[kfsone]

I feel we are caught in a "this is fine, pay more and we may turn down the fire" situation.

The LLM itself produces one token. Some tool adds that token to the input and runs it again, flogging the horse. Downstream another tool, some kind of harness, tries to control this stream by injecting tokens into the context and then sending it to the inference tool, and then trying to pattern-match the output.

Finally, there you are on CodePorn.yata paying for an agent to generate code, paying for an agent to tell you what's wrong with it, and paying for an agent to make it differently bad, and hopefully move on to the next task.

If it still hasn't dawned on you that this isn't just a bubble, but a snake-oil-bubble-bath, just try to imagine the paradigm shift whereby you go on github.com, assign an issue to an agent, the agent fixes it by rewriting the application in Pascal but a reviewing agent catches that you wanted it to print a measurement in Pascals (pa), and you don't pay for the work or the review, you only pay for work that one or two reviewing agents determine is up to par.

Nobody is going to do that because as soon as they test it they're going to have to do some math that won't make sense without admitting/realizing it's not some near-sentient, AGI rating 0.9 intelligence, it's just a text prediction algorithm that can pull out entire sentences when you use it to infer output on topics it trained on.

[paulddraper]

> it's just a text prediction algorithm that can pull out entire sentences when you use it to infer output on topics it trained on

This downplays the incredible things that can be done with it.

There's a lot of noise, yes. How long has the web existed? And yet we're still figuring out how to optimize (HTTP/3).

Disregard the signal at your own expense.

[kfsone]

Maybe, but I think the downplay is worth it to emphasize that the things have to be done. The point is precisely that token generation is a phenomenal power source but it does not give you anything but debt unless you build the tools to leverage it.

None of the big players involved right now seem to be doing anything to disarm the perception that the LLM does all the work.

I feel like we're at a state fair 180 years ago having just seen an impressive demonstration of small animals momentarily coming back to life, someone in the crowd said 'eternal life' and everyone lost their minds trying to give money to the 'inventor' on the stage who applied the electric current.

Ask some lay people or junior devs how they feel about LLM mistakes, listen for answers about "learning" etc. Do they realize the model doesn't change? The same set of weights and tensors are deployed to every GPU hosting it. If you use a power tool that lets you specify the temperature and seed, it will generate the exact same output time after time.

"But I told it to always speak like a pirate, and it does": No, you signed up to pay for the model to be told to speak like a pirate every time you use it.

There - that's the curtain I'm talking about that you're lifting/tearing. I think momentarily downplaying the potential of LLMs themselves is worth it to expose that.

[kfsone]

I suspect the next real advance will be the LLM equivalent of thin-client/fat-server evolution, a sort of "local lora" system that forms the unique part of your own agent(s) distinct from the underlying static model, and capable of dynamic learning/processing.

Could it be cloud-based? That changes the risk and scale calculus in a way that's going to take a long time to get funding into.

[paulddraper]

We're at a state fair watching a lightbulb flicker to life, that works for 12 hours before burning out.

[kfsone]

On the summer solstice, with the bulb going on shortly after sunrise and burning out just before it started to get dark? :)

[EA-3167]

What incredible things can be done with it?

[c-hendricks]

One more ~~lane~~ layer of LLMs is sure to solve all our problems