The model is a bit different from React (no deps, hooks can be under ifs) — do coding agents recognize it or they get confused when they see that code?
But it doesn't have the repeating yourself/over-emphasising that you sometimes see in LLM instructions when someone is trying to fight the model's default assumptions
I should have been more precise, yes. But the majority of non-immigrant visas are single intent. H1B requires 100K and if you can’t first enter to see people and attend interviews, chances seem slim in these circumstances, if H1B program is not altogether scrapped.
It made a big difference when it first appeared on the battlefield. Russia has adapted since then, so it's no longer a game changer. But systems like these helped Ukraine hold on, and they continue to do so today.
> Their capabilities should saturate at human or maybe above-average human performance
LLMs do have superhuman reasoning speed and superhuman dedication. Speed is something you can scale, and at some point quantity can turn into quality. Much of the frontier work done by humans is just dedication, luck, and remixing other people's ideas ("standing on the shoulders of giants"), isn't it? All of this is exactly what you can scale by having restless hordes of fast-thinking agents, even if each of those agents is intellectually "just above average human".
Is this a joke? If it's not trainable / differentiable when why do it in the first place? It's just as inefficient and inflexible as it gets compared to tool calling — you have to statically bake programs in the weights, model cannot introspect it and modify, it has very limited IO capabilities, bad performance, bad everything. Its like a weird brainfuck-esque VM — cool that you can do it, but for what except some lulz?
But maybe it's just too genius and I don't understand it.
I'd tend to agree, the only good points I've seen were made by @hedgehog [1] here in this thread:
I'm not sure about the rest but a significant problem with high frequency tool calling (especially in training) is that it breaks batching.
and then later by @ACCount37 [2]:
I'm less interested in turning programs into transformers and more interested in turning programs into subnetworks within large language models.
In theory, if you can create a very efficient sub-net to replicate certain tool calls (even if the weights are frozen during any training steps, and manually compiled), this might help with making inference much more efficient at scale. No idea why in general you would want to do this through the clunky transformer architecture though. Just implement a non-trainable, GPU-accelerated layer to do the compute and avoid the tool-call.
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> Tesla is famously anything but a car company because their cars are mediocre in every way except the battery range
I can't say that I'm a big fan of this guy... But I can tell you this: I learned to drive only after moving to the U.S. recently, and when I had to choose my first car, I found Tesla to be the best among many I tried. It's just awesome, and I don't even use their FSD, the car itself is superb (at least the latest "3"). Minimalistic, no BS, drives well, quiet, comfortable. The same feeling I had with the first iPhone, compared to other phones.
