Blog

Every day, consumers are bombarded with over 6,000 marketing messages. Most fade into the background. Here's the brain science that makes messages stick.

Building mobile games taught me more than any textbook ever could. Here's what I learned shipping real products to real users.

Creating Animetous showed me how to blend machine learning with native iOS development. The technical challenges were worth it.

Quality isn't just about finding bugs. It's about building confidence in your product before users ever see it.

AI-powered testing tools are changing what QA engineers do every day. Here's an honest look at what's actually useful, what's overhyped, and what skills matter now.

Getting consistent, useful output from LLMs isn't magic — it's a craft. These are the techniques that actually work in production.

AI outputs are non-deterministic. Traditional test cases break down. Here's how I approach testing features powered by language models and machine learning.

Language models confidently make things up. Understanding why this happens — and how to build around it — is now a core engineering skill.

From AI-generated assets to procedural dialogue, the tools are impressive — but knowing when to use them changes everything.

Remote Config isn't just a feature flag tool. For QA engineers, it's the fastest way to test edge cases, validate rollouts, and catch regressions before real users do.

I've published apps under Dainty Apps Lab. The gap between 'app works' and 'app is live' is bigger than anyone tells you.

Not a listicle of 50 tools. Just an honest breakdown of which AI tools genuinely changed how I work — and which ones I quietly stopped using.

Everyone has an opinion. Here's mine, from someone who tests AI-powered apps for a living: the job changes, not disappears.

User acquisition funnels fail in ways that standard QA misses entirely. Here's how testing for TikTok, Facebook, and Google Ads taught me to think differently.