GPT-4o

Better performance, twice the speed, and half the cost is a pretty big win. Demoing the voice features is what makes it an announcement

> Humans are more partial to talking than writing.

Amazon, Google, and Apple have sunk literally billions of dollars into this idea only to find out that, no, we aren't.

We are with other humans, yes. When socialization is part of the conversation. When I'm talking to my local barista I'm not just ordering a coffee, I'm also maintaining a relationship with someone in my community.

But when it comes to work, writing >>> talking. Writing is clarity of ideas. Talking is cult of personality.

And when it comes to inputs/outputs, typing is more precise and more efficient.

Don't get me wrong, this is an incredibly revolutionary piece of technology, but I don't think the benefits of talking you're describing (timing, subtext, inexplicit knowledge) are achievable here either (for now), since even that requires HOURS of interaction over days/weeks/months of experiences for humans to achieve with each other.

I’ve found using logic trees with LLMs isn’t necessarily a problem or a deficit. I suppose if they were truly magical and could intuit the right response every time, cool, but I’d always worry about the potential for error and hallucinations.

I’ve found that you can create declarative logic trees from JSON and use that as a prompt for the LLM, which it can then use to traverse the tree accordingly. The only issue I’ve encountered is when it wants to jump to part of the tree which is invalid in the current state. For example, you want to move a user into a flow where certain input is required, but the input hasn’t been provided yet. A transition is suggested to the program by the LLM, but it’s impossible so the LLM has to be prompted that the transition is invalid and to correct itself. If it fails to transition again, a default fallback can be given but it’s not ideal at all.

However, another nice aspect of having the tree declared in advance is that it shows human beings what the system is capable and how it’s intended to be used as well. This has proven to be pretty useful, as letting the LLM call functions it sees fit based on broad intentions and system capabilities leaves humans in the dark a bit.

So, I like the structure and dependability. Maybe one day we can depend on LLM magic and not worry about a team understanding the ins and outs of what should or shouldn’t be possible, but we don’t seem to be there yet at all. That could be in part because my prompts were bad, though.

The entire exchange at the comments below is appalling. Didn't expect to see so many emotionally retarded people on HN.

It is absolutely amazing. Thank you to everyone at OpenAI!