AI engineering

Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

The article traces the journey from a single nn.Linear to a fused MLP, showing that torch.compile fuses GeLU and multiplication into one Triton kernel while leaving the three cuBLAS GEMMs untouched. It then introduces hand-tuned Liger kernels that achieve the same fusion without compile latency or shape specialization, trading a few microseconds of peak performance for robustness across changing input sizes.

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The Substitution Wave in AI

The Substitution Wave in AI explains how rising frontier model prices and the rise of good-enough open-source models are forcing AI buyers to substitute cheaper alternatives, with concrete examples from Coinbase, Lindy, Harvey, and Cursor showing cost savings of 10x or more without sacrificing performance.

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Expanding Project Glasswing

Project Glasswing has expanded from 50 to 200 partners using Claude Mythos Preview to scan codebases, finding over 10,000 critical vulnerabilities. With Mythos-class models expected from many AI companies within 6-12 months, the bottleneck is shifting from finding flaws to patching them at massive scale.

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