key numbers from my first meta ads campaign:. - 0.40€ avg cost per click.- 1.50€ per install.- 6.3% of installs upgrade to paid. metrics look healthy. scaling? not so sure yet.

Turns out, user engagement speaks volumes:. Took a closer look at time to adopt today. Wild how much it reveals. If it takes ages for users to use a feature after seeing it, that's a blinking red light. Usually means it's hidden, confusing, or doesn't seem valuable. Sometimes a.

4️⃣ Why this matters for building fast:. When your tools don't fight you, you ship faster. When types flow seamlessly, you debug less. When setup is simple, you focus on features. Hono + tRPC = more building, less configuring. What's your go-to for rapid prototyping? 👇.

3️⃣ The moment I knew I made the right choice:. Frontend automatically gets all my API types. No manual type definitions. No API documentation to maintain. Change backend → frontend knows instantly. Developer experience matters.

2️⃣ My actual development experience:. 10:00 AM: `npm create hono@latest` .10:15 AM: Added tRPC integration .10:45 AM: First API endpoint working .11:30 AM: Auth middleware implemented .12:00 PM: Multi-tenant routing done. This would've taken me a full day with Express.

1️⃣ What made Hono + tRPC so fast to implement:. ✅ Zero boilerplate - just define your routes .✅ TypeScript-first (no wrestling with types) .✅ Middleware that actually makes sense .✅ tRPC integration works out of the box . No configuration hell. Just code.

Why Hono + tRPC was the easiest backend setup I've ever done 🧵. Most devs overthink backend architecture. I got my entire analytics API running in 2 hours. Here's how stupidly simple it was.

built a whole dashboard to track feature adoption. now it’s just showing me which parts of my product are officially dead. is this what they mean by “insights”?.

5️⃣ the real lesson. community hype doesn't mean it's the right tool for your problem. postgres handles 90% of use cases just fine. sometimes boring tech is the best tech. trust your gut over the echo chamber. what tech choice saved your project?.

4️⃣ PostgreSQL advantages for analytics:. 🚀 JSONB: Best of both worlds - structured + flexible .🔗 JOINs: Proper relational queries .⚡ Partial Indexes: Query specific tenant data fast .🛡️ Row Level Security: Built-in multi-tenancy .📊 Window Functions: Real analytics queries.

3️⃣ the testing reality. built a small analytics prototype with both. postgres: clean queries, predictable performance. mongodb: constant schema headaches, weird aggregation syntax. you can probably guess which one shipped faster.

2️⃣ The MongoDB case everyone makes:. "Analytics = lots of events = documents = MongoDB". But here's what they don't tell you:.- No joins = duplicated data everywhere.- No transactions = data corruption risk .- Aggregation pipelines = complex and slow.- Multi-tenancy = security.

1️⃣ What analytics actually needs from a database:. ✅ ACID transactions (data integrity matters) .✅ Complex joins (users → projects → events) .✅ Time-series queries with proper indexing .✅ Real aggregations, not just document filtering .✅ Multi-tenant data isolation.

everyone kept telling me "just use MongoDB for analytics bro" but i went with postgres anyway and now i feel like i dodged a bullet 💀.the NoSQL hype was real but the results weren't. what actually happened 👇

started pushing harder on marketing my mobile apps lately. some days: spike in downloads, then crickets for a week. but with feature adoption tracking in my new saas @adopture, finally seeing those gentle, steady upticks on every feature. feels like progress, not just luck.

Hono + tRPC is actually such a vibe for building APIs. 8 endpoints, 12 tables, and somehow my analytics backend doesn't completely suck yet 📊. feels good when the stack just clicks, you know?.

just hit the 5 hour limit on claude after 2 hours of coding on the $100 plan. who thought "let's cap the expensive tier right when they're in flow state" was a good idea. now i'm staring at 3 hours of waiting or just going to bed defeated. feels like paying for the

Day 4 of building Adopture: 72 hours of coding later, here's what the MVP looks like 📱. 🎯 What's working:.- Feature adoption analytics with some widgets.- Real-time event tracking dashboard .- Mobile-responsive design (charts actually work on phones!).- Multi-tenant project

day 4 of building and my time-to-adopt calculations are more broken than a gas station bathroom lock. spent 6 hours "fixing" the algorithm and somehow made it worse. users are getting adoption predictions of 84 years for a simple todo app 💀. at this point i'm just shipping bugs

Learning about stickiness blew my mind. It's not just about users showing up,.but if they return to actually use that feature again and again. Quick check: daily active users divided by monthly active users. Low stickiness?. Start digging, churn might be next.