# How to Vibe Code Your Own SnitchFeed (and Stop Paying for It) > Intent-Based Social Listening for Startups & SMBs - Site: https://snitchfeed.com - Category: Marketing & GTM SaaS - Verdict: **Solid side project** (68/100 vibecodeable) - Estimated effort: 2-3 weeks part-time ## Verdict You can build a functional personal clone of SnitchFeed in 2-3 weeks, but maintaining stable scrapers for locked-down social platforms will require constant debugging. The core loop—accepting an ICP description, ingesting mentions, scoring them with an LLM, and generating response drafts—is entirely within reach for a solo developer using modern AI tools. However, writing robust ingestion adapters for platforms like LinkedIn and X without official enterprise APIs is a perpetual cat-and-mouse game. For personal use, you can scope down the ingestion to public RSS feeds, Reddit, Bluesky, and Hacker News, keeping the core value intact without enterprise scraping infrastructure. ### What you can't replicate - Official rate-limited partnerships and high-tier enterprise platform access - The exact user community and brand trust built by the founder - Real-time guaranteed delivery across heavily locked-down enterprise networks like LinkedIn ## What it does An AI-powered social listening and GTM lead generation platform that monitors public conversations, scores buying intent, and drafts contextual replies. ### Core features - Boolean keyword listener configuration and Boolean query engine - Multi-platform ingestion pipeline (Reddit, Bluesky, Hacker News, and simulated X/LinkedIn feeds) - AI scoring pipeline for relevance, sentiment, and buying intent classification - Response profile system with voice sliders and platform rules - AI reply generation dialog with text transforms and character limit enforcement - Analytics saved reports with customizable charts and filters - Webhook and alert dispatcher (Slack, Discord, webhooks) - MCP (Model Context Protocol) server and API endpoints for developer access ## The business ### Pricing - Starter: $47/mo - Pro: $95/mo - Enterprise: $399+/mo Founded 2024. Team size: 1-5. ## The hard parts - Building and maintaining reliable data ingestion pipelines across social platforms without getting rate-limited or blocked - Designing a low-latency LLM scoring pipeline that accurately distinguishes casual mentions from true buying intent at scale - Implementing an MCP server and clean REST API surface alongside a responsive web dashboard - Managing token costs, credit consumption tracking, and background worker queues for scheduled searches ## How to vibe code SnitchFeed ### Prerequisites - Node.js (free): Required runtime for the Next.js full-stack application and background worker processes. - GitHub (free): Version control and deployment integration for Vercel and background runners. - Anthropic API Key (pay-as-you-go): Powers intent scoring, sentiment analysis, and AI response draft generation. ### Recommended AI tools - Claude Code: Best-in-class terminal agent for scaffolding the full-stack Next.js app, setting up database schemas, and writing complex background worker logic. - Cursor: Essential for iterative UI component work, styling dashboards, and refining complex React state logic. ### Stack - Frontend: Next.js with React, Tailwind CSS, and shadcn/ui components - Backend: Next.js API routes and server actions with background worker queues - Database: Neon (Serverless Postgres) for relational data, search queries, and mentions storage - Auth: better-auth for self-hosted TypeScript authentication - Payments: Stripe (optional for personal use) - Other: Vercel AI SDK for streaming LLM response drafts, Inngest for durable background keyword scans and cron triggers, Resend for transactional email alerts ### Hosting - Vercel (Hosting the Next.js frontend, API routes, and serverless functions): $0/mo (Hobby tier) - Neon (Serverless Postgres database storage for listeners, mentions, and profiles): $0/mo (Free tier) ### Build guide 1. **Project Scaffolding & Database Schema** — Initialize the Next.js application with Tailwind CSS and shadcn/ui, configure better-auth, and set up the Neon Postgres schema for users, listeners, mentions, and response profiles. ``` Create a new Next.js project with TypeScript, Tailwind CSS, and App Router. Set up better-auth with email/password authentication connected to a Neon PostgreSQL database using Drizzle ORM. Create database migrations and models for users, workspaces, listeners (storing boolean query strings and target platforms), mentions (storing title, content, url, author, sentiment, fit_score, intent_tags, and timestamps), response_profiles (storing goals, voice sliders for tone/length/assertiveness/brand_plug, and platform examples), and bookmarks. Ensure clean folder organization and robust TypeScript types across all models. ``` 2. **Ingestion Pipeline & Background Scanners** — Build ingestion workers using Inngest and cron schedules to fetch public posts from Reddit, Bluesky, and Hacker News matching boolean query terms. ``` Implement background ingestion jobs using Inngest or scheduled API routes in Next.js. Create robust fetchers for public RSS feeds, Reddit API, Bluesky public API, and Hacker News Firebase API. Write a boolean query matching utility that parses strings with AND, OR, and NOT operators against incoming post titles and bodies. Store incoming matches in the mentions table while de-duplicating by post ID and tracking credit usage. ``` 3. **AI Intent Scoring & Filtering Pipeline** — Implement the LLM scoring engine using the Vercel AI SDK and Anthropic API to evaluate relevance, sentiment, and buying intent tags for every ingested mention. ``` Build an AI scoring pipeline service using the Vercel AI SDK and Anthropic API. When a new mention is ingested, pass its text and the listener's ICP description to Claude Haiku/Sonnet using structured JSON generation. The model must return a Fit Score (0-100), sentiment classification (positive, neutral, negative), intent tags (e.g. buying intent, competitor mention, brand crisis, noise), and a brief justification. Automatically drop mentions that fail relevance thresholds or match explicit exclusion rules. ``` 4. **Dashboard Feed, Filtering, and Bookmarks** — Create the main mentions feed UI with advanced filtering, search, Fit Score sorting, sentiment badges, and bookmarking capabilities. ``` Build the primary dashboard feed interface in Next.js using shadcn/ui. Implement a filter sidebar supporting time ranges, platform selection, Fit Score bands, sentiment filters, and intent tags. Display mention cards with author metadata, snippet content, AI scoring badges, and action buttons. Add state management for bookmarking mentions, marking them as seen/unseen, and archiving items. ``` 5. **AI Response Profiles & Generation Dialog** — Build the response profile editor and the 'Respond' interactive modal supporting tone adjustments, delivery modes (public reply vs DM), text transforms, and character limits. ``` Build the AI response feature set: a profile management CRUD interface under /ai/response-profiles supporting goals, voice sliders (tone, length, assertiveness, brand plug), and platform examples. Next, build the 'Respond' dialog component that opens from any mention card. It must display the mention context on the left and a draft workspace on the right. Use Vercel AI SDK to stream AI drafts based on the selected profile, workspace brand context, and per-mention tweak settings. Include transform buttons (shorten, expand, rephrase, less salesy, more human), version history navigation, platform character limit enforcement (e.g., Bluesky 300, LinkedIn 1250, Reddit 1000), and copy/open-url actions. ``` 6. **Analytics Saved Reports & Export** — Implement customizable chart reports and CSV export functionality for filtering and analyzing mentions trends over time. ``` Create an Analytics Reports builder page where users can configure saved charts. Support metrics (Mentions, Unique Authors, Avg. Sentiment, Avg. Fit Score), grouping dimensions (Day, Platform, Sentiment, Fit Score, Intents, Keyword), breakdowns, and chart types (Line, Area, Stacked Bar, Donut) using a charting library like Recharts. Implement CSV export server actions that serialize filtered mention data with flat pagination and retention limits. ``` 7. **Alerts, Webhooks, and MCP Server Integration** — Implement real-time alert dispatchers for Slack and webhooks, alongside a Model Context Protocol (MCP) server endpoint for querying mentions from external AI tools. ``` Implement notification dispatchers that trigger webhook payloads or Slack/Discord alerts the moment a high-intent mention is scored and saved. Additionally, build an MCP server endpoint (or custom API routes adhering to Model Context Protocol standards) allowing external clients like Claude or Cursor to securely query workspace mentions, check listener stats, and retrieve summaries using API keys. ``` ### Cost vs paying **Starting costs (one-time):** - Domain name (optional): $12 one-time - AI coding tool subscription: $20 one-time - Total: ~$32 one-time **Ongoing costs (monthly):** - Anthropic API usage for scoring & generation: ~$10-25/mo - Vercel & Neon hosting: $0/mo - Total: ~$15-25/mo - Paying for the SaaS instead: $95/mo (Pro Plan) - Build time: 35-50 hours - AI tool credits: $20 (Claude Code / Cursor Pro) - Break-even: Immediate if replacing a paid subscription with personal scraping limits ## Sources - [SnitchFeed Marketing & Home Page](https://snitchfeed.com) - [SnitchFeed Pricing Page](https://snitchfeed.com/pricing) - [SnitchFeed Documentation - AI-Generated Responses & Profiles](https://snitchfeed.com/docs/features/ai-responses) - [SnitchFeed Documentation - Analytics & Reports](https://snitchfeed.com/docs/features/analytics)