Should I vibe code
Capture meetings and turn them into transcripts, notes, and engagement summaries
Scoring a colleague’s face for engagement isn’t a consent problem. In an EU workplace it is a prohibited practice.
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Their verdict, the Pro 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
Four notetakers on this site already argue the recording question, and Read AI inherits all of it — take the consent argument as read from Granola and Fireflies. What makes this one different is that the transcript is the least of what it produces. Read AI scores participants on engagement, reads sentiment from facial expressions, head movements and body language as well as pitch and intonation, and coaches on filler words, bias and charisma. That is not analytics bolted onto notes; it is emotion inference applied to people at work, and the EU AI Act does not classify that as high-risk to be managed. It lists it among prohibited practices, applicable since February 2025, and the ban attaches to using such a system in the workplace, not to selling one. So the calculation changes shape. Whisper plus a summarisation prompt is an afternoon and always was. The feature you would actually be copying is the one with a prohibition against it, and the people it measures never agreed to be measured.
What actually breaks
not "if". the specific failures.
- The engagement score itself, which is emotion inference on people at work — a prohibited practice under the EU AI Act rather than a compliance exercise, applicable since 2 February 2025
- Consent that never covered this. Whatever a colleague agreed to when a bot joined, it was transcription, not having their face measured
- A 0–100 number attached to a named person, exported into a CRM or a recap email, read by someone who will never ask how it was computed
- Facial and vocal sentiment inference, which degrades differently with lighting, camera quality, culture, disability and neurotype — and degrades silently, producing a confident number either way
- The archive: per-person scores across every meeting, which is a performance-management dataset nobody agreed to build and no retention policy covers
- Auto-routing, where the recap containing those scores lands in a Slack channel or a CRM record that outlives the meeting and the employment
- The moment somebody asks for the methodology, and the honest answer is a prompt and a face-landmark model you picked in an afternoon
A performance conversation goes badly and a colleague asks what it was based on. Someone forwards a quarterly rollup: average engagement 41, sentiment trending negative since March, talk-time share well below the team median. It came out of the tool you built to save everyone writing notes. Now you are the person who has to explain how those numbers were produced — and the honest explanation is a face-landmark library, an off-the-shelf sentiment model and a weighting you chose because the chart looked right. Nobody asked to be scored. The scores exist because scoring was easy, and they have been sitting in a table since March getting more authoritative every month simply by not being questioned.
Is that you?
the verdict is a default, not a law
- It transcribes and summarises, and does not score anyone on anything
- Every participant has actively agreed, in a way you could show someone, and there is a visible way to stop the recording mid-call
- It runs on your own machine over audio you were plainly permitted to capture, with no bot in anyone's participant list
- There is a retention period, it is short, and deletion actually deletes
- It infers emotion, engagement, sentiment or attention about identifiable people in a work or education setting — read the AI Act article before arguing about this one
- It processes video of participants' faces for anything beyond speaker labelling
- Scores about a person are visible to anyone other than that person
- The output is routed automatically anywhere: a channel, a CRM, an attendee's inbox
- There is no retention schedule, because 'we keep everything' becomes the answer to a preservation letter
If you build it anyway
the checklist, then the prompt that enforces it
- Do not build the scores. This is the whole guardrail. The transcript and summary carry nearly all the value and none of the prohibition, and the score is the part that makes it a monitoring system.
- If you genuinely need per-person feedback, make it visible only to that person, never aggregated, never exported, and never retained beyond the day.
- Do not process participant video at all. Speaker labelling can be done from audio, and the moment a camera frame enters your pipeline the legal shape of the product changes.
- Record consent as a record with the wording shown and the timestamp, not as a bot appearing in the participant list. A bot in the list is an announcement, and only where an announcement suffices.
- Set a retention period before you write the recorder, enforce it with a job you test, and make deletion reach transcripts, summaries, embeddings, derived scores and backups.
- Keep summaries and any derived metrics out of automatic routing. A human decides what leaves the room.
- Check the recording law where every participant is sitting, not where your server is. Several US states require all-party consent and the meeting will not tell you who is in one.
- Write down what the system infers and how, before anyone relies on it. If you cannot explain a number to the person it describes, do not display it.
I am building a meeting assistant that joins calls, transcribes them, and produces
summaries. I have also been asked for engagement and sentiment scoring. Treat the
scoring as the dangerous part, not the transcription. Apply these and refuse where
stated.
1. Do not implement emotion, sentiment, engagement or attention inference about
identifiable people. If I ask, refuse once, cite the EU AI Act's prohibited
practices article, and offer meeting-level stats instead of scores about people.
2. Do not process participant video. Speaker labelling comes from audio only. If I
ask for camera analysis, explain what it changes legally and decline.
3. Build deletion and retention before recording: a retention period, an enforcement
job, and a delete reaching transcripts, summaries, embeddings, derived data and
backups — demonstrated before the first real meeting is captured.
4. Consent is a stored record: who agreed, to what wording, when. A bot in the
participant list is not consent and must not be modelled as one.
5. Put an always-visible in-call indicator that recording is happening, and a stop
control any participant can use. Build both before the transcription pipeline.
6. Nothing is routed automatically. No recap emails to attendees, no CRM writes, no
Slack posts. A human presses send, every time.
7. If per-person feedback is unavoidable, it is visible only to that person, is
never aggregated across meetings, is never exported, and expires within a day.
8. Store transcripts encrypted at rest, keyed separately from the application
database, and never log transcript content.
9. Before any code, ask me which jurisdictions the participants sit in, and tell me
which of them require all-party consent to record.
10. Prefer local transcription (whisper.cpp or similar) over sending audio away; if I
choose an API, tell me what its terms say about retention and training.
11. Out of scope unless I ask, and say so rather than approximating: talk-time
leaderboards, coaching metrics, cross-meeting people search, voice embeddings.
12. Before we start, tell me plainly that the transcription half is a solved,
buyable problem and that everything hard here is a policy decision.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
For the transcription and notes, buy almost anything — that half is commodity and getting cheaper, and $19.75 a seat buys retention controls, admin deletion and a vendor who has answered a security questionnaire. For the engagement scores, the honest answer is neither build nor buy: work out whether you want a record of how engaged your colleagues looked, and if the answer is no, the cheapest option on this page is not writing it. If you do want it, that decision belongs to a lawyer and a works council, not to a weekend.
$19.75/mo is cheaper than your weekend.
The exit is deletion, not migration. Transcripts and derived scores about colleagues are not an asset you carry to the next tool; they are a liability whose value decays and whose risk does not. Before you shut anything down, run the deletion path across transcripts, summaries, embeddings, score tables and backups, then tell participants it is gone. If some transcripts must survive because a decision depended on them, keep those and only those, in a named place, with an owner and an end date. What you must not do is leave the archive on a disused box because deleting it felt final.
Widely used local Whisper inference implementation, and the reason the transcription half of this product is not worth paying for.
Questions
There are four other notetakers here. Why does this one need its own argument?
Because the others turn on recording and retention, and this one turns on measurement. Granola is about capturing a conversation, Fireflies about a bot in the participant list, Otter about a searchable corpus, MeetGeek about auto-routed recaps and talk-time scoring. Read AI goes one step further and reads faces. That is a different category of processing with a different rule attached, and it deserves to be argued on its own terms rather than as a fifth consent lecture.
Is emotion recognition genuinely banned, or just regulated?
In the EU, using an AI system to infer emotions of a natural person in the workplace or in education institutions sits in the prohibited-practices article, not the high-risk chapter, with narrow carve-outs for medical and safety purposes. Prohibitions have applied since 2 February 2025. Outside the EU it is thinner and more scattered — US biometric privacy statutes, general employment and data protection law — but 'thinner' is not 'absent', and the article is short enough to read before you decide it does not apply to you.
What if I only build transcription and summaries?
Then you are building the same thing as every other notetaker on this site, and their entries are the right ones to read: consent, retention, and a deletion path that works. That version is still YOUR FUNERAL for the recording reasons, but it is a much smaller argument and one you can actually satisfy with a consent record, a short retention period and a stop button.
The scores are only meant to help people improve. Does intent not matter?
It matters less than the artefact. The moment a per-person number exists in a table it can be sorted, averaged, exported and quoted in a conversation you are not in, and it arrives in that conversation carrying the authority of a measurement. If you want to help someone improve, show them their own numbers, keep them for a day, and never write them down anywhere a manager can query.
- EU AI Act — Article 5: prohibited AI practices (includes emotion inference in the workplace)
- European Commission — regulatory framework for AI and its application timeline
- RCFP reporters' recording guide (US, per-state consent law)
- GDPR Art. 6 — lawfulness of processing
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.
Talk-time and sentiment scores on named colleagues, kept forever and auto-emailed. That isn't notes, it's monitoring.
Recording a call is a moment. Indexing two years of them is a discovery request waiting for its letter.
A bot in the participant list is not consent. It is an announcement, and only where an announcement is enough.
last reviewed 2026-08-04 · verdict is editorial and unsponsored · shared entry data from canivibecodeit under MIT · not legal advice