Building a Full-Stack Habit Tracker with Claude Code - Part 2: Polish, Testing & Deployment Taking the habit tracker from MVP to production-ready with categories, analytics, comprehensive testing, and Vercel deployment In [Part 1], we built a fully functional habit tracker MVP in about 6-8 hours using Claude Code as our AI pair programmer. We had: ✅ Basic CRUD operations for habits ✅ Date-based
Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
The "Deploy" button is not a self-destruct mechanism for your career, despite what your brain screams. We’ve all been there: you’ve poured hours into a project, the code is (mostly) working locally, and then you stare at that final, terrifying button. The one that says "Deploy". It's a mental roadblock, a sudden surge of "what ifs" that can paralyze even experienced developers. But here's the secr
Why Does Switching Embedding Models Make Such a Huge Difference? In the first four articles, we built the RAG pipeline, tuned parameters, and mastered chunking strategies. But there's one question we haven't dived into: After your documents are chunked, how do they become vectors? This process is called Embedding. It transforms human-readable text into machine-computable vectors. The choice of E