19+ years building software that ships — from AI/ML systems and cloud infrastructure to the engineering teams that run them. Currently deep in LLM tooling, AWS Bedrock, and MCP server design.
Where I've worked, what I studied, and the certifications that stuck
💬 Available for conference talks, technical panels, and podcasts — get in touch
What I reach for when something needs to actually work
Things I built, some of which other people found useful
A fork of Suno's Bark that adds voice cloning. Feed it a few seconds of audio and it generates new speech in that voice. Built this before the big labs made it easy.
Python library for the Razer Hydra motion controller. I needed it for a VR project, the official SDK was painful, so I wrote a cleaner wrapper.
Converts Office and image formats to PDF. Wrote it to batch-process files without depending on LibreOffice or paid converters.
Fully private, entirely in-browser LLM chatbot. Runs open-source models (Llama 3, Mistral, Phi) directly in your browser using WebGPU — no server, no data ever leaves your machine.
A self-hosted library of 160+ curated shell commands for AWS, Azure, GCP, Kubernetes, Docker, and Terraform. Served from S3 and consumed by a minimal single-file Go CLI — your distributed cloud operations cheat sheet.
42+ repos — cloud tooling, AI experiments, VR projects, and things I built to solve specific problems. 4.3k+ stars, mostly from bark-with-voice-clone.
Long-form thinking on technology, economics, and structural change
The retrieval-augmented generation patterns that survived contact with production traffic on AWS Bedrock — chunking strategies, embedding choices, reranking, and the failure modes nobody writes about.
How I gave Claude Code real-time access to CloudWatch metrics, logs, and alarms — in both TypeScript and Python — and what I learned about the design decisions that make observability tools actually useful for AI agents.
How to build a custom MCP server that connects Claude Code to any REST API — the protocol explained, a working TypeScript and Python implementation, and the patterns that make it production-ready.
Both frameworks try to make AI useful across long projects. BMAD structures the process with 12+ specialized agents and 34+ workflows. MASTERPLAN persists the state with two markdown files and zero dependencies. Here's when each wins — and how to use both.
Claude Code's CLAUDE.md tells the AI how to work. But nothing tells it what's already built, what's in progress, or what comes next — until now. MASTERPLAN.md is the persistent project brain that survives context compression, structured like an Agile sprint board for AI work.
An SRE's years of hard-won expertise — keyless OIDC deployment, IAM least-privilege, CDK stack design — can now be distilled into a 15-minute Claude Code prompt. What happens to specialized engineering value when superpowers become consumable AI skills, and who ends up writing the skills vs. running them?
A sector-by-sector breakdown of who wins and who gets erased as AI agents eliminate "human friction." Compute owners, stablecoin rails, and data center landlords thrive. SaaS seat-counters, card networks, and middle management face structural collapse — and the market hasn't priced any of it yet.
Custom skills in Claude Code aren't prompts — they're codified institutional knowledge that compounds. Skill chaining turns individual expertise into team infrastructure, and the gap between teams that do this and teams that don't is widening fast.
Open-weight AI running on consumer phones doesn't just democratize intelligence—it democratizes spam. With 4.6 billion mobile internet users and zero marginal cost per message, the signal-to-noise ratio of the internet is approaching collapse. Here's what that looks like, and how to protect yourself.
When Anthropic released a 200-line legal contract plugin, $285 billion in SaaS market cap evaporated in 48 hours. The crash wasn't about the plugin—it revealed that the per-seat pricing model was already broken.
I ran a 10-year economic simulation: AI compresses white-collar wages, capital concentrates into compute, and the curve looks worse than the Great Depression in three of four scenarios.
How agentic LLM workflows crossed a coherence threshold in late 2025—reshaping how engineers build software, where the real leverage comes from, and why human verification is now the core bottleneck.