Vibecode Feedspace

You are building a lean indie version of Feedspace.

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 =====
# Feedspace indie build

## Goal

Build the smallest trustworthy replacement for the core Feedspace workflow for one developer or a tiny team.

## Scope

A public form collects a written, video or audio review, you approve it, and saved filters decide which approved reviews appear on each embeddable widget and hosted wall page.

## 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:
- 150+ review sources with 120+ on auto-sync, on an extraction engine Feedspace owns and runs in-house
- the upkeep · sources change and keeping the fetching working is someone else's job
- rule-based curation · matching new reviews join a widget or wall page by themselves, so the embed never goes stale
- in-browser video and audio recording that works on mobile without an app, and the media pipeline behind it
- aggregate rating badges that stay accurate because the auto-syncs keep feeding them

If those capabilities are essential, use Shosay 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 me a testimonial system for my own site where the wall keeps itself up to date. Requirements:

- Python 3 with FastAPI, SQLite via sqlite3, Jinja2 for server-rendered HTML, and local disk for
  uploads. Single process, no Docker, no signup flow, no tenants. It serves one site: mine.
- Collection page: a shareable link taking a star rating, a written quote, and optionally a video
  or audio take recorded in the browser. Ask for name, role, company and a photo, let me mark which
  of those are required, and stop recordings at three minutes.
- Re-encode recordings to MP4 with a thumbnail in a worker off the request path, keeping the
  original. If ffmpeg is absent, store the upload and flag it rather than failing the submission.
- Moderation screen at /inbox behind an .env password: newest first, approve or bin, plus free-text
  labels I can attach to a review.
- Now the part I actually care about. A display is either a hand-picked list or a saved filter. A
  filter can require a minimum rating, a media type, a photo on the reviewer, a job title, a named
  company, one of my labels, a phrase that must appear, and a phrase that must not.
- When a review is approved, every filter it satisfies picks it up immediately, with no republish
  step. Each display shows whether it is filtered or hand-picked and how many reviews it resolves to.
- Let me override a filtered display by pinning up to three reviews to the front of it.
- Embedding: a one-line script tag plus an empty div. Attach a shadow root and inject markup the
  server already rendered, so the host theme cannot restyle it and the quotes sit in the response.
- Three presentations: a masonry grid, a swipeable row, and a slow vertical ticker. Theme, accent
  and radius come off data- attributes.
- A standalone wall at /wall with my logo, a call to action, and Review plus AggregateRating
  structured data emitted from the same query that renders the cards.
- A score badge: rounded average and total count, recalculated whenever a review is approved or removed.
- Public endpoints get a per-IP throttle and an invisible decoy field, since anything with a form
  attracts bots.
- README covering the embed snippet, the .env keys, and running it behind nginx with a TLS cert.
- Not building: fetching reviews I already have on Google, Trustpilot, G2, Yelp or an app store, or
  polling them for new ones. That is a parser per platform plus someone maintaining them, which is
  not a weekend. Teammates and outbound review-request email are out too.

## Required capabilities

- a small always-on host
- somewhere to keep uploaded video and audio
- ffmpeg to convert recordings into a web-playable file

## 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 Feedspace.

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 =====
# Feedspace indie build

## Goal

Build the smallest trustworthy replacement for the core Feedspace workflow for one developer or a tiny team.

## Scope

A public form collects a written, video or audio review, you approve it, and saved filters decide which approved reviews appear on each embeddable widget and hosted wall page.

## 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:
- 150+ review sources with 120+ on auto-sync, on an extraction engine Feedspace owns and runs in-house
- the upkeep · sources change and keeping the fetching working is someone else's job
- rule-based curation · matching new reviews join a widget or wall page by themselves, so the embed never goes stale
- in-browser video and audio recording that works on mobile without an app, and the media pipeline behind it
- aggregate rating badges that stay accurate because the auto-syncs keep feeding them

If those capabilities are essential, use Shosay 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 me a testimonial system for my own site where the wall keeps itself up to date. Requirements:

- Python 3 with FastAPI, SQLite via sqlite3, Jinja2 for server-rendered HTML, and local disk for
  uploads. Single process, no Docker, no signup flow, no tenants. It serves one site: mine.
- Collection page: a shareable link taking a star rating, a written quote, and optionally a video
  or audio take recorded in the browser. Ask for name, role, company and a photo, let me mark which
  of those are required, and stop recordings at three minutes.
- Re-encode recordings to MP4 with a thumbnail in a worker off the request path, keeping the
  original. If ffmpeg is absent, store the upload and flag it rather than failing the submission.
- Moderation screen at /inbox behind an .env password: newest first, approve or bin, plus free-text
  labels I can attach to a review.
- Now the part I actually care about. A display is either a hand-picked list or a saved filter. A
  filter can require a minimum rating, a media type, a photo on the reviewer, a job title, a named
  company, one of my labels, a phrase that must appear, and a phrase that must not.
- When a review is approved, every filter it satisfies picks it up immediately, with no republish
  step. Each display shows whether it is filtered or hand-picked and how many reviews it resolves to.
- Let me override a filtered display by pinning up to three reviews to the front of it.
- Embedding: a one-line script tag plus an empty div. Attach a shadow root and inject markup the
  server already rendered, so the host theme cannot restyle it and the quotes sit in the response.
- Three presentations: a masonry grid, a swipeable row, and a slow vertical ticker. Theme, accent
  and radius come off data- attributes.
- A standalone wall at /wall with my logo, a call to action, and Review plus AggregateRating
  structured data emitted from the same query that renders the cards.
- A score badge: rounded average and total count, recalculated whenever a review is approved or removed.
- Public endpoints get a per-IP throttle and an invisible decoy field, since anything with a form
  attracts bots.
- README covering the embed snippet, the .env keys, and running it behind nginx with a TLS cert.
- Not building: fetching reviews I already have on Google, Trustpilot, G2, Yelp or an app store, or
  polling them for new ones. That is a parser per platform plus someone maintaining them, which is
  not a weekend. Teammates and outbound review-request email are out too.

## Required capabilities

- a small always-on host
- somewhere to keep uploaded video and audio
- ffmpeg to convert recordings into a web-playable file

## 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 Feedspace.

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 =====
# Feedspace product brief

## Problem

The form, the queue, the wall and the embed widget are one sitting, as the two tools here show. What is not: 150+ sources with 120+ on auto-sync, on an extraction engine Feedspace owns in-house, and widgets that fill themselves from curation rules. Only collecting written reviews through your own form? Build it. Want the ones you have elsewhere kept current? Don't.

## Product outcome

A public form collects a written, video or audio review, you approve it, and saved filters decide which approved reviews appear on each embeddable widget and hosted wall page.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- a small always-on host
- somewhere to keep uploaded video and audio
- ffmpeg to convert recordings into a web-playable file

## Explicit non-goals for v1

- 150+ review sources with 120+ on auto-sync, on an extraction engine Feedspace owns and runs in-house
- the upkeep · sources change and keeping the fetching working is someone else's job
- rule-based curation · matching new reviews join a widget or wall page by themselves, so the embed never goes stale
- in-browser video and audio recording that works on mobile without an app, and the media pipeline behind it
- aggregate rating badges that stay accurate because the auto-syncs keep feeding them

## 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 me a testimonial system for my own site where the wall keeps itself up to date. Requirements:

- Python 3 with FastAPI, SQLite via sqlite3, Jinja2 for server-rendered HTML, and local disk for
  uploads. Single process, no Docker, no signup flow, no tenants. It serves one site: mine.
- Collection page: a shareable link taking a star rating, a written quote, and optionally a video
  or audio take recorded in the browser. Ask for name, role, company and a photo, let me mark which
  of those are required, and stop recordings at three minutes.
- Re-encode recordings to MP4 with a thumbnail in a worker off the request path, keeping the
  original. If ffmpeg is absent, store the upload and flag it rather than failing the submission.
- Moderation screen at /inbox behind an .env password: newest first, approve or bin, plus free-text
  labels I can attach to a review.
- Now the part I actually care about. A display is either a hand-picked list or a saved filter. A
  filter can require a minimum rating, a media type, a photo on the reviewer, a job title, a named
  company, one of my labels, a phrase that must appear, and a phrase that must not.
- When a review is approved, every filter it satisfies picks it up immediately, with no republish
  step. Each display shows whether it is filtered or hand-picked and how many reviews it resolves to.
- Let me override a filtered display by pinning up to three reviews to the front of it.
- Embedding: a one-line script tag plus an empty div. Attach a shadow root and inject markup the
  server already rendered, so the host theme cannot restyle it and the quotes sit in the response.
- Three presentations: a masonry grid, a swipeable row, and a slow vertical ticker. Theme, accent
  and radius come off data- attributes.
- A standalone wall at /wall with my logo, a call to action, and Review plus AggregateRating
  structured data emitted from the same query that renders the cards.
- A score badge: rounded average and total count, recalculated whenever a review is approved or removed.
- Public endpoints get a per-IP throttle and an invisible decoy field, since anything with a form
  attracts bots.
- README covering the embed snippet, the .env keys, and running it behind nginx with a TLS cert.
- Not building: fetching reviews I already have on Google, Trustpilot, G2, Yelp or an app store, or
  polling them for new ones. That is a parser per platform plus someone maintaining them, which is
  not a weekend. Teammates and outbound review-request email are out too.

## 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 Feedspace capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# Feedspace indie build

## Goal

Build the smallest trustworthy replacement for the core Feedspace workflow for one developer or a tiny team.

## Scope

A public form collects a written, video or audio review, you approve it, and saved filters decide which approved reviews appear on each embeddable widget and hosted wall page.

## 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:
- 150+ review sources with 120+ on auto-sync, on an extraction engine Feedspace owns and runs in-house
- the upkeep · sources change and keeping the fetching working is someone else's job
- rule-based curation · matching new reviews join a widget or wall page by themselves, so the embed never goes stale
- in-browser video and audio recording that works on mobile without an app, and the media pipeline behind it
- aggregate rating badges that stay accurate because the auto-syncs keep feeding them

If those capabilities are essential, use Shosay instead of pretending the gap is solved.

# 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

## Original build brief

Build me a testimonial system for my own site where the wall keeps itself up to date. Requirements:

- Python 3 with FastAPI, SQLite via sqlite3, Jinja2 for server-rendered HTML, and local disk for
  uploads. Single process, no Docker, no signup flow, no tenants. It serves one site: mine.
- Collection page: a shareable link taking a star rating, a written quote, and optionally a video
  or audio take recorded in the browser. Ask for name, role, company and a photo, let me mark which
  of those are required, and stop recordings at three minutes.
- Re-encode recordings to MP4 with a thumbnail in a worker off the request path, keeping the
  original. If ffmpeg is absent, store the upload and flag it rather than failing the submission.
- Moderation screen at /inbox behind an .env password: newest first, approve or bin, plus free-text
  labels I can attach to a review.
- Now the part I actually care about. A display is either a hand-picked list or a saved filter. A
  filter can require a minimum rating, a media type, a photo on the reviewer, a job title, a named
  company, one of my labels, a phrase that must appear, and a phrase that must not.
- When a review is approved, every filter it satisfies picks it up immediately, with no republish
  step. Each display shows whether it is filtered or hand-picked and how many reviews it resolves to.
- Let me override a filtered display by pinning up to three reviews to the front of it.
- Embedding: a one-line script tag plus an empty div. Attach a shadow root and inject markup the
  server already rendered, so the host theme cannot restyle it and the quotes sit in the response.
- Three presentations: a masonry grid, a swipeable row, and a slow vertical ticker. Theme, accent
  and radius come off data- attributes.
- A standalone wall at /wall with my logo, a call to action, and Review plus AggregateRating
  structured data emitted from the same query that renders the cards.
- A score badge: rounded average and total count, recalculated whenever a review is approved or removed.
- Public endpoints get a per-IP throttle and an invisible decoy field, since anything with a form
  attracts bots.
- README covering the embed snippet, the .env keys, and running it behind nginx with a TLS cert.
- Not building: fetching reviews I already have on Google, Trustpilot, G2, Yelp or an app store, or
  polling them for new ones. That is a parser per platform plus someone maintaining them, which is
  not a weekend. Teammates and outbound review-request email are out too.

## Required capabilities

- a small always-on host
- somewhere to keep uploaded video and audio
- ffmpeg to convert recordings into a web-playable file

## 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.

# 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.

# Feedspace product brief

## Problem

The form, the queue, the wall and the embed widget are one sitting, as the two tools here show. What is not: 150+ sources with 120+ on auto-sync, on an extraction engine Feedspace owns in-house, and widgets that fill themselves from curation rules. Only collecting written reviews through your own form? Build it. Want the ones you have elsewhere kept current? Don't.

## Product outcome

A public form collects a written, video or audio review, you approve it, and saved filters decide which approved reviews appear on each embeddable widget and hosted wall page.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- a small always-on host
- somewhere to keep uploaded video and audio
- ffmpeg to convert recordings into a web-playable file

## Explicit non-goals for v1

- 150+ review sources with 120+ on auto-sync, on an extraction engine Feedspace owns and runs in-house
- the upkeep · sources change and keeping the fetching working is someone else's job
- rule-based curation · matching new reviews join a widget or wall page by themselves, so the embed never goes stale
- in-browser video and audio recording that works on mobile without an app, and the media pipeline behind it
- aggregate rating badges that stay accurate because the auto-syncs keep feeding them

## 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

## Starting brief

Build me a testimonial system for my own site where the wall keeps itself up to date. Requirements:

- Python 3 with FastAPI, SQLite via sqlite3, Jinja2 for server-rendered HTML, and local disk for
  uploads. Single process, no Docker, no signup flow, no tenants. It serves one site: mine.
- Collection page: a shareable link taking a star rating, a written quote, and optionally a video
  or audio take recorded in the browser. Ask for name, role, company and a photo, let me mark which
  of those are required, and stop recordings at three minutes.
- Re-encode recordings to MP4 with a thumbnail in a worker off the request path, keeping the
  original. If ffmpeg is absent, store the upload and flag it rather than failing the submission.
- Moderation screen at /inbox behind an .env password: newest first, approve or bin, plus free-text
  labels I can attach to a review.
- Now the part I actually care about. A display is either a hand-picked list or a saved filter. A
  filter can require a minimum rating, a media type, a photo on the reviewer, a job title, a named
  company, one of my labels, a phrase that must appear, and a phrase that must not.
- When a review is approved, every filter it satisfies picks it up immediately, with no republish
  step. Each display shows whether it is filtered or hand-picked and how many reviews it resolves to.
- Let me override a filtered display by pinning up to three reviews to the front of it.
- Embedding: a one-line script tag plus an empty div. Attach a shadow root and inject markup the
  server already rendered, so the host theme cannot restyle it and the quotes sit in the response.
- Three presentations: a masonry grid, a swipeable row, and a slow vertical ticker. Theme, accent
  and radius come off data- attributes.
- A standalone wall at /wall with my logo, a call to action, and Review plus AggregateRating
  structured data emitted from the same query that renders the cards.
- A score badge: rounded average and total count, recalculated whenever a review is approved or removed.
- Public endpoints get a per-IP throttle and an invisible decoy field, since anything with a form
  attracts bots.
- README covering the embed snippet, the .env keys, and running it behind nginx with a TLS cert.
- Not building: fetching reviews I already have on Google, Trustpilot, G2, Yelp or an app store, or
  polling them for new ones. That is a parser per platform plus someone maintaining them, which is
  not a weekend. Teammates and outbound review-request email are out too.

## 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.

# 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.

# 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

## 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 Feedspace 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