Hi, I'm Vince Lam 👋
I'm a customer data scientist / forward deployed engineer (FDE) at H2O.ai based in Singapore. I moved here from London looking for new challenges, and I write about what I learn along the way.
This blog covers AI in practice, productivity tools I actually use, and the occasional reflection on career and life. My current focus is agentic AI.
You can see what I'm working on now here .
If you'd like to work on a project together, just drop me an email .
Featured
Launching LLMRepos.com - A Live Dashboard for Open-Source LLM Projects
Published:A live dashboard tracking hundreds of open-source LLM repositories - updated daily with stars, forks, and growth trends so you can spot what the community is actually building with right now.
Building Simple HTML Tools with AI: From Idea to Deploy in Minutes
Published:Exploring how single-file HTML tools and AI coding assistants have transformed my workflow - from idea to deployed tool in minutes. No frameworks, no build steps, just pure simplicity and speed.
50+ Open-Source Options for Running LLMs Locally
Updated:There are many open-source tools for hosting open weights LLMs locally for inference, from the command line (CLI) tools to full GUI desktop applications. Here, I’ll outline some popular options and provide my own recommendations. I have split this post into the following sections
Recent Posts
🏋️ My Fitness Journey: Strength, Climbing, Running, and HYROX
Updated:A timeline of my fitness journey - key milestones, personal bests, and small experiments across different sports over the years.
How I Used AI to Build a 20-Week Training Plan
Published:How I used AI to design, iterate, and maintain a periodised 20-week training plan for HYROX and a half marathon - from writing the metaprompt to updating it with real data.
🏃♂️ Run Clubs in Singapore
Updated:A compilation of Singapore's exploding run club scene. I made this resource both for myself and to get friends to join me running.
📚 Book Notes: Machine Learning Design Patterns
Published:My notes on essential machine learning design patterns from Google Cloud experts, featuring practical insights on ML system architecture and implementation strategies for engineers.