21–30 of 98 posts
In the age of local LLMs I’d like to see a personal recommendation system that doesn’t care about being scalable and efficient. Why can’t I write a prompt that describes exactly what I’m looking for in detail and then let my GPU run for a week until it finds something that matches?
In the age of local LLMs I’d like to see a personal recommendation system that doesn’t care about being scalable and efficient. Why can’t I write a prompt that describes exactly what I’m looking for in detail and then let my GPU run for a week until it finds something that matches?
(the reasonable way is embedding search, which runs much faster with some precomputation, but you still have to store things)
In the age of local LLMs I’d like to see a personal recommendation system that doesn’t care about being scalable and efficient. Why can’t I write a prompt that describes exactly what I’m looking for in detail and then let my GPU run for a week until it finds something that matches?
>The idea was that he could graft queries in this that he did not expect to finish quickly but which he could let run for hours or days and how freeing it was to do more advanced research this way.
In the age of local LLMs I’d like to see a personal recommendation system that doesn’t care about being scalable and efficient. Why can’t I write a prompt that describes exactly what I’m looking for in detail and then let my GPU run for a week until it finds something that matches?
Why would it take a week? Is this because you want it to continuously watch for live data that could match your need?
In the age of local LLMs I’d like to see a personal recommendation system that doesn’t care about being scalable and efficient. Why can’t I write a prompt that describes exactly what I’m looking for in detail and then let my GPU run for a week until it finds something that matches?
This is exactly what I am hoping to get sometimes (but I would say, 1 week is maybe a little long). If I go through my current tasks and see, that for some task I need a set of documents, emails, .., why cant I just prompt the system to get it in 30-ish minutes. But as someone already stated Apple Intelligence is supposed to fill this gap.
Many of us have ongoing problems pending for years - for just "a week", "where do I sign".
It really depends on the task.
Earlier quoted context omitted.
It doesn't solve any problem, you can just search your files using your prefered file explorer (crtl-f) I'd assume most people organise their files so that they know where things are as well.
> you can just search your files using your prefered file explorer This only work if you remember specific substrings. An LLM (or some other language model) can summarize and interpolate. It can be asked to find that file that mentions a transaction for buying candy, and it has a fair chance to find it, even if none of the words "transaction", "buying" or "candy" are present in the file, e.g. it says "shelled out $17…
i really believe that this is not an actual problem in need of solving, but instead creating a tool (personal ai assistant) and trying to find a usecase
Edit0: note to self, rambling - assuming there exist valuable information that one needa to access in their files, but one doesn't know where it is, when it was made, it's name or other information about it(as you could find said file right away with this information).
Say you need an information for some documentation like the C standard - you need precise information on some process. Is it not much simpler to just open the doc and use the index? Then again for you to be aeare of the C standard makes the query useless.
If it's from something less well organised, say you want letters you wrote to your significant other, maybe the assistant could help. But then again, what are you asking? How hard is it to keep your letters in a folder? Or even simply know what you've done (I surely can't imagine forgetting things I've created but somehow finding use in a llm that finds it for me).
Like asking it "what is my opinion on x" or "what's a good compliment I wrote" is nonsensical to me, but asking it about external ressources makes the idea of training it on your own data pointless. "How did I write X API" - just open your file, no? You know where it is, you made it.
Like saying "get me that picture of unle tony in Florida" might save you 10 seconds instead of going into your files and thinking about when you got that picture, but it's not solving a real issue or making things more efficient. (Edit1: if you don't know Tony, when you got the picture or of what it's a picture of, why are you querying? What's the usecase for this information, is it just to prove it can be done? It feels like the user needs to contorts themselves in a small niche for this product to be useful)
Either it's used for non valuable work (menial search) or you already know how to get the answer you need.
I cannot imagine a query that would be useful compared to simply being aware of what's in your computer. And if you're not aware of it, how do you search for it?
In the age of local LLMs I’d like to see a personal recommendation system that doesn’t care about being scalable and efficient. Why can’t I write a prompt that describes exactly what I’m looking for in detail and then let my GPU run for a week until it finds something that matches?
In the age of local LLMs I’d like to see a personal recommendation system that doesn’t care about being scalable and efficient. Why can’t I write a prompt that describes exactly what I’m looking for in detail and then let my GPU run for a week until it finds something that matches?
You could just run a local LLM over every document and ask it "is this related to this query". I don't think you actually want to wait a week (and holding all the documents you might ever want to search would run to petabytes). (the reasonable way is embedding search, which runs much faster with some precomputation, but you still have to store things)