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to be quite frank, i’m very proud of my technical progression as a software engineer. when i was admitted into ece at the university of waterloo, i knew how to code in the sense that i could make things work, but i had very little of the judgment or depth that i now associate with actually being an engineer. i wasn’t thinking seriously about architecture, performance, failure modes, maintainability, or what was happening beneath the abstractions i was using. i was mostly just excited that the code ran lmfao (why did i think commenting was the shit back then). ...

these days, the more i read about modern ai inference, the more i start to draw these web of connections to HFT (high-frequency trading). replace market data with tokens, NICs with GPUs, and order execution with inference, and suddenly… you’re reading the same playbook. not to mention, the underlying skill set is a near exact match. in HFT, engineers obsess over shaving microseconds off the critical path. we bypass the kernel to eliminate unnecessary context switches, build lock-free structures so threads never wait on each other (on a side note: the mutex strategy generally wins over lock-free lol), organize memory to maximize cache locality, vectorize hot-paths with SIMD instructions, and profile everything like maniacs because every cache miss has a measurable cost. ...