My first blogpost is out: ikot.blog/anthropic-take-hom… Like many of you, my feed has been dominated recently by Anthropic's Optimization team take-home. TL;DR: They retired this "notoriously difficult" exam because Claude Opus 4.5 effectively solved it. So, they released the task, and everyone started the grind. Yes, AI beat human candidates here, but AI-generated solutions aren't easy to follow if you aren't familiar with the domain. And they carry 0 educational value. I didn't just want to see the solution; I wanted to understand the mechanics under the hood. So I spent the weekend digging into the task. I started with a naive Python baseline that took 147k cycles. After three rounds of specific optimizations, I got it down to ~2,200 cycles. That’s a 65x speedup—and likely would have passed the hiring bar half a year ago. I’ve just published a full breakdown of the solution. I explain everything in detail and depth, but I kept it accessible. Hope you'll love the visuals! Enjoy!