About

I'm Shawn. I work in malware research, with a focus on applying ML and AI to analysis workflows.

That usually means building agents for reverse engineering tasks, fine-tuning models for security workloads, and experimenting with models for classification, byte-level analysis, and other security-specific problems.

Outside work, I spend a lot of time running ML infrastructure in my homelab. I'm interested in inference performance, GPU behavior, agent systems, memory, ML/AI ops, and the engineering needed to make these systems reliable under real workloads.

This blog is where I write about that work: LLM inference and serving, quantization, fine-tuning, agentic systems, GPU performance, and occasional reverse engineering topics. The goal is practical measurement from systems I actually run, not vendor summaries or recycled benchmarks.

Some related code is on GitHub:vLLM serving on the DGX Spark,a kernel guide for consumer GPUs,an inference gateway, and amechanistic-interpretability puzzle solution. Security tooling stays private for obvious reasons.

How I Write

• Measured where possible. Performance numbers come from hardware I run, with versions, configs, and dates included when they matter.
• Corrections stay visible. If I get something wrong, I'll update the post and keep enough context for the correction to be useful.
• Dates matter. ML infrastructure changes quickly. Posts here are snapshots of what was true at the time, not permanent advice.

If you spot an error, send it my way. The best technical writing gets better when people push on the details.