Field notes from building and scaling fintech and AI systems — cloud cost, security, infrastructure and the decisions that actually move the needle. Written to be useful to founders and the engineers who work for them.
The dangerous AI failures aren't crashes — they're confident, plausible, wrong outputs that look like success. Optimistic confirmation, model-as-calculator, and trusting output as a system of record — and how to engineer against each.
Read the article →Lift-and-shift just moves the storage bill. The real win is re-architecting the network-mapped file system to S3 + Glacier lifecycle tiering — the POSIX-to-object gap, the migration playbook, and 70–90% off the archival tier.
Read the article →Why regulated fintechs should treat their cloud as version-controlled infrastructure: a Terraform, secure-by-default landing zone you can reproduce in a day — and what that actually buys you when disaster, expansion, or diligence comes knocking.
Read the article →The most expensive server was running at 3% CPU. A FinOps teardown of a lending platform on Azure — and the five-lever pattern behind most oversized cloud bills, with a clear path to ~40% savings and no features removed.
Read the teardown →