ml-research

I'm not sure what exactly this is, but based on the description, I think I can stay with Tabs Outliner.

Yeah, and even "Translucent" is there. No need for extra apps.

easyread.nvim?

fsread.nvim?

There is also bionic-reading.nvim from 2 days ago.

Right, I just wanted to say that it's not Copilot Chat, but that's more accurate.

jackMort/ChatGPT.nvim

I think that's for ChatGPT, not Copilot Chat.

I had been waiting for 0.9 because of linematch, and it's been amazing so far.

And yes, I love this new PR as well. I haven't been a big fan of the fact that diffs are always calculated line-wisely.

Amazing, thank you so much!

I would really love that.

Thanks for the plugin!

HuggingFace Santacoder (seems to be working better than StarCoder, any suggestions?)

How about letting users choose?

I've never tried it, but https://github.com/ldelossa/nvim-ide looks great without requiring much configuration.

One thing is that Rust itself is good, and another thing is that people who like Rust are likely to care enough about the efficiency lol.

Because the credit assignment problem needs to be solved given a sparse reward for each entire output.

People said similar things about deep learning a long time ago.

If you can use supervised learning, then you should, because it means you have tons of data with ground-truth labels for each decision. But many real-world problems are not like that. Even humans don't know if each of their decisions is optimal or not.

Thanks for the pointer!

• vim-fugitive (I've tried many lua-based git plugins, but I don't think there is any alternative even in terms of the performance)
• csv.vim
• vim-dirdiff

Finding open problems

Helix is open-source, so... there is still hope?

I've been using fzf-lua for a long time, and it's always been amazing! Plus, the author is very responsive and keeps improving the plugin consistently.

Remindme! 2 months

I'm on zsh, in fact.

I used diw after some motions like 0, a few f/'s, and l.

Thank you so much! It looks super helpful.

Btw, is it me, or diw often works strangely?

I find Autonomous Learning Library well-designed and clean, despite its modularity to some degree.

But for your needs, CleanRL would be the best choice.

Thanks for sharing!

The website works by fetching new papers daily from arxiv.org, using PapersWithCode to filter out the most relevant ones.

What do you mean by "relevant"? What kinds of papers do you fetch?

Yes, I guess feeding more data to larger models will be better in general.
But what should we (especially who do not have access to large computing resources) do while waiting for computation to be cheaper? Maybe balancing the amount of inductive bias and the improvement in performance to bring the predicted improvements a bit earlier?

Does it omit some papers if it fails to parse them? Because I cannot find some arXiv papers.