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I think the joke here is getting missed

here is how I attempted to explain it to my company's AI group chat, is this roughly accurate?

"instead of autoregressive string output it instead outputs structured type-safe 'decisions' with probabilities/confidence scores, each generated in parallel

so sort of more like a Large Classification Model than a Large Language Model? or, maybe better to think of it as a sort of "shift left" in the LLM's transformer architecture, allowing you to replace the predefined token vocabulary of an LLM with a prescribed set of 'decisions' that need to be made based off the input context; and exposing those probabilities directly so they can be integrated into the system logic, instead of just sampling from top-K.

all of this while still being instruction-tuned (!!!)"

It's always been possible to build classification pipelines using LLM embeddings as the input. seems like this is a much more sophisticated / useful application of that concept

What version of Gemini were you using? i.e. were you calling it locally via the API or thru their Gemini or AI Studio web apps?

Not every LLM app has access to web / news search capabilities turned on by default. This makes a huge difference in what kind of results you should expect. Of course, the AI should be aware that it doesn't have access to web / news search, and it should tell you as much rather than hallucinating fake links. If access to web search was turned on, and it still didn't properly search the web for you, that's a problem as well.

I had hardly written any code prior to ChatGPT other than teaching myself some VBA.

Since then, using Gen AI tools to learn + write code, I have deployed a functioning full stack application to the web. NextJS on Vercel, with a backend server also deployed running on Python, and a Supabase DB. Is it the best application ever making loads of money? Definitely not. Are there things wrong with it? Absolutely (although I promise I'm not exposing sensitive env vars and API keys to the web). Did the first versions look like absolute ass as I clumsily figured things out and made bad mistakes? You bet. But it's a functioning app that does some useful things and has real users.

I would never have imagined doing this in a million years prior to Gen AI.

Do some devs see mixed results depending on how they're using the tools? I'm sure. Is Gen AI overhyped broadly speaking? Probably so. But when I see people say it's a delusion, waste of resources, and everyone is wasting their time on it ... for me, it just doesn't line up.

It sounds like you've made up your mind! Best of luck.

Consider using a better AI IDE platform than Copilot ... cursor, windsurf, cline, all great options that do much better than what you're describing. The underlying LLM capabilities also have advanced quite a bit in the past year.

You could easily test this for yourself right now, two different ways.

A) Go and ask ChatGPT the same question that Arve Hjalmar Holmen asked. (Make sure to turn off the 'Web Search' functionality, otherwise it will simply share what it finds on Google. We want to see what ChatGPT actually 'knows' in its 'internal data'.) After you do this, do you get the same answer, or something completely different?

B) Go and use ChatGPT and tell it the Answer to some Question about yourself that is not publicly known or recorded on any internet source. Then, tell one of your friends to ask that same Question to ChatGPT, and see what answer they receive. If ChatGPT is simply 'storing' information in its 'internal data' then surely your friend will receive the secret answer that you shared with ChatGPT - right?

Is there any evidence that ChatGPT is continuously making this false claim to multiple users? Can the false claim about this specific person be reproduced by anyone else or did it just appear randomly?

I can't reproduce the error. Is there any evidence that others are getting the same answer?

Shhh ... you're spoiling everybody's confirmation bias against LLMs. They are obviously terrible at coding, just as we have known all along, and everybody should laugh at them. Nothing to see here!