"A Fix for Softmax and Cross-Entropy"
Turns out two of the most fundamental functions in deep learning have numerical bugs that can actually destabilize training.
This paper proposes simple fixes to softmax and cross-entropy that prevent tiny gradients from incorrectly rounding to zero, or remaining nonzero when they should vanish, as these errors can cause sudden loss spikes and interfere with knowledge distillation.
On small-scale grokking benchmarks, the fixes eliminate Slingshot loss spikes and improve test accuracy from 94.3% to 100% in one setting. They also prevent student models from drifting away from their teachers during distillation, without requiring higher numerical precision.
https://t.co/0nXRLrQVbe
I did all of this in the wrong order
Started my own things first, joined someone else's early stage company much later, and spent the first month realising how much I had been confidently guessing about for years
The advice everyone repeats is to just start your own thing, because nobody learns to swim from the side of the pool
I believed that completely. I now think it is right about the destination and wrong about the route
When you only ever run your own company, you learn one company deeply
That sounds like a lot until you notice it is a sample size of one. One market, one set of customers, one way of making decisions, and no way to tell which lessons generalise
You also see your own failures from the inside, where they are hardest to read. Something goes wrong, you can give four plausible explanations for it, and you have no idea which one is correct
No control group, no second run with one variable changed
The things that surprised me inside someone else's company were not strategic at all, they were procedural
How a decision gets made when two senior people disagree and both are being reasonable
What a customer sounds like when they are not being polite, which is nothing like the calls you have when you are the founder and everyone in the room knows it
I watched people decide things in ten minutes that I would have agonised over for a week, not because they were smarter, but because they had seen it before and knew which bits did not matter
Then the cost argument, which is the one that actually got me
Make a mistake in your own company and you pay with your runway and two years of your life
Make the same mistake inside someone else's and you learn it while they absorb the bill
Most people who turn that down are turning it down on status, not economics. Founder just sounds better than employee
Two or three years is plenty though
Past that you are doing year one again with nicer tools
🚨 Indian physicist Ashoke Sen has become the first Indian to win the 2026 Oskar Klein Medal for his pioneering contributions to string theory, black-hole physics and quantum gravity.
Half the AI tools I try are just a prompt in a trench coat
I do not mind it, the prompt is usually better than mine
What bugs me is the pricing page acting like it invented reasoning