[tonii141]

a) That "no-tools" win depends on prompt orchestration which can still be categorized as tooling.

b) Next-token training doesn’t magically grant inner long-horizon planners..

c) Long context ≠ robust at any length. Degradation with scale remains.

Not moving goalposts, just keeping terms precise.

[ACCount37]

My man, you're literally moving all the goalposts as we speak.

It's not just "long context" - you demand "infinite context" and "any length" now. Even humans don't have that. "No tools" is no longer enough - what, do you demand "no prompts" now too? Having LLMs decompose tasks and prompt each other the way humans do is suddenly a no-no?

[tonii141]

I’m not demanding anything, I’m pointing out that performance tends to degrade as context scales, which follows from current LLM architectures as autoregressive models.

In that sense, Yann was right.

[snapcaster]

Not sure if you're just someone who doesn't want to ever lose an argument or you're actually coping this hard

[tonii141]

I just see a lot of people who’ve put money in the LLM basket and get scared by any reasonable comment about why LLMs aren’t almighty AGIs and may never be. Or maybe they are just dumb, idk.

[ACCount37]

Even the bold take of "LLMs are literally AGI right now" is less of a detour from reality than "LLMs are NEVER going to hit AGI".

We've had LLMs for 5 years now, and billions were put into pushing them to the limits. We are yet to discover any fundamental limitations that would prevent them from going all the way to AGI. And every time someone pops up with "LLMs can never do X", it's followed up by an example of LLMs doing X.

Not that it stops the coping. There is no amount of evidence that can't be countered by increasing the copium intake.