Newsletter · Engineering Frontier
How to tell when a model is lying to you
One essay at a time, about eight minutes to read, on supply chain and distribution and the places where AI and data analysis quietly stop being trustworthy. Sent when a piece is finished. Free.
Why there is no subscriber count here
Every page like this one quotes a number. The honest reason this one does not is that the number would be small, and a site whose argument is that claims should be measured is a poor place to start dressing one up.
So here is the evidence instead, which is better than a number anyway: the archive is already public and free. Nothing is held back for subscribers. Read one before you decide whether you want the next one.
What has already gone out
- Averaging threw the answer away before the model ever saw it The number my model predicts is an average, and averaging deletes what tells two shapes apart. On corrected data the recovery is small: +0.02 at worst.
- There was a random number hiding inside my measurement I reported that a descriptor loses its value when computed cheaply. The collapse was a random draw my own solver made, and the check meant to catch it passed.
- The bouquets got better and sales fell Shelf life went from half a week to 14–21 days and sales fell anyway. Customers could see the size of a bouquet. They could not see the freshness.
Who is writing it
I work on supply chain and distribution, and I spent seven months in flower supply, buying direct from the wholesaler and building to a costed spec, which is where most of the operating detail in this writing comes from. Alongside it I publish computational research with DOIs, indexed on Google Scholar, and I am a mechanical engineering undergraduate at Tel Aviv University. The pieces that cite my own work link the data and the code.