import mmap, codecs

    from collections import Counter

    def word_count(filepath):

        freq = Counter()
    
        decode = codecs.getincrementaldecoder('utf-8')().decode
    
        with open(filepath, 'rb') as f, mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
        
                for chunk in iter(lambda: mm.read(65536), b''):
            
                        freq.update(decode(chunk).split())
            
                    freq.update(decode(b'', final=True).split())
        
                return freq

... Ah, but I suppose the existing code hasn't avoided that anyway. (It's also creating regex match objects, but those get disposed each time through the loop.) I don't know that there's really a way around that. Given the file is barely a KB, I rather doubt that the illustrated techniques are going to move the needle.

In fact, it looks as though the entire data structure (whether a dict, Counter etc.) should a relatively small part of the total reported memory usage. The rest seems to be internal Python stuff.

Really the one thing that conclusively has changed is that the 'ask it on stackoverflow' has become 'ask it an LLM'. Around 95% of the stackoverflow questions can be answered by an LLM with access to the documentation, not sure what will happen to the other 5%. I don't think stackoverflow will survive a 20-fold reduction in size, if only because their stance on not allowing repeat questions means that exponential growth was the main thing preventing them from becoming stale.

Right.

I don't think you even need cynicism or whatever you felt you were having impolite thoughts about:

I'd expect the top mature libraries to be the most resistant to AI tool use for various reasons. They already have established processes, they don't accept drive-by PR spam, the developers working on them might be the least likely to be early adopters, and -- perhaps most importantly -- the todo list of those projects might need the most human comms, like directional planning rather than the sort of yolo feature impl you can do in a one-man greenfield.

All to further bury signals you might find elsewhere in broader ecosystems.

I'm not sure that is a useful model, or more complete. I don't think you can assign interceptors to undetected missiles, so considering their effect on the value is rather pointless. It's effectively a sunk cost.

Multiplying with the probability also makes no sense from an optimisation point of view. Why would you assign lower value to a target about to be hit simply because you were unlikely to detect the missile?

The tracking probability only shows up in the meta game described at the end, where one side is trying to optimise their ability to hit valuable targets and the other is trying to optimise their ability to prevent that from happening.

The strongest claim is the lack of significant effects for anxiety, ptsd etc. but it varies a bit whether that's because the effect is too small or because it is not studied enough.

For anxiety for instance the effect they list in the paper is quite big but the error bars are even bigger so the net result is inconclusive. This is quite different from PTSD where they note little to no effect with small error bars.

Is this paper trying to confuse people about psilocybin?

Some people are going to be more susceptible to depression, for whatever reasons, and improving someone's surroundings is probably going to prevent or alleviate depression to an extent, but to people who are depressed now it's somewhat pointless advice.

Anything else just proves someone prefers making money to improving the models.