Vibecode ArtificialWatch

You are building a lean indie version of ArtificialWatch.

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 =====
# ArtificialWatch indie build

## Goal

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

## Scope

Polls every provider's model-list endpoint on a one-minute cron, diffs new model ids against a local database, and pushes an alert to your phone.

## 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:
- coverage of labs you hold no key for · 48 tracked models including Chinese labs and restricted previews
- the phone call that rings until you answer, and SMS · Twilio plus US A2P 10DLC registration
- the pre-launch watchlist and live Polymarket odds on models that have not shipped, which no API can return
- debounce and alias filtering tuned so dated snapshots and -preview ids do not fire false alarms
- someone else owning the uptime · your poller sleeps when your machine does

If those capabilities are essential, use models.dev 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 new-AI-model launch alerter to replace ArtificialWatch. Requirements:

- Node 22 + node-cron + better-sqlite3, one process on a small VPS under pm2 so
  it never sleeps · a laptop that naps misses the launch.
- Every 60 seconds, GET the model-list endpoints for the keys in .env: OpenAI
  /v1/models, Anthropic /v1/models, Google generativelanguage /v1beta/models,
  and OpenRouter /api/v1/models, which covers labs I have no account with.
- Store every model id ever seen in SQLite. A launch is an id new to that table
  · seed it on first run so the first sweep is silent.
- Debounce: an id fires only after two consecutive sweeps, and ids matching a
  regex list in config.json (dated snapshots, -preview, -latest) never fire.
- Alert by POSTing to an ntfy.sh topic: model id as the title, provider plus
  context window and per-token price as the body, link to the provider's docs.
- Append each confirmed launch to launches.md as `YYYY-MM-DD · provider · id`.
- One page on localhost:8080: last 50 launches, last good sweep per provider,
  red banner when a provider has errored 10 minutes · a silent poller is worse
  than none.
- Out of scope: SMS and phone calls (Twilio plus US A2P 10DLC registration is
  paperwork, not code) and any watchlist of unshipped models.
- README with the four .env keys and the pm2 command.

## Required capabilities

- API keys for the providers you want covered (OpenAI, Anthropic, Google, OpenRouter)
- an always-on box · a small VPS or a Pi, not a laptop
- an ntfy or Pushover topic for push

## 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 ArtificialWatch.

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 =====
# ArtificialWatch indie build

## Goal

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

## Scope

Polls every provider's model-list endpoint on a one-minute cron, diffs new model ids against a local database, and pushes an alert to your phone.

## 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:
- coverage of labs you hold no key for · 48 tracked models including Chinese labs and restricted previews
- the phone call that rings until you answer, and SMS · Twilio plus US A2P 10DLC registration
- the pre-launch watchlist and live Polymarket odds on models that have not shipped, which no API can return
- debounce and alias filtering tuned so dated snapshots and -preview ids do not fire false alarms
- someone else owning the uptime · your poller sleeps when your machine does

If those capabilities are essential, use models.dev 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 new-AI-model launch alerter to replace ArtificialWatch. Requirements:

- Node 22 + node-cron + better-sqlite3, one process on a small VPS under pm2 so
  it never sleeps · a laptop that naps misses the launch.
- Every 60 seconds, GET the model-list endpoints for the keys in .env: OpenAI
  /v1/models, Anthropic /v1/models, Google generativelanguage /v1beta/models,
  and OpenRouter /api/v1/models, which covers labs I have no account with.
- Store every model id ever seen in SQLite. A launch is an id new to that table
  · seed it on first run so the first sweep is silent.
- Debounce: an id fires only after two consecutive sweeps, and ids matching a
  regex list in config.json (dated snapshots, -preview, -latest) never fire.
- Alert by POSTing to an ntfy.sh topic: model id as the title, provider plus
  context window and per-token price as the body, link to the provider's docs.
- Append each confirmed launch to launches.md as `YYYY-MM-DD · provider · id`.
- One page on localhost:8080: last 50 launches, last good sweep per provider,
  red banner when a provider has errored 10 minutes · a silent poller is worse
  than none.
- Out of scope: SMS and phone calls (Twilio plus US A2P 10DLC registration is
  paperwork, not code) and any watchlist of unshipped models.
- README with the four .env keys and the pm2 command.

## Required capabilities

- API keys for the providers you want covered (OpenAI, Anthropic, Google, OpenRouter)
- an always-on box · a small VPS or a Pi, not a laptop
- an ntfy or Pushover topic for push

## 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 ArtificialWatch.

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 =====
# ArtificialWatch product brief

## Problem

The core loop is a genuine one-sitting build: poll the model-list endpoint of every provider you hold a key for, diff the ids against a local table, push the new ones to your phone. What does not one-shot is the three things a launch alert is actually judged on. Coverage · your script sees only the labs you have accounts with, while the watchlist here runs to 48 models including Chinese labs, restricted previews and things that have not shipped at all, which no API returns. Telephony · a call that rings until you answer means Twilio, a purchased number and US A2P 10DLC registration, which is days of paperwork before a line of code. And uptime, which is the whole product · a poller on a laptop that slept through the drop is worth nothing, and the second-sweep debounce that keeps preview aliases from crying wolf is the part you only tune after it has already cried wolf twice.

## Product outcome

Polls every provider's model-list endpoint on a one-minute cron, diffs new model ids against a local database, and pushes an alert to your phone.

## Target user

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

## Required capabilities

- API keys for the providers you want covered (OpenAI, Anthropic, Google, OpenRouter)
- an always-on box · a small VPS or a Pi, not a laptop
- an ntfy or Pushover topic for push

## Explicit non-goals for v1

- coverage of labs you hold no key for · 48 tracked models including Chinese labs and restricted previews
- the phone call that rings until you answer, and SMS · Twilio plus US A2P 10DLC registration
- the pre-launch watchlist and live Polymarket odds on models that have not shipped, which no API can return
- debounce and alias filtering tuned so dated snapshots and -preview ids do not fire false alarms
- someone else owning the uptime · your poller sleeps when your machine does

## 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 new-AI-model launch alerter to replace ArtificialWatch. Requirements:

- Node 22 + node-cron + better-sqlite3, one process on a small VPS under pm2 so
  it never sleeps · a laptop that naps misses the launch.
- Every 60 seconds, GET the model-list endpoints for the keys in .env: OpenAI
  /v1/models, Anthropic /v1/models, Google generativelanguage /v1beta/models,
  and OpenRouter /api/v1/models, which covers labs I have no account with.
- Store every model id ever seen in SQLite. A launch is an id new to that table
  · seed it on first run so the first sweep is silent.
- Debounce: an id fires only after two consecutive sweeps, and ids matching a
  regex list in config.json (dated snapshots, -preview, -latest) never fire.
- Alert by POSTing to an ntfy.sh topic: model id as the title, provider plus
  context window and per-token price as the body, link to the provider's docs.
- Append each confirmed launch to launches.md as `YYYY-MM-DD · provider · id`.
- One page on localhost:8080: last 50 launches, last good sweep per provider,
  red banner when a provider has errored 10 minutes · a silent poller is worse
  than none.
- Out of scope: SMS and phone calls (Twilio plus US A2P 10DLC registration is
  paperwork, not code) and any watchlist of unshipped models.
- README with the four .env keys and the pm2 command.

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

# ArtificialWatch indie build

## Goal

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

## Scope

Polls every provider's model-list endpoint on a one-minute cron, diffs new model ids against a local database, and pushes an alert to your phone.

## 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:
- coverage of labs you hold no key for · 48 tracked models including Chinese labs and restricted previews
- the phone call that rings until you answer, and SMS · Twilio plus US A2P 10DLC registration
- the pre-launch watchlist and live Polymarket odds on models that have not shipped, which no API can return
- debounce and alias filtering tuned so dated snapshots and -preview ids do not fire false alarms
- someone else owning the uptime · your poller sleeps when your machine does

If those capabilities are essential, use models.dev 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 new-AI-model launch alerter to replace ArtificialWatch. Requirements:

- Node 22 + node-cron + better-sqlite3, one process on a small VPS under pm2 so
  it never sleeps · a laptop that naps misses the launch.
- Every 60 seconds, GET the model-list endpoints for the keys in .env: OpenAI
  /v1/models, Anthropic /v1/models, Google generativelanguage /v1beta/models,
  and OpenRouter /api/v1/models, which covers labs I have no account with.
- Store every model id ever seen in SQLite. A launch is an id new to that table
  · seed it on first run so the first sweep is silent.
- Debounce: an id fires only after two consecutive sweeps, and ids matching a
  regex list in config.json (dated snapshots, -preview, -latest) never fire.
- Alert by POSTing to an ntfy.sh topic: model id as the title, provider plus
  context window and per-token price as the body, link to the provider's docs.
- Append each confirmed launch to launches.md as `YYYY-MM-DD · provider · id`.
- One page on localhost:8080: last 50 launches, last good sweep per provider,
  red banner when a provider has errored 10 minutes · a silent poller is worse
  than none.
- Out of scope: SMS and phone calls (Twilio plus US A2P 10DLC registration is
  paperwork, not code) and any watchlist of unshipped models.
- README with the four .env keys and the pm2 command.

## Required capabilities

- API keys for the providers you want covered (OpenAI, Anthropic, Google, OpenRouter)
- an always-on box · a small VPS or a Pi, not a laptop
- an ntfy or Pushover topic for push

## 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.

# ArtificialWatch product brief

## Problem

The core loop is a genuine one-sitting build: poll the model-list endpoint of every provider you hold a key for, diff the ids against a local table, push the new ones to your phone. What does not one-shot is the three things a launch alert is actually judged on. Coverage · your script sees only the labs you have accounts with, while the watchlist here runs to 48 models including Chinese labs, restricted previews and things that have not shipped at all, which no API returns. Telephony · a call that rings until you answer means Twilio, a purchased number and US A2P 10DLC registration, which is days of paperwork before a line of code. And uptime, which is the whole product · a poller on a laptop that slept through the drop is worth nothing, and the second-sweep debounce that keeps preview aliases from crying wolf is the part you only tune after it has already cried wolf twice.

## Product outcome

Polls every provider's model-list endpoint on a one-minute cron, diffs new model ids against a local database, and pushes an alert to your phone.

## Target user

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

## Required capabilities

- API keys for the providers you want covered (OpenAI, Anthropic, Google, OpenRouter)
- an always-on box · a small VPS or a Pi, not a laptop
- an ntfy or Pushover topic for push

## Explicit non-goals for v1

- coverage of labs you hold no key for · 48 tracked models including Chinese labs and restricted previews
- the phone call that rings until you answer, and SMS · Twilio plus US A2P 10DLC registration
- the pre-launch watchlist and live Polymarket odds on models that have not shipped, which no API can return
- debounce and alias filtering tuned so dated snapshots and -preview ids do not fire false alarms
- someone else owning the uptime · your poller sleeps when your machine does

## 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 new-AI-model launch alerter to replace ArtificialWatch. Requirements:

- Node 22 + node-cron + better-sqlite3, one process on a small VPS under pm2 so
  it never sleeps · a laptop that naps misses the launch.
- Every 60 seconds, GET the model-list endpoints for the keys in .env: OpenAI
  /v1/models, Anthropic /v1/models, Google generativelanguage /v1beta/models,
  and OpenRouter /api/v1/models, which covers labs I have no account with.
- Store every model id ever seen in SQLite. A launch is an id new to that table
  · seed it on first run so the first sweep is silent.
- Debounce: an id fires only after two consecutive sweeps, and ids matching a
  regex list in config.json (dated snapshots, -preview, -latest) never fire.
- Alert by POSTing to an ntfy.sh topic: model id as the title, provider plus
  context window and per-token price as the body, link to the provider's docs.
- Append each confirmed launch to launches.md as `YYYY-MM-DD · provider · id`.
- One page on localhost:8080: last 50 launches, last good sweep per provider,
  red banner when a provider has errored 10 minutes · a silent poller is worse
  than none.
- Out of scope: SMS and phone calls (Twilio plus US A2P 10DLC registration is
  paperwork, not code) and any watchlist of unshipped models.
- README with the four .env keys and the pm2 command.

## 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 ArtificialWatch 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