Flatworld Solutions

Our Laravel dashboard was timing out. 177,000+ tickets, multiple joins, and an Excel export that just... hung. The problem wasn't Laravel. It was how we were querying. We had a leftJoinSub fetching the last assignment for every ticket — one massive subquery trying to do everything at once. On 27,000+ tickets per month, it choked. The fix was deceptively simple: → Replaced the monolithic join with chunked batch fetching (3,000–5,000 records per batch) → Used PHP collections to key and merge results instead of asking the database to do it all → For Excel exports — stopped using view rendering, switched to direct collection-based exports → Eliminated duplicate totals queries by calculating aggregations in PHP during the same loop Result: Dashboard that was timing out now loads in under 3 seconds. Excel exports that hung indefinitely now complete in seconds. Sometimes the best database optimization isn't a better query — it's knowing when to stop asking the database to do everything. What's the biggest performance win you've gotten from rethinking your approach instead of adding indexes? #laravel #PHP #backend #DBOptimization

The honest truth about AI coding assistants on my Android projects Six months ago, I was skeptical. Today, AI tools write somewhere between 30–40% of the code in my Android projects. But the headline number hides what actually matters. Here's where AI assistants genuinely pull their weight on Android work: Boilerplate that used to eat my afternoons Room entities, DAOs, Hilt modules, ViewModel scaffolding, Compose previews, data class mappers. The stuff that's "easy but tedious" is exactly where AI shines. I can describe a screen's data needs and get 80% of the plumbing in seconds. Migrations and refactors Converting XML layouts to Compose. Moving from LiveData to StateFlow. Rewriting callback chains as coroutines. AI handles the repetitive transformation work, I handle the judgment calls. Tests I would have skipped This is the underrated win. I now write unit tests for edge cases I used to shrug off because "the happy path works." AI lowers the activation energy enough that coverage actually improves. Where it still fails me 1. Gradle issues — confidently wrong, every time 2. Anything involving a recent Jetpack library update past its training cutoff 3. Architecture decisions that need context about my codebase, not Android in general 4. Subtle Compose recomposition bugs (it'll suggest fixes that don't address the root cause) What actually changed how I work I stopped treating AI as autocomplete and started treating it as a junior pair. I describe intent, review output critically, and push back when something feels off. The 10x productivity claims are nonsense. The 1.3x is real, and compounding. The engineers getting the most out of these tools aren't the ones who trust them most — they're the ones who review most carefully. What's your experience been? Curious whether other Android devs are seeing the same patterns. #AndroidDev #AI #DeveloperProductivity #Kotlin #JetpackCompose