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Cake day: June 14th, 2023

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  • That’s a good example. If I’m regularly running a command that is a single whitespace character away from disaster, that’s a problem.

    Imagine a fighter aircraft that had an eject button on the side of the flight stick. The pilot complains “I’m afraid I might accidentally hit the eject button when I don’t need to”, but everyone responds “why would you push the eject button if you don’t want to eject?”, or “so your concern is that the eject button will cause you to eject…?” – That’s how I feel right now.


  • Just checked my command history and I’ve run 60,000 commands on this computer without problem (and I have other computers). I guess people have different ideas of what “comfortable” means, but I think I consider myself comfortable with the command line.

    I have shot myself in the foot with rm -rf in the past though, and screwed up my computer so bad the easiest solution was to reinstall the OS from scratch. My important files are backed up, including most of my dotfiles, but being a bit too quick to type and run a rm -rf command has caused me needless hours of work in the past.

    I realized the main reason I have to use rm -rf is to remove git repos and so I thought I’d ask if anyone has a tip to avoid it. And I’ve found some good suggestions among the least upvoted comments.


  • That’s a good suggestion for some, but I’m quite comfortable with the command line.

    It’s not that I’m irrationally scared of rm -rf. I know what that command will do. If I slow down an pay attention it’s not as though I’m worried “I hope this doesn’t break my system”.

    What I really mean is I see myself becoming quite comfortable typing rm -rf and running it with little thought, I use it often to delete git repos, and my frequent use and level of comfort with this command doesn’t match the level of danger it brings.

    Just moving them to /tmp is a nice suggestion that can work on anywhere without special programs or scripts.









  • As a programmer I can confirm that LLMs definitely have loops. Look at the code, look at the algorithms, you will see the loops. The “core loop” in the LLM algorithm is “read the context, produce the next work, read the context, produce the next word”.

    The core loop in animals is “receive stimulus using senses, move muscles, receive stimulus using senses, move muscles”. That’s all humans do, that’s all animals do.

    I think there’s a possibility that humans are simply very advance machines. Look at the debate over whether humans have free will, it’s an interesting question and the important take away is that we still have a lot to learn about our brains and physics. I don’t want to get into that though.

    You’ve ignored my main complaint. I said that you treat LLMs and humans at different levels of abstraction:

    It’s not fair to say that LLMs simply predict the next word and humans have feelings and reason.

    It would be fair though, to say that LLMs simply predict the next word and humans simply bounce electric-chemical signals between neurons and move muscles.

    I don’t think that way about people or LLMs though. I think people have feeling and reason, and I think LLMs reason too. LLMs aren’t the same as people and aren’t as good though. But LLMs are good enough to say that they can “reason” in my experience[0].

    [0]: I formed this opinion when learning linear algebra from GPT4. It was quite a good teacher. The textbook I’m using made a mistake that GPT4 caught. I encountered a proof that GPT4 wasn’t aware of, and GPT4 wouldn’t agree with me that C(A) = C(AA^T) until I explained the proof, and then GPT4 could finally reason for itself and see for itself that C(A) = C(AA^T). As an experiment, I started a new GPT4 session and repeated the experiment using a faulty proof, but I wasn’t able to convince GPT4 with a faulty proof, it was able to reason through the math concepts well enough to recognize when a mathematical proof was faulty and could not be convinced by a faulty proof. I tried this experiment 4 or 5 times. To be clear, what happened here is that GPT4 was able to learn a near math concept in one shot (within a single context window), but only if accompanied by a proper mathematical proof, and was smart enough to recognize faulty proofs as being faulty. To me, that rises to the level of “reason”.


  • You apply a reductionist view to LLMs that you do not apply to humans.

    LLMs receive words and produce the next word. Humans receive stimulus from their senses and produce muscle movements.

    LLMs are in their infancy, but I’m not convinced their “core loop”, so to speak, is any more basic than our own.

    In the world of text: text in -> word out

    In the physical word: sense stimulation in -> muscle movement out

    There’s nothing more to it than that, right?

    Well, actually there is more to it than that, we have to look at these things on a higher level. If we believe that humans are more than sense stimulation and muscle movements, then we should also be willing to believe that LLMs are more than just a loop producing one word at a time. We need to assess both at the same level of abstraction.