Software engineer with mathematics background specializing in systems programming, security research, and AI/ML applications. I build production tools across the full stack—from WebGPU-accelerated browser applications to Rust CLI tools to bare-metal NixOS infrastructure.

What I ship:

• Observatory - WebGPU deepfake detection running 4 ML models in-browser (live demo)
• specHO - LLM watermark detection via phonetic/semantic analysis (The Echo Rule)
• filearchy - COSMIC Files fork with sub-10ms trigram search (Rust)
• nautilus-plus - Enhanced GNOME Files with sub-millisecond search (AUR)
• indepacer - PACER CLI for federal court research (PyPI: pacersdk)

Self-hosting 32GB bare-metal infrastructure (NixOS) with post-quantum cryptography (ML-KEM, Rosenpass VPN), authoritative DNS, and containerized services.

Philosophy: AI as augmentation, not replacement. Privacy by default. Ship tools that make people better at their jobs.

Live Demo: look.definitelynot.ai

Browser-based AI image detection running 4 specialized ML models (ViT, Swin Transformer) through WebGPU. Zero server-side processing—all inference happens client-side with 672MB of ONNX models.

Stack: JavaScript (ES6), Transformers.js, ONNX, WebGPU/WASM Design: 2006 "Purist" UI aesthetic - no frameworks, pure web standards

3,372+ PNG icons with semantic CLI discovery. Find the right icon by meaning, not filename.

icon suggest security       # → lock, shield, key, firewall...
icon suggest data           # → chart, database, folder...
icon use lock shield        # Export to ./icons/

Features: Fuzzy search, theme variants, batch export, markdown integration Stack: Python, FuzzyWuzzy, PIL

COSMIC Files fork with embedded trigram search engine. Memory-mapped indices achieve sub-millisecond searches across 2.15M+ files with ~0 bytes resident memory.

filearchy/
├── triglyph/      # Zero-RSS trigram library (mmap, ~0 bytes resident)
└── triglyphd/     # D-Bus daemon for system-wide search

Performance: 2.15M files indexed, sub-10ms query time, 156MB index on disk Stack: Rust, libcosmic, memmap2, zbus

LLMs echo their training data. That echo is detectable through pattern recognition:

Implemented in specHO with 98.6% preprocessor test pass rate. Live demo at definitelynot.ai.

Core: Rust · Python · TypeScript · C · Nix · Shell

Primary Server: Intel Xeon E3-1270v5 | 32GB RAM | 3.6TB SSD (btrfs) | NixOS 24.05

Infrastructure as Code:

AI should expand human capability, not replace workers. I call it additive innovation: build tools that make people better at their jobs, not tools that eliminate their jobs.

The best way to predict AI's impact is to build the tools that shape it.

_{SF Bay Area · Open to remote · Icons from iconics}