kener VS llm-classifier

1. Kener

I think the incident.svelte file could use some love, is it best practice to put part of the phrase somewhere else? Doesn't it increase the cognitive stress? Like there is somewhere a phrase, but part of the text is being calculated https://github.com/rajnandan1/kener/blob/74ea57d6bbf6ac4dd3e...

Isn't it easier to understand what is going on just by calculating the condition on the top and put the text on the markup based on that condition?

I feel like there are few places where in order to don't duplicate part of the text it's being made extremely difficult what the text is going to be by putting it far away

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”.