You can get older version of TorchCodec that work with older version of PyTorch, but it unfortunately will not have the new features (HDR video decoding; fast Wav decoding) in the latest release. See the compatbility matrix: https://github.com/meta-pytorch/torchcodec#compatibility-wit...
That's not what the author means. Multiple times a day, I have conversations with LLMs about specific code or general technologies. It is very similar to having the same conversation with a colleague. Yes, the LLM may be wrong. Which is why I'm constantly looking at the code myself to see if the explanation makes sense, or finding external docs to see if the concepts check out.
Importantly, the LLM is not writing code for me. It's explaining things, and I'm coming away with verifiable facts and conceptual frameworks I can apply to my work.
Yes, and that peer review happens through the ACM. It serves an organizing function. The conferences themselves are also in-person events, and most of the important research papers come out of those conferences.
It doesn't. arXiv is exclusively a pre-print service. The ACM digital library is for peer-reviewed, published papers. All of the peer-review happens through the ACM, as well as the physical conferences where people present and publish their papers.
IEEE may do it, as it's a professional organization. That is, they're a non-profit dedicated to the furtherance of the field. Being open access fits their mission, and the costs can be handled by dues and fees. Springer and Elsevier are for-profit publishers. I don't know how if they can have an open-access business model.
Agreed. In grad school, I used Perl to script running my benchmarks, post-process my data and generate pretty graphs for papers. It was all Perl 5 and gnuplot. Once I saw someone do the same thing with Python and matplotlib, I never looked back. I later actually started using Python professionally, as I believe lots of other people had similar epiphanies. And not just from Perl, but from different languages and domains.
I think the article's author is implicitly not considering that people who were around when Perl was popular, who were perfectly capable of "understanding" it, actively decided against it.
You train on data. Context is also data. If you want a model to have certain data, you can bake it into the model during training, or provide it as context during inference. But if the "context" you want the model to have is big enough, you're going to want to train (or fine-tune) on it.
Consider that you're coding a Linux device driver. If you ask for help from an LLM that has never seen the Linux kernel code, has never seen a Linux device driver and has never seen all of the documentation from the Linux kernel, you're going to need to provide all of this as context. And that's both going to be onerous on you, and it might not be feasible. But if the LLM has already seen all of that during training, you don't need to provide it as context. Your context may be as simple as "I am coding a Linux device driver" and show it some of your code.
Because training one family of models with very large context windows can be offered to the entire world as an online service. That is a very different business model from training or fine-tuning individual models specifically for individual customers. Someone will figure out how to do that at scale, eventually. It might require the cost of training to reduce significantly. But large companies with the resources to do this for themselves will do it, and many are doing it.
> Of course, because I am not new to the problem, whereas an LLM is new to it every new prompt.
That is true for the LLMs you have access to now. Now imagine if the LLM had been trained on your entire code base. And not just the code, but the entire commit history, commit messages and also all of your external design docs. And code and docs from all relevant projects. That LLM would not be new to the problem every prompt. Basically, imagine that you fine-tuned an LLM for your specific project. You will eventually have access to such an LLM.
The tools are at the point now that ignoring them is akin to ignoring Stack Overflow posts. Basically any time you'd google for the answer to something, you might as well ask an AI assistant. It has a good chance of giving you a good answer. And given how programming works, it's usually easy to verify the information. Just like, say, you would do with a Stack Overflow post.
Solutions that shell out to the `ffmpeg` binary are not going to perform well.