Pot să vibecodez SnitchFeed?

Ce construiești

Poll a couple of free, open feeds (Reddit's API, Bluesky's firehose, Hacker News's Algolia API) for keyword matches, run each hit through an LLM for a rough relevance/sentiment tag, and push matches to a Slack or Discord webhook.

ce îți trebuie

• Reddit API app credentials (free)
• Bluesky app password for firehose/search access
• Hacker News Algolia API (no key required)
• OpenAI/Anthropic API key for relevance & sentiment tagging
• hosted Postgres + a cron worker or queue
• Slack/Discord incoming webhook URL(s)

Credibility row: the moat is the refinement stack (five platforms in one stream, layered noise reduction, one-click actioning), not the interface. One of the few tools here built to be used by AI agents via MCP, not just replaced by one.

Promptul

Verdictul e NU CHIAR, iar promptul rămâne oricum: nu înlocuiește aplicația, reconstruiește partea care chiar e cod. Restul e moat-ul.

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.

Ce pierzi

• X/Twitter and LinkedIn coverage entirely (neither has a free public search API), so two of the five platforms are simply gone
• one aggregated, deduplicated stream across five platforms instead of five half-working pollers
• the noise-reduction stack: boolean query grammar, AI fit scores, sentiment, intent tags, and automated noise auditing that keeps tuning what gets through
• a real-time dashboard with curated feeds, saved views, and analytics reports instead of a Slack ping you learn to ignore
• an agent-native surface: an MCP server and public REST API so your own AI agents can search mentions, create listeners, and act on intent directly

De ce se plătește în continuare

Because a mention here isn't something to read, it's something to act on. Someone posting 'what tool does X?' is at peak intent, and that window closes in hours; SnitchFeed hands you that thread scored and tagged, then feeds the follow-up: webhooks into outreach stacks like Clay and HeyReach, or an AI agent working the queue over MCP. A DIY matcher can find mentions; it can't power a pipeline.

moat: Date proprietare Integrări Calitatea execuției ce e un moat

the moat is the refinement stack, not any single feed: five platforms aggregated into one stream, layered noise reduction (query grammar, AI scoring, noise auditing) that turns it into a shortlist, and one-click paths to act on it, for humans via Slack/Discord/dashboard and for AI agents via a real MCP server and REST API

Alternative gratuite

N-ai chef să ți-o construiești? Astea există deja, sunt gratuite sau open source, și le-am verificat una câte una.

Cine l-a construit deja

Să pornești de aici e tot vibecoding: promptul e pentru când o vrei exact în felul tău.

• Huginn (se deschide într-o filă nouă) — Self-hosted agent system for watching sites/feeds and triggering actions on events.

Ești de acord?

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