Practical notes on MVP development, AI integrations, and improving existing products.
Notes from the workbench—what we are learning while building useful products.
Should your MVP be a web app or a mobile app? A practical comparison of cost, speed, distribution, and capabilities, plus a simple decision test and the sequencing that works for most startups.
Read MoreReact Native or fully native (Swift/Kotlin)? A practical framework for deciding based on performance needs, platform-specific features, team size, and budget, plus where the "write once" promise actually holds up.
Read MoreA realistic look at mobile app development costs in 2026: typical ranges by complexity and platform, what drives the price, React Native vs native cost differences, and hidden costs founders miss.
Read MoreWhat to actually look for when hiring an AI development team: the gap between demo experience and production experience, the interview questions that reveal it, red flags, and how to vet before committing.
Read MoreHow to test and evaluate LLM outputs before shipping: building a real evaluation set, choosing the right scoring method, what to actually measure, and running evaluation on every prompt, model, or retrieval change.
Read MorePractical ways to cut LLM API costs without hurting quality: right-sizing models by task, reducing token count, caching aggressively, fixing retry behavior, and what not to cut when trimming spend.
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