51–60 of 121 posts
So new business model meta is: acquire enough compute that you can burn millions of dollars on patentable scientific breakthroughs with unused capacity and on models nobody else has yet. That might actually kind of make sense.
This has always been the end game
Relevant paper [0] "Replication of Quantum Factorisation Records with an 8-bit Home Computer, an Abacus, and a Dog" [0]: https://eprint.iacr.org/2025/1237.pdf
If you want to see other similar quantum computing exploits on 8bit computers:
https://medium.com/@dakk/quantum-computing-on-a-commodore-64... https://youtu.be/7dgAaZa22nU https://youtu.be/Mo177GGJb3g https://youtu.be/zCC3AmM1_lo
Earlier quoted context omitted.
Do you also have a graph for more useful metrics Yes. I wrote about it in my original post. In my own company, the last 2 months, I feel like we've made more feature announcements to our internal staff than they can handle. AI has genuinely made us that much faster. In the past, making one of these announcements every month and the company celebrated. Now we're making making multiple each week. But I suspect you want…
My bad - I misread your original post as general company announcements, rather than feature announcements. Now my question is, if your internal staff are not requesting these features and are struggling to adapt fast enough, how useful are they? To your last question - what do you mean by velocity? Velocity is speed and direction. You may have speed, but beware the brownian motion that is stochastic predictive models…
Now my question is, if your internal staff are not requesting these features and are struggling to adapt fast enough, how useful are they
My neighbour - a few hundred metres further up the road - has two dogs, one of which a surefire genius as it has no problems barking out 334543333-bit RSA factorisations. It must have been solving NP-complete problems for years by now, night after night.
My neighbour - a few hundred metres further up the road - has two dogs, one of which a surefire genius as it has no problems barking out 334543333-bit RSA factorisations. It must have been solving NP-complete problems for years by now, night after night.
Earlier quoted context omitted.
AI just solved a millennium prize problem. In a matter of days. Because of a rumor that someone else solved the same problem with AI. What exactly would AI have to do in order to not be called a bubble?
You don’t understand what a bubble is. How good the technology is is irrelevant. That has nothing to do with an economical bubble. It’s all about massive capital misallocation driven by a frenzy of FOMO, which is specifically the case for AI investments. Economically speaking what is happening is the most obvious bubble possible, it follows everything that would be expected from a bubble where companies are chasing a…
And today these data centers are fully utilized. OpenAI tweeted today that they may need to disable new signups for the Pro subscription in the near future due to capacity constraints.
A year from now, who knows what the situation is going to be like. It seems quite possible that robotics, self driving, research, etc. drive even more demand and revenue.
Stating with any certainty that allocating capital to build infrastructure is a mistake and that there is a correction coming seems unserious.
Relevant paper [0] "Replication of Quantum Factorisation Records with an 8-bit Home Computer, an Abacus, and a Dog" [0]: https://eprint.iacr.org/2025/1237.pdf
The article you linked is about using quantum computer for fake big number factorization. You pick a very big number with a well known easy factorization and then use a quantum computer to factorize it, that is easy because you choose the number very carefully.
This article is about using a LLM to calibrate a quantum chip. It replace the work of a junior researcher (or something like that). In another comment, someone claims that is using a python script for this same task.
I like to think that the python script is an "expert system" that is 1980 AI, and the main article is an "large language model" that is 2020 AI. My guess is that for now the python script is better, but the LLM are advancing very fast and will catch up soon.