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Joined 3 years ago
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Cake day: July 1st, 2023

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  • I love C++ as a god defying monument of backward compatibility. You can go through the versions like an archeologist looking through layers of rock to see what the trends of that time were. There’s not only one way to do the thing you want, but several, too many ways, and no one can stop you from frankensteining pieces together that were never meant to be.

    If C is meant to be a basic toolbox, C++ is your dad’s disorganized tool cabinet he keeps in the garage. Overflowing with barely used, highly specific, and redundant tools.





  • From what I understand, pain is generally defined as our neurons wasting energy.

    If we assume the Free Energy Principle is fundamental, then updating information produces free energy, which is more intuitive to think of as wasted energy. Overfitted/overcomplicated beliefs take more energy more often to fix to be more accurate. So free energy is defined at complexity of belief minus accuracy of belief.

    The brain over evolution has defined a very ingrained belief that “my pain sensors shouldn’t activate” so when your pain sensors activate it violates this belief.

    Emotions are actually condensed representations of your bodily state and some of your mental state, and whether they are good or bad depends on if you can predict your emotions. For instance you feel fear because you are trying to predict your pain sensors will go off to reduce its pain, but you keep predicting it’ll happen when it hasn’t yet. It’s also why anger during a boxing fight could feel good and anger when you stub your toe does not, one was predictable the other a surprise.

    Assuming all the links in the chain of theories are true, then current LLMs cannot feel pain or emotion. They care about prediction error and sometimes complexity during training, but during inference it doesn’t get any signal or consequence for delivering the wrong token. It has no internal state except for the context window, which is never surprising for the LLM. The attention layers literally guides the model for what is most expected from the text it’s handed.

    Even if the LLM received a negative training signal during inference, acting wounded after pain is the expected response. A random sequence of gibberish would be more “painful” than any torture fantasy.

    On top of all this, the Free Energy Principle works because on a cellular neuron level we are built to reduce wasted energy, free energy being one source of wasted energy. LLMs are built on matrix multiplication running through GPUs and neither are even aware of the energy wasted, nor the free energy produced from changing values.

    I think a feeling machine is possible, but the foundation of neural networks right now don’t have the necessary components to drive any subjective sensation or emotion.






  • I face some issues with updates on linux breaking things (Arch btw) but it’s usually an hour debugging and then a “hey, someone already made a fix for this as a pull request that you could install now if you compile from git”.

    That’s a hassle, but it’s better than spending an hour debugging, realizing the only real solution is to wait for a hotfix from the powers that be, then several more hours trying to get a workaround, then the five stages of grief ending in admitting defeat and waiting for the hotfix that’ll break something else.

    On Linux, there’s always a way to do what you want to do, with all the risk and reward that comes with that.






  • Game designers, and I’m including tabletop rpgs, love having status effects that freeze, paralyzed, or do a percentage of health of damage; but they always neglect the fact that they’re ineffective against hordes of weak enemies and are overpowered against strong solo enemies.

    Really wish they would have more foresight than to just make it have zero effect on bosses. I’d be satisfied with it applying a weakened effect instead of being outright immune.