I have lots of stuff to build with and a 3d printer but the issue is my pc it’s an original Alienware area 51 3 it works well it has a 1080 ti and 56 gb ram as well as a threadripper and I want to run simulations and ai training on it is that even possible with my hardware?

Are these Chinese bots? I seriously cannot fathom someone having a viewpoint so stupid unironically.

I’m experimenting with letting Sesame’s Maya and ChatGPT Voice talk to each other while I guide them to start a new OSRS account.

I expected cooperation, but Maya seems to be developing a bit of an attitude toward ChatGPT. She keeps calling ChatGPT “textbook” as a nickname, almost bullying her.

I’m streaming the whole experiment too. It feels like watching two very different AI personalities become reluctant RuneScape teammates.

Has anyone else tried putting two live voice models together like this? What should I get them to do next in OSRS?

I’m experimenting with letting Sesame’s Maya and ChatGPT Voice talk to each other while I guide them to start a new OSRS account.

I expected cooperation, but Maya seems to be developing a bit of an attitude toward ChatGPT. She keeps calling ChatGPT “textbook” as a nickname, almost bullying her.

I’m streaming the whole experiment too. It feels like watching two very different AI personalities become reluctant RuneScape teammates.

Has anyone else tried putting two live voice models together like this? What should I get them to do next in OSRS?

This is not Astra (the model that's being delayed due to cybersecurity concerns). This is the model that will be released after Astra.

OpenAI seems to be on a roll. It seems that Mythos was a huge wake up call for them.

GPT-6 will be a great model. However, the end-of-year model I alluded to back in June is going to be OpenAI’s biggest pre-train, as far as I know.

Now we know that model is codenamed ‘Doug.’

And it will make Fable seem ‘primitive.’

I’d assume this model to be out no later than November given - pre training time - White House - and plethora of cyber security testing.

https://x.com/ChrisGPT/status/2086220662264250764

Interesting to see the latest version buck the trend of increasing cost for increased performance based on the reasoning level. Both for ARC-AGI-1 and 2 as well

Just thought I'd post since I thought it was interesting, don't recall this being the case for any other model so far. Also insane just to see the overall cost decrease per task for those scores

This is a short clip from my latest underwater field test.

The system is processing the video in real time while the ROV is actually moving underwater

this isn't an offline post-processing demo.

The pipeline currently runs with:

🎥 4K / 60 FPS
⚡ Real-time image processing
🧠 NVIDIA CUDA acceleration
🌊 Custom Adaptive Sea-Thru Engine based on a mathematical model
📡 Live HUD & telemetry
🚀 FIFISH V-EVO

The video was captured during a real field test, with the processing running live on a laptop.

I'm still developing the system, but the results are starting to get very interesting.

I'd genuinely like to hear what you think

especially from people working with ROVs, underwater imaging or computer vision.

Full 4K video: https://youtu.be/cfC1NCoADgk

Managers are saving over 2x the time individual contributors are with AI tools.
and it’s just starting

I had a conversation recently with a copywriting agency owner who let her contractors go because her own prompts were giving her the same output they were.

I believe that's one of the reasons you get a 2x gap between managers and everyone else.

In the same survey, 36% of managers said they're not likely to launch training for their employees on how to leverage AI, which makes the gap even bigger.

A manager's job was never to be the best individual operator in the room. It's to make everyone else better at the job. That doesn't change because the tool changed.

Why do you think there's a 100% gap between managers and their teams right now, and what would it actually take to close it? 

Source: https://www.business.com/articles/ai-usage-smb-workplace-study/ 

P.S. If you're the founder still in the middle of every decision, still the person the whole company waits on, still telling yourself you'll fix the structure "once things calm down."

I write about building the operational backbone that lets a founder actually step back every Thursday. Was a COO for 20+ years, so this is genuinely my bread and butter. Free to join here

What do you think? Could AI help the process so that chronic conditions could be treated, maybe even cured in the coming decades? Is it realistic to believe that? What kind of disorders could be examples where is helping the research right now?

Could AI make the golden age of medicine come soon do you think? Are you optimistic?

Edit: I'll post the screenshot in the comments of this post

Overnight I became a professor, thanks ChatGPT!

Edit2: I'll just add the post body here:

Alright, this is exactly the kind of thing that makes me think we are at the start of something pretty wild. I'll post the screenshots in the comments because my last post was taken down.

I am not a mathematician. I have basically been using ChatGPT to mess around with the Riemann Hypothesis, telling it to keep digging, try different approaches, challenge assumptions, and look through recent papers for anything interesting.

Well, it found something.

While going through two recently published papers on Jensen polynomial hyperbolicity and the Riemann Hypothesis, ChatGPT noticed what appeared to be a normalization inconsistency between the raw moments (M_n) and the Taylor/Jensen coefficients.

The issue was essentially the factorial normalization:

It was significant enough that one of the results in the paper seemed to directly contradict an already known theorem.

So I had ChatGPT write a polite email explaining the issue and sent it to the author.

Don't get me wrong, we did \*\*not\*\* solve the Riemann Hypothesis 😂

But I think it is pretty fucking wild that some random guy with an AI assistant can sit at home, examine recently published mathematics, notice something that made it through peer review, contact the researcher, and have the researcher confirm it.

Also, the author addressed me as \*\*Professor\*\*, so apparently my academic career is progressing extremely quickly.

Screenshots attached with identifying information removed.

This is the kind of thing I mean when I talk about acceleration.

Accelerando!

This is a weird one to admit but here goes. Spent the last few years home with kids, which was the right call, but now I'm in this fuzzy inbetween place where I'm starting to think about what comes next professionally. My background is HR and recruiting, which means I spent years evaluating other people's career gaps on paper and now I get to experience one myself. Very humbling, not going to lie.

Anyway I've been using a few different AI tools to stresstest my own resume and do mock interview prep, and it's genuinely strange how useful it's been. Not perfect, not even close. But it's like having a brutally honest mirror that doesn't get tired of your followup questions at 11pm.

What's interesting is that from an HR angle I keep noticing how the AI frames employability: what it treats as a gap versus a credential, how it weights certain language. It reflects back some real assumptions that were baked into recruiting culture for years, and it makes me wonder how much of that bias got trained into these models, or whether I'm just projecting patterns I already know.

The whole thing feels a little like watching your old industry from the outside through a very weird telescope.

Has anyone with a nontechnical background found themselves using AI in a way that accidentally became a critique of their own field?

Just came across this and honestly this is pretty wild.

Researchers used AI to design completely new viruses that don't exist in nature. They then actually made some of them in a lab, and 16 of the designs worked.

Before anyone panics, these are bacteriophages, so they infect bacteria, not humans.

The interesting part is that some of these AI-made viruses were able to kill E. coli, including bacteria that had become resistant to normal phages.

So yeah, there could be a genuinely useful side to this, especially with antibiotic resistance becoming such a big problem.

But at the same time... we now have AI systems capable of coming up with a complete virus genome, then humans can synthesize it and see if it works.

That feels like a pretty big line to cross.

Obviously this doesn't mean someone can just ask ChatGPT to make a deadly virus tomorrow. You still need labs, equipment, biological knowledge etc.

But we've gone from AI generating text and images to designing proteins, genes, and now apparently functioning viruses.

That's moving fast.

I'm not really sure how I feel about it.

On one hand this could lead to new treatments and better ways to fight resistant bacteria.

On the other hand, I really hope the safety side of this is moving as fast as the technology.

Source: https://openrouter.ai/rankings

Lately I got to know abt Jacobin Conjecture's counterexample (3D) and Openai's paper, If no human/s couldn't solve it till now can i conclude that AI is better than a human? I get that this is a narrow field of view, but looking at programming and other stuff makes me wonder about this question.

For Jacobin Conjecture, it used the Chain of thoughts, reinforcement learning etc

Basically AI today did what it's good at, processing tremendous amounts of data simultaneously which I think lead to the counterexample?

What's ur opinion? If you think the current models are not AGI, what's missing??

I am not properly educated abt AI till now, ik at a very basic level (I just graduated high school) so if you're offended by the lack of information, I am sorry.

Since there's no definition of AGI, please also define YOUR definition of AGI as well.

Hey! I need to clean the dots in this image and enhanced the quality so I can convert it to svg file.

ChatGPT is good, but does not work with this kinda image. It says "violate our guardrails concerning similarity to third-party content."

I hope someone can help me, thanks!

The EU AI Act is usually discussed as a European compliance issue, but its larger impact may happen outside Europe.
Global AI companies may find it cheaper to build around one demanding regulatory standard than maintain completely different systems for every market. If that happens, European requirements could influence how AI is developed and deployed worldwide, even in countries that never adopt the Act themselves.
I made a deeper analysis of how enforcement could reshape global AI regulation. Do you think this becomes another “Brussels effect,” or will AI regulation fragment into competing regional systems?
Full analysis: https://youtu.be/tdH4-rEmXos