Python

The last deliverable of my summer project was a slide deck in the official university template. I generate slides from Python, and my first instinct was to rebuild the branding by hand: sample the navy, find the fonts, redraw the footer. That path produces decks that look almost right, which is worse than wrong. The trick that works is to load the official .pptx itself, delete every sample slide while keeping the slide masters, and add my own slides on the official layouts. Backgrounds, fonts, and logo come along for free. Auditing AI-Solvability · Part 5 of 5 1 2 3 4 5 The generator is one Python file, roughly 470 lines of python-pptx, and it built the final presentation for the auditing project from part 1. This post is about the two things in it worth stealing and the one bug worth laughing at.

The scariest failure mode in an eval harness is not a wrong answer. It is a harness problem wearing a model problem's clothes. A test file that does not run looks exactly like a problem no model can solve. A sandbox flake looks exactly like a failed attempt. Before I trusted any number from my CS1 auditing tool, I had to make those confusions impossible, and it came down to three rules. Auditing AI-Solvability · Part 2 of 5 1 2 3 4 5 Part 1 of this series reported scores from 1,095 sampled solutions. This post is about why I believe those scores. The harness lives in one Python module, and its docstring is a contract I wrote before the code: