[authorfly]

This is useful. Why the step of involving LLMs though? I note you cluster the tabs and then GPT-4 is involved to name them. But my tab groups don't usually need names - just the icons tells me what I mostly need to know. Could this work locally better using much smaller sentence-transformer models?

[stoev]

That's a good question. If you have many tabs open from the same few websites (depending on what those websites are), maybe just grouping them based on domain names would be enough to provide context.

But LLMs are needed if you want the product to have a deeper understanding of everything that you are reading and really organise it into groups. You might be reading about architecture across three devices, multiple browsers, from a bunch of different websites. This gives you the opportunity to reunite them and really dive into that topic when you need to.

LLMs are also used to create summaries of each page. So, if some content takes 30 minutes to read, you can have them extract all the interesting information for you in bullet points and based on that you can decide if its worth spending the 30 minutes or if you would rather just close that tab.

So, in short, it's about capabilities. You can have just simple statistical models and regex rules filtering similar websites into predefined categories, or you can have a tool that truly organises your reading and shortens it for you. But for the latter, you need complex models handling a lot of context.