Hey HN. I'm a solo dev who could never figure out what to actually do with my analytics data. I'd open Mixpanel, look at charts, and close the tab. The data was there but I didn't have the expertise to turn it into decisions.
So I built Lurnna. You add a 3.5kb script tag (vanilla TypeScript, zero dependencies, no cookies). It auto-captures page views, clicks, sessions, and JS errors. Every morning at 6 AM it analyzes the last 24 hours and sends you 3-5 plain-English insights with specific actions.
The insight engine works in four stages:
1. SDK captures events and batches them every 5s (uses sendBeacon on unload)
2. Events go into Supabase PostgreSQL with monthly partitioning
3. Daily cron aggregates into structured summaries
4. Summary gets sent to Claude Sonnet with a system prompt tuned for growth analysis. Returns categorized insights (retention, acquisition, engagement, anomalies) each with a specific action item
The SDK respects Do Not Track, uses sessionStorage instead of cookies, and never stores IPs. 3.5kb gzipped.
Stack: Next.js 14, Supabase, Claude API, Resend for email delivery, Vercel for hosting.
Still in private beta. Looking for devs with live web apps who want to test it. The main thing I need to validate is whether the AI insights are actually useful or just noise.
Happy to answer questions about the architecture or the prompt engineering approach.
Hey HN. I'm a solo dev who could never figure out what to actually do with my analytics data. I'd open Mixpanel, look at charts, and close the tab. The data was there but I didn't have the expertise to turn it into decisions.
So I built Lurnna. You add a 3.5kb script tag (vanilla TypeScript, zero dependencies, no cookies). It auto-captures page views, clicks, sessions, and JS errors. Every morning at 6 AM it analyzes the last 24 hours and sends you 3-5 plain-English insights with specific actions.
The insight engine works in four stages:
1. SDK captures events and batches them every 5s (uses sendBeacon on unload) 2. Events go into Supabase PostgreSQL with monthly partitioning 3. Daily cron aggregates into structured summaries 4. Summary gets sent to Claude Sonnet with a system prompt tuned for growth analysis. Returns categorized insights (retention, acquisition, engagement, anomalies) each with a specific action item
The SDK respects Do Not Track, uses sessionStorage instead of cookies, and never stores IPs. 3.5kb gzipped.
Stack: Next.js 14, Supabase, Claude API, Resend for email delivery, Vercel for hosting.
Still in private beta. Looking for devs with live web apps who want to test it. The main thing I need to validate is whether the AI insights are actually useful or just noise.
Happy to answer questions about the architecture or the prompt engineering approach.