I built this after shipping a few apps and realizing I had no idea what was breaking in production until a user told me. The problem with existing tools like Sentry is they were designed for engineering teams who can read stack traces and interpret p95 latencies. Most people building with AI tools today cannot, or they can but they want to move fast without becoming a DevOps engineer. VybeSec captures errors from both the browser and the backend (Node.js, Supabase Edge Functions, Cloudflare Workers, Deno, Python), runs each unique error fingerprint through an LLM to generate a plain-English explanation and root cause, and produces a ready-to-paste fix prompt tailored to the specific AI tool the founder used to build the app. Lovable gets a Lovable prompt, Cursor gets a Cursor prompt, Next.js gets a Next.js fix. The LLM layer uses Claude as primary with Gemini and OpenAI as fallbacks, plus a heuristic fallback for the most common error patterns such as undefined reads, ReferenceErrors, and network failures, so analysis never fully fails. Errors are fingerprinted by normalizing the type, message, and top stack frames so duplicate events group cleanly rather than flooding the feed. Stack: Cloudflare Workers for ingest, processing, LLM dispatch, alerts, and digest. Railway Postgres for analytics. D1 for auth, orgs, and config. Cloudflare Queue for buffering between ingest and processor. R2 for session replay storage. Resend for email. 14-day free trial, no card required. Happy to answer technical questions and would genuinely appreciate harsh feedback since this is still early.

VybeSec

Monitor browser & backend failures, get AI root causes fast!

Monitor browser & backend failures, get AI root causes fast!