as someone that quit almost 1 year ago, I can tell you that yes smokers could stare at their screen instead of talking to you when going out to have a puff
Started with friends introducing me to anime in highschool (with english subtitles), which I got hooked on, then got into the music as well, and later into vtubers (so no subtitles when watching live). I haven't ever really been into other entertainment, so for a little over a decade I've been listening to japanese on a near daily basis.
I know it's a meme for people to claim to know japanese from watching anime, which is why I don't claim to be able to speak it, but over time I did pick up enough that I don't need subtitles anymore. I'm slowly working on reading with practice books, wanikani etc, will eventually figure out some way to practice speaking too.
Web search is not a capability of a “bare” LLM, but in an LLM-based system it can be done by giving the LLM access to a “web search tool”, I.e essentially you instruct it to output a specific structured text (typically json but doesn’t have to be) indicating its “intent” to search, and your wrapper intercepts/detects this structured response, does the actual search and returns the results (e.g snippets from top k results) into the context of the LLM amd have it use these to respond to your question.
A similar thing can be done with external documents - your wrapper retrieves docs/fragments relevant to the query, puts them in the context and lets the LLM use them to answer the query. This is called Retrieval Augmented Generation (RAG).
The above is a highly simplified description.
In the Langroid library (a multi-agent framework from ex-CMU/UW-Madison researchers) we have these and more. For example here’s a script that combines web search and RAG:
