I'm an engineer and technologist with nearly two decades of experience across top companies in the Asia Pacific and US. I've contributed to leading repositories from Airbyte, Apache Foundation, Cohere, Databricks, Facebook, Google, Hasura, Microsoft, and many others!
- 🇺🇳 I'm currently a totally remote work from anywhere nomad but most of my time is in SF. (Previously 🇦🇺🇪🇺🇸🇬🇹🇷🇵🇹🇸🇬🇺🇸🇬🇧)
- 🎓 Teaching applied data science, no-code AI, and generative AI at top universities such as MIT, UT Austin and UNSW.
- 🧠 Run and organise events and meetups like Deeplearning.AI in Sydney and Data & Analytics Wednesday.
- ✍️ Writing regularly on Medium.
- ✨ Passionate about cooking, hiking, cycling and travel.
- 🌱 Currently diving into self-optimizing agents, design patterns for complex systems, AI in precision health and behavioral psychology.
- 🔭 Giving back by sponsoring other developers, and you can also sponsor me.
- 💬 Ask me about artificial intelligence, SaaS, startups, leadership.
- ☕ Feel free to buy me a coffee to show support.
- 🍜 Ramen and noodles are my kryptonite.
Some of my projects I have published on GitHub:
- 🐦 finagotchi (2026) - Tamagotchi‑inspired AI agent that evolves with financial data, using SLMs and local graph.
- 🧠 openamnesia (2026) - Backfill
.mdmemory files for agents locally from iMessage to Cursor logs locally with no apis. - 👻 dexscraper (2025) - Real-time DexScreener scraper for multi-chain token discovery, filtering, and feed for bots.
- 🧑🚀 airgapped-offline-rag (2025) - 100% offline dockerized RAG with quantized model inference.
- ✨ Synthetic user research (2024) - Autonomous agents powering reasearch studies and panels with persona prompting.
- 🔑 autosecure (2017) - Public threat-feed and blocklists to automatic IP blocking for Linux and macOS firewalls.
I'm always open to building, contributing, collaborating, and chatting. Feel free to 📫 reach out.
- The Dawn of Self-Optimizing AI Agents
- From Observability to Optimization: Announcing the Opik Agent Optimizer Public Beta
- Tiny QA Benchmark++: Ultra-Lightweight, Synthetic Multilingual Dataset Generation & Smoke-Tests for Continuous LLM Evaluation
- Mind the Metrics: Patterns for Telemetry-Aware In-IDE AI Application Development using the Model Context Protocol (MCP)
- LLM Evaluation Complexities for Non-Latin Languages
- Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges
- The GenAI Compass: a UX framework to design generative AI experiences
- Creating Synthetic User Research: Using Persona Prompting and Autonomous Agents
- Explaining OpenAI Sora’s Spacetime Patches: The Key Ingredient
- Generative AI Design Patterns: A Comprehensive Guide