Vibecode Hedy
track this build5 steps, step by step0%KINDA · weekend project
price $12.99/moyou'd save $155.88/yrbuild time a weekendcategory 🎙️ meeting notesreplaced by 0 people
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.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Hedy. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Hedy indie build ## Goal Build the smallest trustworthy replacement for the core Hedy workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Hedy instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build 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. ## Required capabilities - 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 ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Hedy. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Hedy indie build ## Goal Build the smallest trustworthy replacement for the core Hedy workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Hedy instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build 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. ## Required capabilities - 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 ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Hedy. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Hedy product brief ## Problem 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. ## Product outcome 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. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - 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 ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting 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. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Hedy capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Hedy indie build ## Goal Build the smallest trustworthy replacement for the core Hedy workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Hedy instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build 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. ## Required capabilities - 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 ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Hedy product brief ## Problem 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. ## Product outcome 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. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - 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 ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting 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. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Hedy capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
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.
xMobile apps that keep recording reliably when the screen is off, which is where most of these sessions actually happen
xTuned latency: your DIY suggestions arrive a beat late, and a beat late is useless in conversation
xSpeaker diarization and meeting-type presets that shape the assistant for a sales call vs a lecture vs a doctor visit
xCalendar and conferencing integrations that join and label sessions for you
xHosted, searchable history across every session with no laptop babysitting
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Hedy ($12.99/mo) with one prompt"
Vibecode Hedy
Kinda. The core of Hedy is buildable in a weekend with the prompt on this page, but there are real gaps: 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. Read the honest list above before committing.
How much does Hedy cost?
Hedy costs about $12.99/month (Pro, checked 2026-08-18), which is $155.88 per year.
What do I lose by replacing Hedy?
Honestly: 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. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Hedy?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.