Using a 100B parameter LLM to overcome the large amount of experience required to learn how to do natural language processing one's self rather than using a library to do it sure is something.
I should write a bot to analyze users and hide those who are actively and regularly negative online or on a particular forum and hide their comments.
> I should write a bot to analyze users and hide those who are actively and regularly negative online or on a particular forum and hide their comments.
Yes, please! I'd sponsor this work if you want donations.
A browser plugin would be even better.
I want to filter out (or at least highlight): ads, sponsored content, clickbait, rage, negativity, trolling, crypto (much of it anyway), assholes, rudeness, politically partisan, spam emails, etc.
If this could work on every website, and eventually every pane of glass (phone, etc.), it would be truly magical and absolutely game changing for the trajectory of the internet.
I want more signal and less negativity. And fewer people trying to sell me on things (unless those things are interesting ideas).
This is the correct way to fight the algorithm and the walled off platforms.
While this made me laugh and there is some truth to it, the nice thing when running the process described in the blog post is that you don't need to know what or how you want to count - the LLM has the knowledge to classify it correctly enough to get good estimations. Go and Rust are both good examples of words that have multiple meanings and are pre-/suffix to many other words.
It's not doing that though, and if you'd done classification tasks you'd realise the huge benefit that comes from taking a computationally expensive system and using it to solve one off problems that were very time consuming, and doing it easily.
The article says it cost ~$50. That's astonishing. That's 25 minutes of my billable time.
