wwfn 29 minutes ago

Tangential (but topical in that "The threat is comfortable drift toward not understanding what you're doing" is also on the front page):

Is the generated python code in the example wrong?

The prompt

> Develop a Python function that removes any falsey values from a list. Return the modified list without creating a new one.

Is answered with list comprehension, which makes a new list and leaves the original unmodified (never mind that the *args input necessarily can't be a modifiable list?)

   def remove_falsey_values(*args): return [val for val in args if val]
Whereas I'd expect something like

    def remove_falsey_values(l):
          for i in reversed(range(len(l))):
               if not l[i]: l.pop(i)
          # returned list is linked to input l 
          return l

    a = [1, 0, False, 'foo']
    x = remove_falsey_values(a)
    x[0] = 2
    print(a) # [2,'foo']
  • hecanjog 17 minutes ago

    It doesn't fit the requirement to modify the list in place, but the prompt itself contradicts the requirements by asking explicitly for the implementation to use *args and a list comprehension.

    • wwfn 7 minutes ago

      Ahh I didn't see the full original prompt -- it's overflowing into a horz scroll for me. I thought it was the "critique loop" that injected the *args requirement. I guess garbage in, garbage out. Still unfortunate example to use.

jaboostin an hour ago

As someone with zero ML experience, this was a super interesting and digestible read!

  • bwfan123 6 minutes ago

    agree, great educational tool ! tied a bunch of things around coding agents for me.

bdbdbdb an hour ago

Dumb question - and I'm not trying diminish the achievement here, I just genuinely don't understand:

Why would people want to spend $200 to train a coding model when there are free coding models?

  • desideratum an hour ago

    This is a great question. You definitely aren't training this to use it, you're training it to understand how things work. It's an educational project, if you're interested in experimenting with things like distributed training techniques in JAX, or preference optimisation, this gives you a minimal and hackable library to build on.