Can Hedy be vibe coded?
Listens to your meetings, classes and calls live and feeds you suggested questions, context and notes while they are still happening.
The pieces are all commodity now: capture audio, stream it through a local Whisper variant, keep a rolling transcript, and every few seconds ask a model for the next good question. An agent will get you that in a focused implementation on a laptop, and the transcript-plus-summary half will genuinely be fine. What it will not get you is a phone in your pocket that survives being backgrounded during a two hour lecture, or sub-second suggestions that arrive before the moment passes. Live assistance is a latency and UX problem more than an AI problem, and that is exactly the part that takes weeks of fiddling. Build it if you mostly sit at a desk; keep paying if you mostly do not.
Jump to the build brief ↓Legacy-calibrated assessment
Checked Aug 2026
What you pay today, before any DIY hosting
low editorial confidence
Tracked separately from the pricing check
Buildability by layer
Screens, forms, and focused interactions
The repeatable job the product performs
Availability and legality of required data
Uptime, queues, support, and maintenance
Security, compliance, and user confidence
The achievable core
- A local desktop app that records mic and system audio, streams a rolling transcript, and every few seconds asks a model for suggested questions and notes in a side panel.
- Capture supplied audio, transcribe it, create structured notes, and export them.
- A responsive interface with real empty, loading, success, and error states.
The parts a prompt cannot buy
- Mobile apps that keep recording reliably when the screen is off, which is where most of these sessions actually happen
- Tuned latency: your DIY suggestions arrive a beat late, and a beat late is useless in conversation
- Speaker diarization and meeting-type presets that shape the assistant for a sales call vs a lecture vs a doctor visit
- Calendar and conferencing integrations that join and label sessions for you
- The last 20 percent is sync, migration fidelity, speed, and edge cases.
- Connectors, OAuth flows, and vendor API changes require constant upkeep.
Build, switch, or keep paying
Narrower, with trade-offs
A local desktop app that records mic and system audio, streams a rolling transcript, and every few seconds asks a model for suggested questions and notes in a side panel.
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Compare the prior art below or build only the workflow you need.
$12.99/mo
Because real-time is unforgiving. A post-hoc summarizer can be sloppy and still useful, but a live copilot that lags four seconds or drops audio when you switch apps is worse than nothing, and people pay to not think about that. The mobile side compounds it: background audio on iOS is a permissions and lifecycle minefield that nobody wants to solve for themselves. Add the meeting-type presets and calendar hookups and the subscription is mostly buying tuning you would otherwise do by hand for a month.
Visit Hedy ↗Why people still pay
Because real-time is unforgiving. A post-hoc summarizer can be sloppy and still useful, but a live copilot that lags four seconds or drops audio when you switch apps is worse than nothing, and people pay to not think about that. The mobile side compounds it: background audio on iOS is a permissions and lifecycle minefield that nobody wants to solve for themselves. Add the meeting-type presets and calendar hookups and the subscription is mostly buying tuning you would otherwise do by hand for a month.
The last 20 percent is sync, migration fidelity, speed, and edge cases.
Connectors, OAuth flows, and vendor API changes require constant upkeep.
The brief
Context, requirements, acceptance criteria, non-goals, and the full production standard — as Markdown, ready for any coding agent.
Build brief — a focused alternative to Hedy
Context
Hedy — Listens to your meetings, classes and calls live and feeds you suggested questions, context and notes while they are still happening. It currently costs $12.99/mo.
The pieces are all commodity now: capture audio, stream it through a local Whisper variant, keep a rolling transcript, and every few seconds ask a model for the next good question. An agent will get you that in a focused implementation on a laptop, and the transcript-plus-summary half will genuinely be fine. What it will not get you is a phone in your pocket that survives being backgrounded during a two hour lecture, or sub-second suggestions that arrive before the moment passes. Live assistance is a latency and UX problem more than an AI problem, and that is exactly the part that takes weeks of fiddling. Build it if you mostly sit at a desk; keep paying if you mostly do not.
This brief describes a focused, single-operator replacement for the part of Hedy that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.
What you are building
A local desktop app that records mic and system audio, streams a rolling transcript, and every few seconds asks a model for suggested questions and notes in a side panel.
Capture supplied audio, transcribe it, create structured notes, and export them.
A responsive interface with real empty, loading, success, and error states.
Requirements
Functional
Consent from whoever you are recording, which is on you.
Data and integrations
A laptop with a decent CPU or GPU for local Whisper, or an API key for hosted transcription.
An LLM API key in .env.
A virtual audio device (BlackHole on macOS, WASAPI loopback on Windows) to capture the other side of the call.
Each of these needs a real account, credential, or quota. Set them up before writing feature code.
Non-functional
Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
Security: server-side secrets, validated input, and no credentials in the client bundle.
Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
Portability: the operator can export their data and leave without losing it.
Implementation brief
Build a local desktop meeting copilot. Stack: Python 3.11, FastAPI backend, a single HTML page with vanilla JS and a WebSocket, no build step. Run everything on localhost, no accounts, no telemetry, no cloud storage.
Audio capture: use sounddevice to record from a selectable input device at 16kHz mono. Document in the README that capturing the other side of a call requires a loopback device (BlackHole on macOS, a WASAPI loopback device on Windows) and let the user pick it from a dropdown of available inputs. Optionally mix two devices (mic plus loopback) into one stream.
Transcription: faster-whisper with the small.en model by default, configurable via .env. Process audio in 5 second chunks with a 1 second overlap, maintain a rolling transcript in memory, and push each new segment to the browser over the WebSocket with a timestamp.
Assistance loop: every 15 seconds, or on a manual "nudge" button, send the last 2000 characters of transcript plus a short session brief the user typed at the start to an LLM via the OpenAI-compatible API. Key comes from OPENAI_API_KEY in .env. Ask for exactly three things in JSON: two suggested questions to ask next, one factual note or definition worth knowing, and one line of what seems to be at stake. Render these as cards in a right-hand panel, newest on top, with the old ones dimmed rather than deleted.
Session end: on stop, write the full transcript and all generated cards to ./sessions/YYYY-MM-DD-HHMM.md, then make one final call for a summary with action items appended at the top of that file.
UI: two columns. Left is the live transcript, auto-scrolling, with a pause button. Right is the assistance cards. A header with device picker, session brief field, start/stop, and a visible recording indicator. Dark, dense, no animations.
In scope: local recording, streaming transcript, periodic suggestions, markdown export, session list page that greps past files.
Out of scope: mobile, speaker diarization, calendar or Zoom integration, multi-user, auth, hosted anything. Do not add them.
Ship a README with the loopback setup steps, the exact pip install line, a .env.example, and a blunt note that recording other people may require their consent where the user lives.
Delivery standard
Inspect the repository first, then write a short implementation plan before writing code.
Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
Add structured logs around every external call and return actionable errors without leaking sensitive details.
Write unit tests for the core logic and one automated test of the main user journey.
Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.
Acceptance criteria
A clean install starts the app using only the README and .env.example.
The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
Tests, type checking, linting, and a production build all pass with no ignored failures.
No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.
Non-goals
Do not build these, and do not claim to have replaced them:
Mobile apps that keep recording reliably when the screen is off, which is where most of these sessions actually happen.
Tuned latency: your DIY suggestions arrive a beat late, and a beat late is useless in conversation.
Speaker diarization and meeting-type presets that shape the assistant for a sales call vs a lecture vs a doctor visit.
Calendar and conferencing integrations that join and label sessions for you.
Hosted, searchable history across every session with no laptop babysitting.
What you still own after launch
Secure credentials, rotate secrets, and handle provider rate limits.
Run migrations, backups, restores, and dependency updates.
Test the critical journey after every model, API, or hosting change.
Monitor failures and fix the edge cases a first prompt will miss.
Maintain every third-party integration as APIs and OAuth rules change.
Risk
Operational risk. The code is achievable; dependable data, integrations, and ongoing operations are the real cost.
Editorial confidence in this assessment: low. No reviewed project implementation is linked yet.
Generated by Can It Be Vibe Coded? · Full report: https://www.canitbevibecoded.com/hedy
You still own the product
- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
- Maintain every third-party integration as APIs and OAuth rules change.
Projects built from this idea
No reviewed implementation has been linked for Hedy yet. A submission is evidence for review, not automatic proof that the whole product was replaced.
Built a version of Hedy?Submit the project as evidence for this report.
Before you start
Can Hedy be vibe coded?
Partly, if you narrow it. The pieces are all commodity now: capture audio, stream it through a local Whisper variant, keep a rolling transcript, and every few seconds ask a model for the next good question. An agent will get you that in a focused implementation on a laptop, and the transcript-plus-summary half will genuinely be fine. What it will not get you is a phone in your pocket that survives being backgrounded during a two hour lecture, or sub-second suggestions that arrive before the moment passes. Live assistance is a latency and UX problem more than an AI problem, and that is exactly the part that takes weeks of fiddling. Build it if you mostly sit at a desk; keep paying if you mostly do not.
What can an AI coding agent reproduce from Hedy?
A local desktop app that records mic and system audio, streams a rolling transcript, and every few seconds asks a model for suggested questions and notes in a side panel. Capture supplied audio, transcribe it, create structured notes, and export them. A responsive interface with real empty, loading, success, and error states.
What will a DIY Hedy replacement still be missing?
Mobile apps that keep recording reliably when the screen is off, which is where most of these sessions actually happen; Tuned latency: your DIY suggestions arrive a beat late, and a beat late is useless in conversation; Speaker diarization and meeting-type presets that shape the assistant for a sales call vs a lecture vs a doctor visit; Calendar and conferencing integrations that join and label sessions for you; The last 20 percent is sync, migration fidelity, speed, and edge cases.; Connectors, OAuth flows, and vendor API changes require constant upkeep.
What do I still own after building a Hedy alternative?
Secure credentials, rotate secrets, and handle provider rate limits. Run migrations, backups, restores, and dependency updates. Test the critical journey after every model, API, or hosting change. Monitor failures and fix the edge cases a first prompt will miss. Maintain every third-party integration as APIs and OAuth rules change.