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LLMs and almost good code

When LLMs generate code, I believe they have one insidious failure mode: they generate code that is almost good. Say, maybe, 10 % too complex in easy cases. This code is easy to accept, but I'm not sure what the long-term consequences of doing so is.

Full article (3–10 minute read): LLMs and almost good code

Flashcard of the week

Something I wrote down a while ago to practice my intuition for the growth of variation in sums of random variables.

Is it more or less risky to do 10 fair bets, than 100 at 1/10th the stake?

The key here is that with n sequential bets, the risk is sqrt(n) the risk of a single bet. Increasing n by 10× means increasing the risk by sqrt(10) times, or around 3. If we simultaneously decrease the risk of each bet to 1/10th, guess which wins out?

More. To match the risk of the 10 bets at 1/10th the stake we would need to do 1000 of the smaller bet.

This is easy to verify with the computer, too. Generate 10 random draws of -1 or +1 and sum them together. Do this 5000 times. See what the variation of the result is. Then do 100 random draws of -0.1 or +0.1. You'll find the result more tightly clustered around zero.

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