Early stage might mean good enough to use in a month or two or it might mean “full self driving this year”. There isn’t any way to tell until it happens.

And everyone will want to funnel their data and pay OpenAI/Microsoft in order to be allowed to implement a basically slightly better Alexa?

Dream on.

This is not a technical problem, this is a business problem. Sadly a lot of engineers don't understand that.

Oh, I think you've misunderstood me. Business problems are someone else's gig - I have no intention in making this a product or making money off it. It's for me.

The thing is, I've managed to get this working as an interface for a whole segment of stuff that was a pain in the ass before. My task list is all in one place for the first time, and it talks! With words! I have a pair programmer, who is excited to do stuff, on the command line, 24/7. They also have encyclopedic knowledge of anything that isn't a super deep cut, so I can move through more spaces and find solutions that I never would have dreamed of due to the cognitive load of sifting through textbooks and documentation just to create a [ insert more or less anything here].

If you're looking at the folks here who are getting excited and wondering "What's up with *them*?, this is it. It's not about the Next Big Thing so much as it's about "Holy shit, computers are magic again". For themselves.

Of course, I can speak for some of us. For sure, the hungry lets-make-a-startup folks exist and are currently working on doing that - and that's fine. But to me that's boring. Commerce and markets and economies are toxic to creativity. I've tried Bing-with-GPT and it's AWFUL compared to GPT-4, despite being sorta the same underlying thing.

I'm perfectly happy paying OpenAI to use the thing they built, for myself, for now. I am seriously looking forward to migrating to locally run models, once we get there (and we will).

Just before the point they built this, I was already chaining queries together to do this. I built a plugin system with bits of JS code that are eval'd and arguments injected.

They couldn't have released this at a better time, I have about 30 plugins and i'd say it manages to get the right one about 90% of the time as opposed to about 70 with my hacked together version (but I guess I wrote it and know what to say so maybe that's a bit skewed)

I've found that GPT really like "google style" python documentation. You need to have a chunk of system prompt explain that it should be 'using the tools according to their documentation etc etc', but once you've dialed that in a little stuff like this works a charm (the docstring is what the LLM sees):

@Tool

def add_todo(title, project=None) -> str:

    """
    Add a new TODO.

    Args:
        title (str): A brief description of the task
        project (str, optional): A project to add the todo to, if requested
    """

    logger.debug(f"Adding task: {title}")
    task = Task(tw, description=title, project=project)
    task.save()
    return f"Added [ {title} ] to the { project + ' ' if project else '' }TODO list."