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]
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'] def remove_falsey_values(l):
l[:] = (x for x in l if x)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.
that being said, there are other potential explanations
Any practitioners can elaborate?
Many recent OSS models have great tech reports where you can learn more about these kind of things: Kimi 2.5 https://github.com/MoonshotAI/Kimi-K2.5/blob/master/tech_rep... GLM 5 https://arxiv.org/abs/2602.15763 DeepSeek R1 https://arxiv.org/pdf/2501.12948
bdbdbdb•3d ago
Why would people want to spend $200 to train a coding model when there are free coding models?
desideratum•3d ago
wongarsu•3d ago
Kaparthy's notes on improving nanochat [1] are one of my favorite blog-like things to read. Really neat to see which features have how much influence, and how the scaling laws evolve as you improve the architecture
There's also modded-nanogpt which turns the same kind of experimentation into a training speedrun (and maybe loses some rigor on the way) [2]
1 https://github.com/karpathy/nanochat/blob/master/dev/LOG.md
2 https://github.com/kellerjordan/modded-nanogpt