Vale construir sua versão de SnitchFeed?

Build a personal social-listening tool inspired by SnitchFeed, starting from an empty folder. This is an honest consolation build: it watches free, open feeds only and does not replace SnitchFeed's aggregation, tuned scoring, or automation layer.

Stack (use exactly this): Next.js 15 + TypeScript + PostgreSQL + BullMQ + Redis.

Core loop:
- Let me define keywords/brand terms and a poll interval per source.
- Poll Reddit's API, Bluesky's search, and Hacker News's Algolia API via a BullMQ worker on a cron schedule; dedupe matches by source + id.
- Score each match's relevance and sentiment with one LLM call (OpenAI or Anthropic); store both alongside the raw post.
- Push new matches to a Slack or Discord incoming webhook.
- Smallest polished UI that closes the loop: add a keyword, watch matches stream in, mark them read or irrelevant.

Rules:
- Single-user and private by default; all data in local Postgres.
- Every API key, app password, and webhook URL in .env, with .env.example provided; never log secrets; validate untrusted input.
- No analytics, telemetry, ads, or accounts beyond what's declared.
- Clear empty, loading, validation, success, and failure states.

Deliberately out of scope (do not fake these): X/Twitter and LinkedIn coverage, cross-platform aggregation and dedup at scale, continuously tuned relevance scoring and noise auditing, a live real-time dashboard, and any agent-facing API/MCP layer.

Finish line:
- Unit tests for the dedupe logic and the scoring call, plus one end-to-end smoke test: keyword added, fake match flows through to a webhook call.
- README covering setup, the exact free APIs used and their rate limits, and this build's limitations versus a paid multi-source listening tool.
- Scripts for install, development, test, build, and a production-style local run.
- Run the tests and the build before finishing; fix errors rather than describing them.