[verdict][curated prompt][source: snitchfeed.com]
Can I vibecode SnitchFeed?
// the value is the network, the data or the infrastructure
A weekend build gets you a keyword matcher; SnitchFeed is a signal refinery. Five platforms in one stream (Reddit, X, LinkedIn, Bluesky, Hacker News), run through layered filtering: boolean queries, AI scoring for relevance, sentiment, and buying intent, and noise auditing that keeps trimming. What comes out is a shortlist of threads worth answering, delivered where you act: Slack, Discord, a live dashboard, or your own AI agent via MCP and REST API. To be fair: if you only care about Reddit and Hacker News, a DIY build gets you further than the verdict suggests. It's the X and LinkedIn coverage, the cross-platform aggregation, and the tuned scoring that a one-shot build can't reach.
confidence: medium
what it is: Find buyers at peak intent: 24/7 monitoring of Reddit, X, LinkedIn, Bluesky, and Hacker News, AI-scored and delivered to your team or your agents in real time.
Buildability index · an editorial game
- Price 59 $/month weight: plus 3
- Time not a true replacement; consolation build in one to two days weight: minus 1
- Category social media no weight
- Moat proprietary data · integrations · execution quality weight: minus 4
- Confidence medium weight: zero
- What you lose 5 items weight: minus 2
- Site snitchfeed.com (si apre in una nuova scheda) no weight
The moat outweighs the price: rebuilding this is a project, not an evening.
Gioco editoriale: il verdetto dice se un agente può, l’indice se conviene.
What you build
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.
what you need
- 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.
The prompt
The verdict is NOT REALLY, and the prompt stays anyway: it does not replace the app, it rebuilds the part that really is code. The rest is the moat.
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.Curated prompt: written and reviewed by hand for this app. In English on purpose — it is the language coding agents work best in.
What you lose
- 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
Why people still pay
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: Proprietary data Integrations Execution quality what a moat is
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
Free alternatives
Not in the mood to build it? These already exist, they are free or open source, and we checked them one by one.
Rejected (1) — and why
- Google Alerts (si apre in una nuova scheda) — The default recommendation, but it watches Google's index rather than live threads · by the time it fires, the peak-intent window has closed.
Who has already built it
Starting from here is still vibecoding: the prompt is for when you want it exactly your way.
- Huginn (opens in a new tab) — Self-hosted agent system for watching sites/feeds and triggering actions on events.
Do you agree?
The vote balance
Ancora nessun voto: il tuo è il primo.
Nessun voto ancora — il primo pesa.
