Blog
Notes on explainable AI, AI safety, Chain-of-Thought reasoning, and building trustworthy AI products.
Posts
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2026
A Robot Ran 100 Metres in 8.64 Seconds in Beijing: So What Does That Number Mean?
The Games by the numbers, the specifications of the record-setting Tiangong Ultra, and what happened after the finish line: several runners, the champion included, hit a padded barrier. Why stopping is harder than running, with the two limits worked through, why the arithmetic inverts off the track, numbers from my own slip measurements, and the deceleration test I would like the Games to add.
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2026
Can My Robot Tell What Ground It Is On? I Measured It, and the Answer Came Eight Seconds Late
Three tests on the precondition for a terrain classifier: what it does on ground it has never seen, whether the information is in the data at all, and when the answer arrives. The experiment sheet, the tables, why derivative features cannot tell two steady states apart, and a warning that every number here was measured on a rig I later found to be faulty.
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2026
I Reviewed for Five Conferences This Year. The Same Paper Keeps Arriving.
Most submissions I score low are not wrong, they are unfalsifiable by the reader. I know that failure from the inside: my own slip study reported 0.869 accuracy and a 72 percent error reduction, and the effect was manufactured by a robot resting on its front edge. Why correct ablations are blind to a fault in the substrate every arm shares, and the five questions I ask instead.
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2026
My Robot Was Tipping 13 Degrees. That Was the Whole Result.
A wheel-slip study invalidated by its own apparatus. The robot had been resting on its front edge at 13.3 degrees, confirmed by ground truth, IMU orientation and accelerometer alike and predicted by the geometry, and the chassis dragging was what produced the slip being measured. Why four months of correct ablations could not catch it, and how slip was re-established on a controlled ramp with a high-friction control arm.
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2026
My Slip Detector Was Accurate. It Was Also Useless.
Corrected August 19, 2026: the slip in this post came from the robot dragging its chassis at a 13.3 degree tilt, not from the friction. Level the robot and the same world produces no slip at all. Originally: a slip detector scored 0.869 accuracy and cut position error by 72 percent. The control arm, driving slower with no detector at all, did better. Then a sharper measurement explained why: a robot stuck with its wheels spinning and a robot driving normally look the same to wheel encoders and an IMU, at 0.552 balanced accuracy against 0.500 for a coin.
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2026
A short note on why I started this blog and the topics I will cover: trustworthy AI, explainable reasoning, and lessons from building real AI products.
Published elsewhere
Selected writing on external platforms.
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2026
The Numbers in Your Tutorial Are Probably Wrong. Mine Were.
Educational material quotes numbers, then the code changes and the prose does not. I gave myself a rule: a number does not appear in a lesson unless a test recomputes it. What that rule cost, and the contrastive learning bug it uncovered after I had spent four paragraphs confidently explaining the behaviour of my own mistake.
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2026
Why Most Machine Learning Methods Stay Unused, and What I Did About It
Some of the most robust methods in machine learning exist only in papers. They are cited thousands of times and almost never used, not because they are irrelevant but because they are inaccessible. On closing that gap with tooling.
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2022
Java fundamentals, a four-part series
An introduction for beginners: Java Overview, Popular Java Editors, First Java Program and Java Keywords and Comments.