Insights on Building Better Software

The React Native choices that quietly determine how your team builds, releases, and recovers for years — and where EAS actually helps.

How early choices in building software quietly turn into expensive constraints later — and what smart teams do differently.

You've heard it said with total confidence, trusted like it was evidence. It isn't. Here's how a green test suite on one laptop becomes a failed checkout in production — and what reproducibility actually takes.

Modern AI can answer questions, write code, and take actions — but it can't see inside your private database. The Model Context Protocol is the open standard that fixes this, giving AI a safe, consistent way to connect to the systems where your data actually lives.

An AI agent that works in a demo and an AI agent that works in production are different systems. This guide covers what actually breaks — compounding step failure, tool design, silent partial success, unbounded loops, context growth — and the evaluation and guardrail work that separates the two.

A traditional FDA clearance freezes your AI device in place — every retrain or improvement risks a new submission. A Predetermined Change Control Plan is how you get cleared to keep updating. This guide covers what a PCCP is, its three required components, and what it demands of your engineering.

The price on the invoice is the smallest part of what an MVP costs you. This guide breaks down the true cost of building with an in-house team, an agency, or an AI-accelerated team in 2026 — including the time-to-market and rework costs nobody quotes upfront.

HIPAA makes you legal. FDA clearance makes you sellable to clinicians. Neither gets you into a hospital. This guide covers the integration standards, vendor marketplaces, and procurement realities that decide whether a healthcare AI product ships into real clinical environments — or sits on a shelf with a great demo.

AI API costs are predictable — once you know the levers. This guide covers every layer of the cost stack: prompt design, model tiering, caching, batching, RAG vs fine-tuning economics, and the monitoring infrastructure that keeps costs from compounding invisibly.