You're right, the equivalent JS script produces the same sequence of outputs.

It turns out there is a way to emulate Python's asyncio.create_task().

Python:

  await asyncio.create_task(child())

  const childTask = new Promise((resolve) => {
    setTimeout(() => child().then(resolve), 0)
  })
  await childTask

I think this is a subtler point than one might think on first read, which is muddled due to the poorly chosen examples.

Here's a better illustration:

  import asyncio
  
  async def child():
      print("child start")
      await asyncio.sleep(0)
      print("child end")
  
  async def parent():
      print("parent before")
      await child()        # <-- awaiting a coroutine (not a task)
      print("parent after")
  
  async def other():
      for _ in range(5):
          print("other")
          await asyncio.sleep(0)
  
  async def main():
      other_task = asyncio.create_task(other())
      parent_task = asyncio.create_task(parent())
      await asyncio.gather(other_task, parent_task)
      
  asyncio.run(main())

  other
  parent before
  child start
  other
  child end
  parent after
  other
  other
  other

Edit: clarification

Comments URL: https://news.ycombinator.com/item?id=45848018

Points: 2

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=44916183

Points: 2

# Comments: 0

> To find the most informative examples, we separately cluster examples labeled clickbait and examples labeled benign, which yields some overlapping clusters

How can you get overlapping clusters if the two sets of labelled examples are disjoint?

Comments URL: https://news.ycombinator.com/item?id=44314147

Points: 2

# Comments: 0

Similar to how we ended up with the huggingface/tokenizers library for text-only Tranformers.

Interesting, but title is definitely clickbait.

> Skywork-OR1-32B-Preview delivers the 671B-parameter Deepseek-R1 performance on math tasks (AIME24 and AIME25) and coding tasks (LiveCodeBench).

Impressive, if true: much better performance than the vanilla distills of R1.

Plus it’s a fully open-source release (including data selection and training code).

Comments URL: https://news.ycombinator.com/item?id=43569644

Points: 1

# Comments: 0

You can’t pre-bake the context into an LLM because it doesn’t exist yet. It gets created through the endless back-and-forth between programmers, designers, users etc.

I love this sort of “anti-hype” research. We need more of it.

Comments URL: https://news.ycombinator.com/item?id=43320683

Points: 4

# Comments: 0

The linked paper proposes an obvious-in-retrospect form of data augmentation: shuffle the order of the premises, so that the model can’t rely on spurious patterns. That’s kinda neat.

[1] https://arxiv.org/abs/2412.06769

Publishing a high-level description of the training algorithm is good, but it doesn't count as "open-sourcing", as commonly understood.