Can SnitchFeed be vibe coded?
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.
A focused 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.
Jump to the build brief ↓Legacy-calibrated assessment
Checked Aug 2026
What you pay today, before any DIY hosting
medium editorial confidence
Tracked separately from the pricing check
Buildability by layer
Screens, forms, and focused interactions
The repeatable job the product performs
Availability and legality of required data
Uptime, queues, support, and maintenance
Security, compliance, and user confidence
The achievable core
- 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.
- Draft, queue, and track content for the few networks you actually use.
- A responsive interface with real empty, loading, success, and error states.
The parts a prompt cannot buy
- 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
- The useful dataset is owned, accumulated, or expensive to reproduce.
- Connectors, OAuth flows, and vendor API changes require constant upkeep.
Build, switch, or keep paying
Narrower, with trade-offs
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.
Use the build brief ↓1 checked option
- F5Bot ↗Emails you when a keyword shows up on Reddit, Hacker News or Lobsters. No scoring, no dashboard, no X or LinkedIn · free and it never sleeps.
Recommended
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.
Visit SnitchFeed ↗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.
The useful dataset is owned, accumulated, or expensive to reproduce.
Connectors, OAuth flows, and vendor API changes require constant upkeep.
The last 20 percent is sync, migration fidelity, speed, and edge cases.
The brief
Context, requirements, acceptance criteria, non-goals, and the full production standard — as Markdown, ready for any coding agent.
Build brief — a focused alternative to SnitchFeed
Context
SnitchFeed — 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. It currently costs $59/mo.
A focused 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.
This brief describes a focused, single-operator replacement for the part of SnitchFeed 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
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.
Draft, queue, and track content for the few networks you actually use.
A responsive interface with real empty, loading, success, and error states.
Requirements
Functional
Bluesky app password for firehose/search access.
Hosted Postgres + a cron worker or queue.
Data and integrations
Reddit API app credentials (free).
Hacker News Algolia API (no key required).
OpenAI/Anthropic API key for relevance & sentiment tagging.
Slack/Discord incoming webhook URL(s).
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 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.
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:
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.
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.
Maintain every third-party integration as APIs and OAuth rules change.
Risk
Operational risk. The code is achievable; dependable data, integrations, and ongoing operations are the real cost.
Editorial confidence in this assessment: medium. No reviewed project implementation is linked yet.
Existing alternatives
Before building, compare these checked options:
F5Bot — Emails you when a keyword shows up on Reddit, Hacker News or Lobsters. No scoring, no dashboard, no X or LinkedIn · free and it never sleeps
Prior art
Working open-source software you can read, fork, or borrow from before starting:
Huginn — Self-hosted agent system for watching sites/feeds and triggering actions on events
Generated by Can It Be Vibe Coded? · Full report: https://www.canitbevibecoded.com/snitchfeed
You still own the product
- 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.
- Maintain every third-party integration as APIs and OAuth rules change.
Projects built from this idea
No reviewed implementation has been linked for SnitchFeed yet. A submission is evidence for review, not automatic proof that the whole product was replaced.
Built a version of SnitchFeed?Submit the project as evidence for this report.
Open-source prior art
Before you start
Can SnitchFeed be vibe coded?
Not faithfully. A focused 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.
What can an AI coding agent reproduce from SnitchFeed?
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. Draft, queue, and track content for the few networks you actually use. A responsive interface with real empty, loading, success, and error states.
What will a DIY SnitchFeed replacement still be missing?
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; The useful dataset is owned, accumulated, or expensive to reproduce.; Connectors, OAuth flows, and vendor API changes require constant upkeep.
What do I still own after building a SnitchFeed alternative?
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. Maintain every third-party integration as APIs and OAuth rules change.