Most (all?) aren't bugs by any stretch of the imagination, no. Let's go over the first 5.
1. random.choices(['a','b','c'], weights=[-1,5,1], k=10000)
Negative weight on 'a' silently shifts
Python docs say, "Weights are assumed to be non-negative and finite." Garbage in, garbage out.
2. random.choices(['a','b','c'], cum_weights=[5,2,7], k=10000)
Non-monotone cum_weights makes 'b' unselectable.
...Those weights aren't
cumulative, which the docs say they should be. Again, garbage in, garbage out.
3. statistics.fmean([1,2,3], weights=[-1,1,1])
“Mean” of three values in [1,3] returns 4 — outside the convex hull.
This is just straight-up mathematically correct behavior. It preserves linearity. It fits the commonly accepted definition of weighted mean as `(w1*x1+w2*x2...)/(w1+w2...)`.
The LLM fabricated a fake/idiosyncratic definition of weighted mean in order to claim it's a bug, because it was instructed to come up with bugs.
4. json.dumps({1: 'a', '1': 'b'})
Produces invalid JSON with duplicate keys; round-trip silently drops one entry.
Again, documented behavior/GIGO. Docs say, "loads(dumps(x)) != x if x has non-string keys."
5. urlparse('http://example.com/?').geturl()
Trailing ? (empty query) and # (empty fragment) silently stripped
This is literally just what geturl() is
supposed to do. It's the whole point. Docs say "empty parameters, queries, and fragment identifiers will be removed". The LLM is claiming that geturl()'s primary intended purpose is a bug.
So all of these "eye watering correctness issues" so far seem to be either (1) straight-up correct, or (2) doing things Python explicitly tell you not to do. Same deal with the Numpy "bugs", AFAICT, as I touched on in my previous comment.
In fact, I would venture that we all know those Python bugs are fake, but (unfortunately) the Julia ones aren't. Because the Julia bugs mentioned by Yuri were reported to the Julia bug tracker, and eventually fixed. Whereas if you really thought these are real bugs in Python, then (IMO) you should be reporting them to the Python tracker, not getting mad at me for doubting them.
Moreover, even if they were real bugs in Python (which they aren't), bugs existing in Python still wouldn't change the situation for Julia. The Discourse user who posted it still admitted that they didn't even take the time to verify them.
Surely you must realize how bad it makes Julia look, when its users fling LLM slop to attack Python in response to Julia's issues being brought up? A constructive project should instead talk about what's been done and planned to improve Julia's situation, not tell lies to drag Python down. I liked Julia when I tried it! The JIT plus multiple dispatch is so unique. But this so isn't the way.