JL

I write systems software and the tools around it. Most of what I build starts the same way: something on my own machine is slower, heavier, or more fragile than it should be, and the fix turns out to live a layer below where people usually look.

The disk scanner was a syscall problem, not a UI problem. The video trimmer was a stream-copy problem, not an encoding problem. The voice dictation tool was a transport problem: recognition was already local and fast, and the latency was in how the text got into the field. The battery meter was a polling problem, so it reads the power sensors directly instead of waiting on the gauge. Each one got faster by removing work, not by adding threads.

Right now I'm reading into machine learning properly: transformer internals, self-attention, how inference actually spends its time, and where agents break down. I use AI daily and want to understand the machine underneath it rather than treat it as a box that returns answers.

• See the work, measured →
• github.com/blarer
• blare@louds.net