I somehow stayed at university long enough to turn undergraduate entropy into a maths PhD, collecting priors, posteriors, and enough caffeine to reject the null hypothesis along the way.
karl.status("now")
curiosity O(β) current_mode machine learning, human playing working_style git stash research_mood n=1 life_cycle coffee -> code -> cope coffee_level p < 0.05 default_answer "it depends" bug_strategy pray
| Project | What it explores | Link |
|---|---|---|
coming_soon |
experiments currently being cleaned, renamed, and made less embarrassing | β |
under_review |
small research projects waiting for future Karl to document them properly | β |
work_in_progress |
plots, priors, bugs, and other signs of academic life | β |
Small debug ritual
1. Make the smallest reproducible example. 2. Check the assumptions before checking the code. 3. Plot the weird part. 4. Delete the clever thing if the plain thing works. 5. Leave a note for future Karl.
Built as a living README: part lab notebook, part dashboard, part reminder to stay curious.