Building an AI-Assisted Kaggle Toolkit
I got tired of the standard Kaggle workflow. Open a notebook, import pandas, start typing
.describe(), throw some models at the wall. It works, but you don’t learn much, and three months later you can’t remember why you chose XGBoost over Random Forest.So I built a toolkit that forces a better process: research first, then model. Document everything. Produce educational content as a natural byproduct of doing the work.
Building a 24/7 Go Bot with KataGo on KGS
I wanted a Go bot that plays games on KGS while I sleep. Not to grief anyone — just to have a persistent presence on the server, accumulate games, and watch a machine play the oldest board game in the world against real humans, all day, every day.
The result: a Python wrapper around KataGo and kgsGtp.jar, running on a $5/month Lightsail instance, playing Chinese rules on 19x19, saving every game as an SGF file, and reporting stats back to me. Here’s how it works.
What I Actually Think About AI
I use AI tools every day. Kiro writes code for me. I use it to organize my email, build projects, and scaffold blog posts. But I draw a line at having it think for me.
AI is a power tool. It’s a table saw, not a carpenter. The interesting decisions, the creative choices, the opinions in these blog posts, those are mine. The formatting, the boilerplate, the grunt work of turning ideas into deployed software, that’s where AI earns its keep.