6 entries
Lab Notes
Each entry is dated the day it happened and reports a real case or a real run: my own reads, my own numbers. Short by design, so the record keeps pace with the work.
Your own labels drift as you learn
My facts per read nearly doubled over a reading campaign. A sparse answer key scores true findings as inventions. Measure your drift before you grade anyone.
Read a paper for the experiments it lets you skip
One paper read twice struck a planned experiment and handed me a step. How to read a paper for the experiments it lets you skip.
Know the resolution of the instrument you trust
A vision model attends in tiles of about three millimetres on a panoramic radiograph. Most of what a radiologist calls is smaller. The one-minute arithmetic.
Three training runs, one lesson
Three fine-tunings on my own radiology reads learned my report and not the image, and no training dial showed it. The three checks that catch it.
Who owns the pixels
598 patient radiographs changed licence four times with nobody careless. Re-derive provenance from the files, not the licence field.
Write the missing page
The first Unsloth fine-tune of a vision mixture-of-experts model: the thirty-second gradient check and the two silent defaults that would have ruined the run.
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Imaging AI, evaluation, and building alone, from a radiologist who reads every day and tests before he trusts. A few essays a month, nothing else.