Devashish Tyagi

I started out fixing small bugs in a college ERP nobody else wanted to touch — the kind of codebase where every fix taught you something about the ten decisions that came before it. Eighteen months later, that instinct hasn't changed: I still learn fastest by shipping something real and living with the consequences.

My work sits at the intersection of execution and ownership. I've carried features from a whiteboard sketch to production traffic, reviewed code for developers who joined after me, and made the unglamorous calls — which library to trust, when to refactor, when to ship — that don't show up in a demo but decide whether a product survives its first year.

Lately, that curiosity has pulled me toward AI systems: retrieval, embeddings, the plumbing that makes a language model useful instead of just clever. I'm not interested in AI as a buzzword — I'm interested in it as an engineering discipline with its own failure modes, and I want to get good at that discipline the same way I got good at backend systems: by building, breaking, and rebuilding.

The long game is simple to state and hard to execute: become an engineer people trust with ambiguity, lead teams that ship with the same care I try to bring alone, and eventually build products that outlast the meetings about them.