AI Performance & Security Engineering

Making Systems Better

From GPU kernels and local inference to model steering and vulnerability research, we build AI systems that perform predictably under pressure.

Latest Work

AI security, model steering, and systems research
The Teacher's Accent: Fingerprinting DeepSeek V4.1 The Stream, Its Writers, and the Assumption in Appendix E Membership vs Mass: Grammar-Constrained Decoding and Forced Abliteration Steering a Loop: Control Vectors Meet the Looped Transformer Inkling on Two DGX Sparks: The Only vLLM Lane Autoresearch: Sticky Refusals and the Invisible Quantisation Cliff Autoresearch: Abliteration Without Redistributing the Model Calif MIE: Five Days of Kernel Exploitation with Kimi K3 The 1.2 ms Eigensolver That Never Ran A Windows Kernel in a Browser Tab: Debugging and Crash Dumps A Windows Kernel in a Browser Tab: Running Microsoft Console Tools A Windows Kernel in a Browser Tab: A Filesystem Over 9P A Windows Kernel in a Browser Tab: Cold Boot, Fast Boot, and Four Megabytes Fable 5's 38-Minute Kernel: The Token Math and the Boot Count Stuxnet's .LNK Zero-Day, Line by Line in the Windows 2000 Source Fable 5 Wrote a Windows Kernel in 38 Minutes Windows 11 Hibernation on ARM64 SMBaloo: An AI Agent and Windows 11 Kernel Internals autoextdetector: A Self-Improving Detection Agent 25 Years of Bugs in the Windows 2000 Source Tree Bleeding Llama: When AI Model Files Become Memory Leaks Legacy Security Is the Real Enterprise AI Bottleneck Local Models Within Reach Odd Lots: Cyberwar in the Age of AI When Machines Pay Machines: The Economics of Agentic AI Porting CUDA FFT to Mojo: Achieving Bit-Exact Precision AMD GPU Support in Triton Gluon RustBPE: High-Performance BPE Tokenizer Training in Rust Optimizing AlphaFold's Triangle Multiplicative Update Multi-GPU Programming with AMD's Iris Framework Gluon: When Triton Isn't Low-Level Enough The Hidden Math Bug That Makes AI Unpredictable Building Agents for Small Language Models