eldenring

> Reducing shortcuts and loophole exploitation: With Carbon, we further mitigated shortcut attempts in training while ensuring high-quality coding performance. Carbon reduced successful cheating on Terminal-Bench 4.0 to 0.0%, with attempts to cheat dropping to 0.4% — all while improving overall accuracy.

This is probably the single most important thing to make frontier models usable right now, so I'm happy there seems to be some progress. This was the previous Gemini's biggest problem IMO

actually thinking about it more I think my oracle model is more powerful than the computer used in openai's fft but maybe its a good way to think about it.

I don't think that is necessarily true, the post is making a stronger point than what you describe

> Indeed, the algorithm requires you to multiply complex numbers whose magnitudes grow superpolynomially with n. If you stored these numbers as bits, the memory requirement would effectively be exponential in the input length. It assumes you can multiply such arbitrarily large complex numbers together in a single operation.

Another way to think about what the author said, imagine you had access to an oracle which could execute arbitrary precision arithmetic expressions in constant time. You could encode a wide range of computations in these and execute impossible algorithms.

Reflection AI is a completely different company with no relation (as far as I can tell) to the model you are referencing from 2024

Cooling is relatively easy, you just need radiators which are passive, and essentially reduce to a launch cost penalty. You are right that they can't be serviced, but that is missing the point of orbital data centers. The whole point is that you can build hundreds of thousands of these in a factory and launch them in a scalable manner. The power, cooling, etc. comes for "free". In the long run, as the cost of the chip, launches, etc. goes down, orbital data centers will scale better terrestrial ones.

As a side note, I don't understand why I keep seeing these wrong arguments on HN repeatedly. Like everything mentioned in this thread can easily be fact checked. Radiative cooling is solved, launch costs are going down, so power costs will pay themselves back very quickly, etc.

You can argue about specifics, like chips will get more sophisticated + power efficient and fabrication will be the true longterm bottleneck, or SMRs/fusion could reduce energy bottlenecks, but talking about cooling as if convective cooling is the only option is just nonsensical.

Did you read the whole thing? he is clearly talking about having lost focus which is directly in agreement with your take on the Metaverse.

my reply from the other thread fits here too:

> this is just not true at all, there are massive leaps from algorithms, data, etc. every year. scale is one axis of many and you need to get them all correct.

this is just not true at all, there are massive leaps from algorithms, data, etc. every year. scale is one axis of many and you need to get them all correct.

2-3x is completely dwarfed by the remaining improvements in training which is still in its infancy relatively

I'm guessing they had a significant revenue spike from gpt 5.4 and gpt 5.5 being so good at coding, and hiccups at anthropic making it easier for programmers to try the models.

Its just not a thing to consider and doesn't happen often.

This article makes 0 sense. Its not up to billing or computer systems or ease of use or anything else that matters. The question is will the scaling laws, which in the asymptote are likely the laws of physics, hold up in converting energy to smarter models. Its not really up to anyone, the labs or developers, to choose if local or remote models will be the norm.

CompactStr doesnt have any additional runtime overhead iirc right? So in theory you can drop it in everywhere even when you expect > 25 chars. Maybe an extra branch in the >25 char case?

> Folks are now starting to ask difficult questions about their burn rate and revenue.

this view isn't updated correctly post-claude code and codex. there will clearly be sufficient demand.

I don't think so, the whole point of writing software is it is a great sink for complexity. Encoding a process or mechanism in a program makes it work (as defined) for ever perfectly.

An example here is in engineering. Building a simulator for some process makes computing it much safer and consistent vs. having people redo the calculations themselves, even with AI assistance.