# Build brief — a focused alternative to ArtificialWatch

> **Verdict:** Partly, if you narrow it · **Buildability:** 58/100 · **Category:** RSS Research
> **Source:** https://www.canitbevibecoded.com/artificialwatch
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from ArtificialWatch. Verify current pricing and capabilities before acting.

## Context

**ArtificialWatch** — Alerts you the minute a new frontier AI model goes live. It currently costs $19.99/mo.

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.

This brief describes a focused, single-operator replacement for the part of ArtificialWatch that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

## What you are building

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.

- Build a focused single-user workflow with real persistence, search, and export.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- An always-on box · a small VPS or a Pi, not a laptop.
- An ntfy or Pushover topic for push.

### Data and integrations

- API keys for the providers you want covered (OpenAI, Anthropic, Google, OpenRouter).

Each of these needs a real account, credential, or quota. Set them up before writing feature code.

### Non-functional

- Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
- Security: server-side secrets, validated input, and no credentials in the client bundle.
- Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
- Portability: the operator can export their data and leave without losing it.

## Implementation brief

Build 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.

## Delivery standard

- Inspect the repository first, then write a short implementation plan before writing code.
- Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
- Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
- Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
- Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
- Add structured logs around every external call and return actionable errors without leaking sensitive details.
- Write unit tests for the core logic and one automated test of the main user journey.
- Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

## Acceptance criteria

- [ ] A clean install starts the app using only the README and .env.example.
- [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
- [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
- [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
- [ ] Tests, type checking, linting, and a production build all pass with no ignored failures.
- [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

## Non-goals

Do not build these, and do not claim to have replaced them:

- 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.
- Reliability at the vendor's scale is an operations problem, not a prompt.
- The last 20 percent is sync, migration fidelity, speed, and edge cases.

## What you still own after launch

- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.

## Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.

## Prior art

Working open-source software you can read, fork, or borrow from before starting:

- [models.dev](https://github.com/anomalyco/models.dev) — open-source database of AI models, providers and pricing · the data layer a DIY watcher can diff against
- [changedetection.io](https://github.com/dgtlmoon/changedetection.io) — self-hosted page-change monitoring with notifications · covers the docs and pricing pages an API cannot

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