I work in AI engineering full-time, but most of what I do there stays behind closed doors. This blog is a separate, personal lab notebook: a place to pick up a new model or technique, actually run it, and write down what happened — including the parts that didn’t work.
The goal isn’t polished tutorials. It’s closer to a running log:
- what I tried
- what broke, and why
- what I’d do differently next time
How this is built
Static site with Hugo and the PaperMod theme, deployed to GitHub Pages via GitHub Actions on every push to main. Each post is a self-contained folder under content/posts/, so code output and images from an experiment can be dropped in alongside the write-up.
A code block, to confirm syntax highlighting works:
from transformers import pipeline
generator = pipeline("text-generation", model="gpt2")
print(generator("The best way to learn a new model is", max_new_tokens=20))
And inline math, to confirm KaTeX renders: the attention weights in a transformer are computed as
$$ \text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^\top}{\sqrt{d_k}}\right)V $$
If both of those rendered correctly, the scaffolding works. Real experiment write-ups start next.