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Hedy hedy.ai

Can AI replace Hedy?

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 one sitting 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.

Verdict: Half-bot · AI gets you partway; the hard part stays hardBuild time: a weekend
Half-bot

01What it costs

$12.99/moPro, monthly per user
$155.88per year at that price

Checked Aug 18, 2026 · source: hedy.ai.

02Could AI build it for you?

The core job: 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.

What a working version needs:

  • 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
  • Consent from whoever you are recording, which is on you

The transcript half is a solved weekend; the live half is where the money is. Build the desk version and keep expectations calibrated.

03What you'd give up

  • 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

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.

05The build prompt

Paste this into an AI coding tool (such as Claude, ChatGPT, Lovable or Replit) to build your own version.

prompt.txt
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.
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App prices, verdicts, alternatives and build prompts are adapted from Can I Vibecode It? (MIT License, © 2026 Rob Hallam). Each price shows the date it was checked and its source. Prices change; confirm on the vendor's site before you decide.