Ideas on engineering, technical decisions, and systems in production
These are iOS engineering insights on judgment, applied AI, and real trade-offs — written from experience with iOS products already in production.
This isn’t a tutorials section or a tools roundup. There’s no shortage of content teaching syntax or walking through a new framework’s getting-started guide. What’s harder to find is honest writing about the decisions behind the code — the ones that don’t have a clear right answer, and the ones that only look wrong in hindsight.
Here I write about the technical decisions that determine whether an iOS system scales, becomes fragile, or ends up getting rewritten. Most of what I cover comes directly from work on production apps — the kind with real users, real deadlines, and real consequences when something goes wrong.
These pieces are based on real experience in iOS architecture and AI, working on products that already have users, pressure, and consequences. I’m not interested in theoretical best practices that fall apart under real production load — everything here has been tested against an actual codebase, not a demo.
Most articles are written in English, since I work with international teams and contexts.
Engineering Judgment
How to think when there’s no obvious answer, and the right call for your iOS architecture isn’t in any documentation. These pieces focus on the reasoning behind a decision, not just the decision itself — because the same choice can be right in one context and wrong in another.
AI & Decision-Making
Where AI genuinely belongs inside an iOS codebase, where it doesn’t, and how to tell the difference before you commit to it. Most AI advice right now is either hype or a toy demo — this is about what actually holds up in a production system.
Scaling & Trade-offs
What actually breaks first as an iOS app scales, and the architectural trade-offs worth making before that happens. These posts cover the early warning signs most teams miss until it’s already expensive to fix.
If these iOS engineering insights resonate, it’s usually because
your system is already facing similar decisions.
I work with teams before those decisions
become expensive.
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