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']

> This is a library showing you how to train your own Claude Code end-to-end.

What does it even mean?

Claude Code is a so called "harness" - a thing that builds a context for LLMs, calls LLMs, executes tool calls etc. It uses various Anthropic models under the hood.

It can also use other models AFAIK.

It cannot be "trained".

Sorry if this comment sounds nitpicky, I'm just annoyed by the imprecise use of terminology.

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

Does this really work? Does this how Anthropic works?

Any practitioners can elaborate?

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?

Not to be confused with nanocoder, the agentic coding harness.

https://github.com/Nano-Collective/nanocoder