“Lazy self-installing Python scripts with uv”[1] article from Trey Hunner has more details with examples

[1] https://treyhunner.com/2024/12/lazy-self-installing-python-s...

  > For example, here’s a script I use to print out 80 zeroes (or a specific number of zeroes) 

Also...

  # Print 80 0's and flush 
  printf %.1s 0{1..80} $'\n'
  # Alternatively
  for i in {1..80}; do echo -n 0; done; echo

For the ffmpeg example is this any different from

  ffmpeg -i in.mp4 -c:v copy -filter:a volumedetect -pass 1 -f null /dev/null &&\
  ffmpeg -i in.mp4 -c:v copy -filter:a "loudnorm" -pass 2 out.mp4

The python seems more complicated tbh

Not in any way relevant to an example of leveraging Python's new inline metadata PEP via uv.

fair enough haha. But I think as others point out, there are some portability issues.

Author here: Wholeheartedly agree with your comment on hallucination. I initially set out to answer the question “Will entropy help identify hallucination?” And soon realised that it doesn’t, for the same reasons you mentioned above. So I pivoted to just writing about the entropy measure in the post. And this is also reflected by how I started with hallucination and then quickly veered away from it. I’ll be more careful in future posts & conversations. Thanks!

Nice post, really, and I think it will help some people to understand more about how LLMs work, especially helping fix the dogma about “LLMs just randomly select the next most likely word” which is kinda true but so many qualifiers and contextual details apply that the statement is more misleading than useful.

On undesired output, I would think it a great service to the field if we could come up with a better and earwormier word for “hallucinations” and somehow make it stick.

Right now we have half the literate world walking around thinking that LLMs are licking frogs, and it does nothing to help people understand how to think about model outputs or how to increase the utility of these fantastic culture / data mining tools in their own lives.

Author here: Thanks for the explanation. Intuitively it does make sense that anything done during "post-training" (RLHF in our case) to make the model adhere to certain (set of) characteristics would bring the entropy down.

It is indeed alarming that the future 'base' models would start with more flattened logits as the de-facto. I personally believe that once this enshittification is recognised widely (could already be the case, but not recognized) then the training data being more "original" will become more important. And the cycle repeats! Or I wonder if there is a better post-training method that would still withhold the "creativity"?

Thanks for the RLHF explanation in terms of BPE. Definitely easier to grasp the concept this way!

Author here: Really interesting work. Updated original post to include link to the paper. Thanks!

Author here: Yes. You are right. I was meaning to paint a picture that instead of the next token appearing magically, it is sampled from a probability distribution. The notion of determinism could be explained differently. Thanks for pointing it out!

Not for self hosting. Self hosting [1] is still free since the code is open source.

[1] https://ntfy.sh/install/

exactly, that's why I mentioned the hosted version.

for all those users who don't want to deal with servers, which Wuf would target to.

so in the end Wuf can just be a fair competitor of ntfy.