable while a call is in progress. Default to
bypass on any error rather than to silence.
5. Device change, sample-rate change and device disappearance are normal
events. Handle each explicitly and never assume 48 kHz stays 48 kHz.
6. Nothing runs in a real-time callback except lock-free, allocation-free
code. No logging, no mutexes, no garbage collection on the audio thread.
7. Keep the model behind an interface. RNNoise or DeepFilterNet first, so it
is replaceable without touching the device layer.
8. Before any system extension or driver work, list what it requires:
signing, notarisation, entitlements, and what happens to it at the next OS
release. Then write me an uninstall path and test it.
9. Warn me that I cannot hear my own output and build a loopback check that
captures what the far side receives.
10. Out of scope, and say so: transcription, meeting summaries, echo
cancellation and shipping this to anyone else's machine.That one keeps you out of trouble. For the prompt that actually builds it, canivibecodeit.com has one.
their build prompt ↗Or don’t build it
the boring option, and the way back out
As soon as it is in the path of calls you are paid to be on. $16 a month per seat — $8 on the annual term — buys a signed device that somebody else re-qualifies against every OS release, and that is the entire recurring cost you would otherwise be paying in evenings. If you only need clean audio for recordings you edit later, the offline open-source version is genuinely enough.
$16/mo is cheaper than your weekend.
Keep the model and the device layer in separate processes or at least separate modules, so abandoning the project means uninstalling one component rather than unpicking an audio stack. Write the uninstall instructions the same day you write the installer — a half-removed virtual device that still claims to be the default microphone is the kind of thing you debug on the morning of a meeting.
Xiph's small recurrent noise-suppression model, still the standard starting point and cheap enough for real time.
Stronger full-band suppression with a LADSPA plugin for PipeWire, though the repo has been quiet since late 2024.
Virtual noise-suppressing microphone for Linux — the device-layer half, and a demonstration of how much work it is.
Questions
Can't I just run RNNoise on my mic input and be done?
On Linux with PipeWire, more or less — NoiseTorch showed exactly that. On macOS and Windows the gap is the device: other applications need to see a microphone, which means a HAL plugin or a driver, which means signing, entitlements and a component that a system update can stop loading. That is the work, and it is not the fun part.
How would I even know it was broken?
You would not, which is the reason this is DEMO ONLY rather than SHIP IT. Your monitor path is the clean signal; the artefacts only exist on the far side. Build a loopback that captures what the other end receives, or join a meeting from a second machine and listen to yourself before you trust it on anything that matters.
Is suppressing noise ever the wrong thing?
Frequently. Aggressive models treat anything that is not one steady voice as noise — a second person in the room, laughter, a guitar, the dog everyone actually wanted to hear. Krisp's tuning across that boundary is years of work on real calls, and it is the part a benchmark score will not tell you about.
Every week, someone ships something they shouldn’t have.
New verdicts, the worst thing that landed in the trap, and the occasional incident report. No other email, ever.
last reviewed 2026-08-04 · verdict is editorial and unsponsored · shared entry data from canivibecodeit under MIT · not legal advice