Build a compiler to solve Anthropic's interview challenge. @AnthropicAI published their take-home interview challenge on Feb/2026: optimize a kernel running on a simulated VLIW SIMD virtual machine to minimize cycle count for a tree traversal + hash computation workload. It has now been about 5 months since the challenge was released, so I am sharing my work as well. I hope this does not interfere with Anthropic’s interview process. The original intent of the challenge was for candidates to collaborate with an LLM and manually optimize the assembly program. I took a somewhat different approach: I built an optimizing compiler for the VLIW machine. The compiler can optimize not only the tree-traversal and hash program used in the interview challenge, but also the performance of arbitrary programs targeting this machine. My solution does not use any algorithm-level optimizations, nor does it contain any optimizations specifically tailored to this program or benchmark. Every optimization is a general-purpose compiler optimization. Result: CYCLES: 1137 Speedup over baseline: 129.93315743183817 Check it out if you are interested in techniques: Loop unrolling; Common subexpression elimination; Dead code elimination; Peephole optimizations; Scalar replacement of aggregate; Arry scalarization; Alias analysis; Memory dependency based load/store elimination; Straight line strength reduction; Data dependency analysis; SLP vectorization; Load-store vectorization; VLIW instruction scheduling; Register allocation; etc.