Is the gravity set very high or am I getting too old to play Flappy Bird with Transformers?

I believe the podman-compose project is still actively maintened and could be a nice alternative for docker-compose. But the podman's interface with systemd is so enjoyable.

__init__ in Python already has a lot of alternatives. IMO a situation encountered similar to the example is when you do not take advantages of the alternatives or your code design is bad.

Also, specifically for this case, if I need an object for this IO operation, I __would not__ uses dataclass. The first solution would be to create a context manager[2] and a NamedTuple (or dataclass if needs some mutations). If it's not a fit, the classmethod and the dunders __enter__, __exit __ are good if this fd implementation is important for the design of my object. Alternatively, I would just make a builder function outside my object if the implementation of the fd capability should be considered as extra and not a builtin for the object. The dataclass decorator could be added to save 1 line of code but would confuse the intent of the object which is a content manager with some state management.

A side geniune question, why not a type[3] statement to alias the int as a fd? What is the difference with using NewType[3]? In which situation creating a NewType from a base type - here int - is useful?

[1]: https://docs.python.org/3/library/collections.html#collectio... [2]: https://docs.python.org/3/library/contextlib.html#contextlib... [3]: https://docs.python.org/3/reference/simple_stmts.html#type [4]: https://docs.python.org/3/library/typing.html#typing.NewType

One illustration is the CUDA toolkit with torch install on conda. If you need a basic setup, it would work (and takes age). But if you need some other specific tools in the suite, or need it to be more lightweight for whatever reason then good luck.

btw, I do not see much interest in uv. pyenv/pip/venv/hatch are simple enough to me. No need for another layer of abstraction between my machine and my env. I will still keep an eye on uv.

I am really lost here if I have missed something about indices notations with tensors or some visualization techniques. Or maybe the confusion of a tensor operation depends of the field? Or maybe I just miss practices and experiences with indices notations...

Did you face similar issues? If yes, how did you solve them? Or maybe your work does not need that much tools? Or you have been more minimalistic than me for the number of features to be included in the neovim configuration?

[1]: I work in R&D, I need to tweak and contribute in many papers code or different toolboxes/frameworks on top of the team projects.

(I 100% agree with you, just your comment gave me a funny smile. The comparison is relevant.)

Comments URL: https://news.ycombinator.com/item?id=42639306

Points: 3

# Comments: 2

*: a lot of it. Plus, the tracking might be task-centered. I would not bet on a general hand gesture tracking with cheap sensors and bayesian modelling only.

It’s important to take a step back before generalizing an economic or political statement that may not be applicable in other contexts. There are little chances that the supply chain in Lebanon is in the same state as Europe countries' ones, for instance. Thus, this is not another example.

I agree that the author of the tweet fairly underestimates the potential portion of OCR'ed contents in OpenAI's training data. In late August, Nougat[1] is released by Meta, this is an OCR model. Its performance are wild and the model is open source.

I hardly believe that OpenAI does not spend effort on getting more training from OCR content. I also hardly believes that OpenAI waits for a Meta paper to have an internal performant OCR model.

[1]: https://arxiv.org/abs/2308.13418

It's possible that does not work. LoRA (for Low Rank) benefits from the "small changes" introduced during finetuning of a model. The update of the weights has a low rank. If you take a smaller model, it might induce that the rank is not so low, resulting in degradation in metrics by LoRA compression. I would be interested to see if LoRA still has a benefit in this configuration.