I build LLM agents and developer tools, and the cloud-native systems that run them. My focus is making AI dependable in daily use: agents that own their tasks, prove their work with checks, and leave clean commits. My foundation is Go, Python, Kubernetes and AWS.

• SAKO  
  Makes coding agents prove their work. When a Claude Code or Codex session stops, a finish gate checks that every change is recorded, tested and committed, then writes a receipt for each finished task. Pure Python, no dependencies.
• aiterm  
  Turns plain-English requests into Unix commands in your terminal, and lets you run, copy or edit each one before it executes. Go and the OpenAI API.
• object-detect
  Benchmarks real-time object detection on a Raspberry Pi, CPU versus a Coral Edge TPU. Tracks FPS, CPU, memory and temperature for SSD MobileNet, EfficientDet-Lite and YOLOv5.
• Open source: merged a cleanup to AutoGPT's memory module (2023).

_{Earlier work: Bayesian Q-learning, a reproduction of Dearden et al. (1998) in OpenAI Gym · saytext, a text-to-speech service over gRPC in Go}

I'm open to applied AI roles and to collaborating on coding-agent tooling, LLM developer tools, and AI features that need solid infrastructure.

• Use Claude Code or Codex? Try SAKO with uvx sako init and tell me what breaks. Issues and client test reports are welcome.
• Have a project or role in mind? Send a collaboration request.

_{The activity and stats cards are rebuilt daily by a GitHub Action in this repo.}