heynote VS llm-classifier

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”.