A practice in restraint, engineered for scale.

I've been in infrastructure since 2003 — through sysadmin, security, virtualization, containers, SRE, platform, and now AI. The work kept changing names; the shape of the problem didn't. Whatever I'm good at now, I owe to the next thing always being harder than the last.

These days I run platform at Xendit — seven thousand services across dozens of clusters, the kind of fleet where you measure success in things that didn't happen. Together with my team, we keep iterating and advancing our stack: materializing our own utopia day by day.

I independently released models with UNA and MGS — post-training methods I developed and applied across multiple transformer architectures, reaching #1 on the HuggingFace LLM Leaderboard several times: TheBeagle, Juanako, miniClaus, and Cybertron, which was served for nearly two years in Cloudflare Workers AI. Each iteration sharpened my intuition over deep neural networks, so I kept building AI.

I read more than I write, run more experiments than I publish, and contribute upstream when the fix belongs in the project itself, not a private fork. The systems I'm proudest of are the ones nobody notices, because they just keep running.