Fatfort

fat-fort is an early-stage effort in adversarial machine learning and computer-vision robustness — auditing where models break, and how to certify that they don’t. Built in the open, honest about the stage: no invented clients, no borrowed logos, no certifications that don’t exist yet.

The mark is a 2×2 grid whose corners read TT · TF · FT · FF. fat-fort is the FF corner (in red); its sibling tutorsfirst is the TF corner — two cells of the same matrix.

What it explores#

Adversarial examples

tiny, deliberate perturbations that flip a model’s prediction while looking unchanged to a human.

Certified robustness

provable guarantees over a bounded neighbourhood of an input — not merely “we couldn’t break it”.

Honest evaluation

robustness claims are easy to overstate; the real work is measuring them without fooling yourself.

Pointers#

• fatfort.com — the project’s home.
• tutorsfirst — sister project (tutorsfirst.com.au), an Australian tutoring marketplace where every tutor is credential-verified.
• abaj.ai — main site, writing, and past work.

Reach the fort at hello@fatfort.com.