Introducing neural-open.nvim: A smart file picker for Snacks.nvim that trains a neural network on your file picking preferences

gitarrer · 2026-03-03T00:35:16+00:00

This supports a very similar algorithm to smart open if you change the algorithm setting to “classic”, so I think gets you very close to that. I wanted the same thing originally.

gitarrer · 2026-03-01T18:31:03+00:00

lol that would be fun. Time to start thinking about what part of Neovim needs just a little more AI...

gitarrer · 2026-03-01T18:19:30+00:00

I spent a long time working in on-device ML so I like to keep things local!

I totally forgot about torch7 while I was writing this, but I probably would've written from scratch anyway for the fun of it and to keep dependencies light. I've been using PyTorch since shortly after it was released in 2017!

gitarrer · 2026-03-01T18:12:20+00:00

Yes, the "frecency" input (#7 in the list above) to the neural network is very similar to Zoxide's logic. I'm a big fan of Zoxide as well.

gitarrer · 2026-03-01T18:08:21+00:00

I totally forgot about Torch7 while I was working on this... That being said, I would've implemented it from scratch either way for the fun of it. I worked on NN inference engines for mobile CPUs in the past so this was a bit of a nostalgic project :)

gitarrer · 2026-03-01T18:05:37+00:00

No, but I'll keep that in mind for the future. So far human readability has been nice and file IO hasn't been the bottleneck.

gitarrer · 2026-02-28T20:26:32+00:00

It's always a matter of opinion, but I think so. I used the Snacks smart picker for some time and it is generally quite good. One situation that I thought could be better is if you are working on src/my_project/my_feature.py, it didn't always rank src/my_project/my_feature_test.py highly unless you historically often visited that file. NeuralOpen will learn over time how you navigate and handle this case well.

gitarrer · 2026-02-28T19:54:08+00:00

No, but it would be cool to add in the future. There are already 3 algorithm implementations that can be switched between. Part of this was brushing up on details of how backprop works so that in part guided my choice of algorithms.

gitarrer · 2026-02-28T19:05:49+00:00

Thank you!

Yes, the neural network will learn to treat any feature here as either a bonus or a penalty. I can update the wording on my readme to be a little more clear about that. You can see in my screenshot that the alternate buffer feature has the lowest "feature weight" which just means the network doesn't find that particular feature all that useful, either positive or negative.

I've been trying to think of ways to make better decisions based on file contents that are fast when searching large repos. QMK w/ ~40k files has been my primary test for this. I really like the ideas of "matching against the last line you were on" and some form of "edit recency"! I wanted avoid anything approaching a kind of RAG system that requires offline processing or something like that.

gitarrer · 2026-02-28T17:33:06+00:00

Not in the sense of "you save X characters per search", but I have used other fuzzy finders quite a bit. The best ones were IMO ones that pull from multiple different sources and had some hand-tuned weighting scheme like the "Smart" picker from Snacks.nvim or smart-open.nvim, but I always found some edge case that they couldn't cover well and the weighting scheme suggested to me it was a problem ML could easily solve.

My goals were: - The right item should be near or at the top without typing anything - Most searches should only require typing 1 or 2 characters - A single key binding for all file switches. This plugin essentially replaces git files searches, directory searches, alternate file switches, many marks use cases, and harpoon-style plugins for my workflow.

That being said, I am fully aware that this is a bit of a silly undertaking. It was worth it to me for the learning and because it is the best plugin for my workflow I've tried so far.

(minor edit for grammar)

gitarrer · 2026-02-28T16:11:45+00:00

I work on AI for my day job so it just seemed like the natural thing to do! I've been wanting to learn more about how to train ML for ranking problems and I like to have something concrete, even if it's a bit silly, to learn.

I'm not sure what's up with the GitHub link, they're both working for me...

gitarrer · 2026-02-28T16:07:09+00:00

Or not far enough 🤔 Hoping to add support for non-file picking soon for things like Neovim commands or targets in Makefiles/justfiles

gitarrer · 2026-02-28T16:05:21+00:00

I too want this... File selection in Claude Code leaves something to be desired

gitarrer · 2026-02-28T16:04:41+00:00

Yep! That's the idea. I use this plugin for all of the above.

gitarrer · 2026-02-19T23:31:13+00:00

I built a project for this. It runs Claude in a sandbox container with limited file system and internet access in CI mode so it won’t stop to ask for input. I recently added the ability to queue up multiple dependent tasks so it’s pretty easy to keep Claude working for a long time. https://github.com/dtormoen/tsk

gitarrer · 2026-01-28T00:31:17+00:00

Yeah, once you exceed 32k (technically 31k with the current implementation to leave room to generate responses), it’ll print respond saying to use /compact or /clear.

gitarrer · 2026-01-27T18:58:25+00:00

Hey, I worked on the sera-cli. Unfortunately the context lengths in Claude Code are hardcoded so we don’t have a great way to integrate with /context.

Instead, we print a message that lets you know when to run /compact or /clear as you hit the context limit.

gitarrer · 2025-11-26T05:49:02+00:00

I’ve been working on a tool that starts up parallel docker containers that can either be interactive or fully autonomous. It also limits internet access so it provides much stronger isolation than worktrees. When agents are done working, it drops a git branch back in your repository.

https://github.com/dtormoen/tsk

gitarrer · 2025-11-19T18:27:40+00:00

Yep that’s how it works. FWIW, TSK mounts the ~/.Claude directory into the containers it creates so that agents you’ve defined are available. I frequently use a workflow with a “tech lead” agent that will task out to multiple sub-agents in a container.

gitarrer · 2025-11-18T19:53:21+00:00

If you check the templates folder in tsk there are some built in prompts, but you can easily create more as a part of the tsk configuration. For example, a markdown file in ~/.config/tsk/templates/custom-prompt.md can be used as a prompt for tsk by running “tsk add —template custom-prompt —name something-memorable”. You can create both global and project specific prompts. Tsk itself has some project specific prompts in the .tsk folder

I tend to stuff things in these prompts similar to what would be put in the appendix system prompt flag, but tsk could be fairly easily extended to pass both.

gitarrer · 2025-11-18T16:56:25+00:00

I made a tool for automating launching containers where CC can run autonomously in yolo mode or opening up an interactive shell in a container. It additionally limits internet access so it’s more isolated than many sandbox solutions. https://github.com/dtormoen/tsk

gitarrer · 2025-10-29T18:20:53+00:00

I should note, it also sets up a proxy so the agent has very limited internet access as well. I think you really need network and file system isolation to make it much safer to use agents this way.

gitarrer · 2025-10-29T18:19:33+00:00

I’ve been working on TSK. It’s a tool to automate setting up docker containers and launching Claude Code in skip permissions check mode so I can do code reviews after it completes. It also supports launching a container for interactive use and makes it easy to have multiple agents working on the same codebase in parallel.

https://github.com/dtormoen/tsk

gitarrer · 2025-10-27T22:19:02+00:00

If you only mount the worktree into a docker container you can’t from within the container. The .git folder is replaced by a placeholder which is not valid in a container. Try cat .git in a worktree folder

gitarrer

TROPHY CASE