I share my thoughts through occasional writing, covering topics that intersect technology, leadership, and the ever-evolving landscape of software engineering.
AI dramatically accelerated individual coding and local execution. End-to-end delivery barely moved.
We optimized leaf-node execution but left the tree structure completely manual. Business intent (“add fraud detection”) decomposes into tasks across teams, each carrying implicit assumptions that only conflict during integration. Performance budgets, data freshness requirements, and retry semantics remain undiscovered until week 8 of 10.
AI can breeze through well-defined tasks with deterministic verification. But getting from business requirement to that well-defined task? That’s where projects die.
The first real test of your infrastructure-as-code understanding: creating the Terraform state backend when it doesn't exist yet. My personal reference for handling the bootstrap problem across different scenarios. Covers production failure patterns, the bootstrap module approach that survives audits, migrating existing infrastructure, S3-compatible backend quirks, and a complete checklist. ~15 minutes read that might save you days of recovery work.
Every infrastructure project starts with the same question: workspaces or folders? One repo or many? I've structured Terraform for multiple environments six different ways. Watched a senior engineer scale prod down to dev instance counts because workspace selection is invisible. Seen AWS bills spike 40% because someone copy-pasted the wrong tfvars. Here's what actually works for teams under 25 engineers, and the specific disasters that taught me why.
Mobile apps ruled the last decade, but AI is quietly dismantling the foundations they were built on. From declining iOS and Android job postings to LLM interfaces replacing entire app flows, this piece argues the golden age of mobile development is over and what comes next might not even run on iOS or Android.