mmq

We have built similar abstractions directly on top of Kubernetes [1]

I was looking at this project a couple of months ago, and I did not understand why not use Kubernetes instead of rebuilding the abstractions. The reason is that Kubernetes already provides other abstractions to run services and batch job, gang scheduling, gpu and other accelerators enabled workflow.

[1]: https://polyaxon.com/docs/sandboxes/overview/

> a) I sound like an LLM when I'm writing articles (possible) or b) turing test AGI something something.

We entered the machinable culture. We spent many years trying to make the machine mimic humans, now humans are mimicking the machine :)

Actually msft CEO mentioned in his presentation that OAI moved to Azure's vector search and AI search services for ChatGPT.

I know that the use-case that I mentioned as well as many of the agentive aspects can be achieved using code. But I have to admit that using the UI and easily create GPTs, whether using them just as templates/personas or full-featured with actions/plugins, makes the use-case much easier, faster, and sharable. I can just @ at specific GPT to do something. Take the use-case that Simon mentions in his blog post, Dejargonizer, I can have a research GPT that helps with reviewin papers and I can @Dejargonizer to quickly explain a specific term, before resuming the discussion with the research GPT.

Maybe this would require additional research, but I think having a single GPT with access to all tools might be slower and less optimal, especially if the user knows exactly what they need for a given task and can reach for that quickly.

Yes, I had it pinned as soon as the UI changed post dev day.

One additional feature that I would like to see: interacting with 2 or more GPTs at the same time where they could perform different tasks based on their specific expertise and capabilities either in parallel or even sequentially as long as the replies/context of the discussion is accessible for further interactions, similar to what can be achieved with the assistants API.

> The default ChatGPT 4 UI has been updated: where previously you had to pick between GPT-4, Code Interpreter, Browse and DALL-E 3 modes, it now defaults to having access to all three. ... So I built Just GPT-4, which simply turns all three modes off, giving me a way to use ChatGPT that’s closer to the original experience.

Isn't that what they have already built-in called "ChatGPT classic". The description litteraly says "The latest version of GPT-4 with no additional capabilities"

Users can rate community notes, not sure if the rating is taken into consideration.

Usually for asking questions about specific details, people are using RAG (Retrieval Augmented Generation) to ground the information and provide enough context for the llm to return the correct answers. This means additional engineering plumbing and very specific context to query information from.

You used the model for fact checking. These models are not good at being used as a knowledge base.

I see, thought you were using GPT-3.5 and moved to GPT-4.

> When OpenAI released GPT-4, we were able to change one parameter, and everything still just worked.

Wouldn't that be the same if you used the OAI js library directly? Basically swapping the model parameter?

Not recent, but the company that runs on top of milvus: https://www.businesswire.com/news/home/20220824005057/en/Vec...

Most demos shared by people can use numpy arrays similar to this https://twitter.com/karpathy/status/1647374645316968449