heynote VS llm-classifier

Heynote was exactly developed for this purpose. Just one big buffer with sections and lots of shortcuts and nice little additional features: https://heynote.com/

Looking at the GitHub repo[0], I don't see why you wouldn't be able to host it yourself (extra configuration may be required). In the package.json, there is a script for running the web app `npm run webapp:build`, so I'd assume you could do that and then host the built web app however you'd like.

[0]: https://github.com/heyman/heynote

Most of my technical note-taking these days happens inside VS Code. I already have it running, so opening a new window and stripping out the chrome (closing other stuff, hiding sidebars, etc. gives me all I need, _plus_ optional preview depending on on what I'm writing (mostly Markdown these days).

Another option some of my friends like is Heynote (https://heynote.com), but, again, I can do the same with VS Code...

obsidian is good for what it does, but in the last month I saw someone share heynote[1] with me that I have grown fond of as a support to my obsidian note taking

[1] https://heynote.com

I’m eagerly waiting for the vi bindings as well!

P.S.: had a quick glance through the PRs after using it for sometime and saw a draft PR for vi bindings already! - https://github.com/heyman/heynote/pull/51

We do this for the null hypothesis - is uses an LLM to bootstrap a binary classifier - which handles null easily

https://github.com/lamini-ai/llm-classifier

LLMs introduced zero-shot learning, or “prompt engineering” which is drastically easier to use and more effective than labeling data.

You can also retrofit “prompt engineering” onto good old fashion ML like text classifiers. I wrote a library to do just that here: https://github.com/lamini-ai/llm-classifier

IMO, it’s a short matter of time before this takes over all of what used to be called “deep learning”.