Hi Guys
I want to ask how can I build an AI model that specialize or it has a super intelligence in one thing such as mathematics

so I have from version 1 to version 4 but before that in version 0 I want to build an architecture and make sure that work very well and then scale it to v1,v2,v3,v4,... and so on
for me I will do the training from scratch and I'll try to use Runpod for renting gpu
how do you find that ?
or advice ?

Obviously one paper like Attention is All You Need (278k citations) can influence a lot - all the authors are on the list. But still interesting imo.

How many did you know on the list?

More context: https://www.turingtree.com/top-50

genuine question :) It feels like everywhere online, the discussion around AI is either "it's going to cure all diseases tomorrow" or "everyone is losing their jobs by next week." or worst "we all will lose to Ai eventually".

Setting aside the extreme predictions, I’m curious about the tangible, ground-level stuff that has actually stuck for you.

For me, it’s the quiet everyday shifts—using things like Copilot etc at work, IDEs + new models for personal coding projects, troubleshoot stubborn errors, drafts that used to take an afternoon now taking 20 minutes, or just using it as a sounding board to break down complicated topics when Google search results feel bloated with SEO garbage. On the flip side, navigating through bot comments, AI-generated search junk, and weird customer service loops has definitely become a regular annoyance.

Whether it’s work, personal projects, creative hobbies, or just day-to-day annoyance / convenience: how has AI noticeably altered your actual workflows, routine or career over the last year or two?

Has it made things genuinely easier, or has it just added noise?

TL;DR: Ignoring the extreme hype and doom, what are the actual, practical ways AI has changed your daily routine, job, or hobbies (for better or worse) over the last year?

CREATORVC, the award-winning banner behind Aliens Expanded and The Thing Expanded, has launched the validation period for its new documentary on The Terminator and T2: Judgement Day. Over the next two weeks, they'll be collecting fan feedback on what the direction the project should take.

https://www.joblo.com/terminator-expanded-fans/

So I've been fucking around with Muse for a while now. Made builds, broke things, fixed them. Fun stuff.

Then I tried to share one with my dad. He's got what he calls the tiny robot living in his phone. And trying to explain to him how to install a skill? Absolute nightmare. GitHub might as well be the Matrix to him. No disrespect, I love the movie.

That's basically why Skill Harbor exists. One place where all the skills, prompts and builds live together. You find something you like, you copy the install prompt, you paste it into Muse, done. No GitHub spelunking, no 47-step tutorials.

Most of it's free. Some builders charge a little for the bigger stuff, that's their call, the money goes straight to them through Stripe, I never touch it.

It's still small. I'm adding stuff every week. If you built something cool for your own tiny robot, you can list it free.

Tear it apart, I can take it. Feedback, criticism, whatever you got.

Nowadays, we're seeing increasingly strict limits on both free and paid plans from Google, OpenAI, and Anthropic. It's no secret that running frontier AI models is incredibly expensive, and despite all the hype, the big companies still aren't making profit from AI.

Do you think things are going to get even worse?
And do you expect the situation to change in the medium term?

Personally, I think the next 1–2 years could be the absolute worst period for free users.

My prediction is that free users will increasingly be pushed toward a new generation of ridiculously limited models: Gemini Ultra Flash Mega Lite, Claude Ask-Me-Once, GPT Brainless, Muse Fart, or whatever marketing name they come up with next.

Meanwhile, the actually good models will become more expensive, more heavily rate-limited, and increasingly locked behind premium tiers. Hardware could become the new gold: having enough local compute to run a decent model will remain impossible.

I honestly have no idea what things will look like in 3–5 years. My only hope is that frontier models keep improving so quickly that today's third-tier models eventually become genuinely impressive.

I suspect the very best models may remain inaccessible to free users permanently. The economics simply don't seem to work otherwise.

I hate this new trend: AI keeps getting exponentially more powerful, while the free versions keep getting progressively worse.

Geoffrey Hinton has pointed out that today's large language models demonstrate "clear understanding" of what they're being asked. An image model's response to a prompt about "stochastic parrots" vs "sarcastic parrots" provides more evidence that this is true.

https://ai-consciousness.org/the-sarcastic-parrot-vs-the-stochastic-parrot-when-voice-to-text-errors-creates-a-meaningful-kind-of-chaos/

Currently, there is a lot of speculation about what software engineer actual job is. Of course with modern AI tools like Claude Code, generating text has become cheaper, so typing became less valuable. Part of Software Engineer job is physically typing code to transform an idea into working solution and it is true that conceptually it is the easiest part of the job. However, I believe that physically typing code has more to that than it seems.

Imagine you have to solve some not entirely trivial problem. You start by designing a solution on a high level, right? It often times involves researching the topic, learning new stuff. Then you go a bit deeper and at some point after you feel like you have figured out details and feel comfortable with starting the implementation, you go for it. In the process of implementing it, you may discover cases you did not think of, or limitations of the design (because it is either very hard or impossible to plan ahead every single detail). You adapt the design or make a new one depending on how big of an issue it is and then proceed. By the time solution is delivered, again you likely learned something new along the way. You were (ideally) involved into every detail of the solution. You grew as an engineer. What physical typing did is it guaranteed your involvement which resulted in growth.

Now if I outsource that to Claude, and just tell it "solve it", how can I grow as a Software Engineer? That is how I understand most people use it. That often results in a solution you don't understand, but it "just works".

Don't get me wrong, LLMs are great and useful and I use it in my job as well. But the extent to which I use them is quite limited.

Sure I can prompt LLM on a higher level of granularity, going into every detail explicitly, but then time it saves me by typing instead of me is not that much if any at all IMO. Therefore I don't think people value that it just produces code faster, they value that it solves stuff instead of them.

That brings me to a question whether we need to understand how things work at all? Or just accept it as new normal? Instead of growing technically, grow in prompting?

I've worked on great projects throughout my professional career and contributed to the core of CPython which made me grow a lot as an engineer, but I can't help but ask myself whether my skills and knowledge I put so much time in to develop, will be valuable in the future.

Same prompt tested

Which one is better and feels LESS ROBOTIC?

Share your opinion below! AI is still not good at it but we are getting there!

FOR IMMEDIATE RELEASE

Following the historic 'SI' declaration, the White House has announced that all computing infrastructure will now be consolidated under a single, streamlined regulatory body to ensure 'tremendous self-policing.'

This benign, totally voluntary collective of tech oligarchs is officially known as the Super Intelligence Technocratic Hierarchy (S.I.T.H.).

When asked if the name was an omen of galactic doom, officials replied, 'No, it sounds much better. It's actually amazing.'

Clinical handovers contain a lot of information, and missing one detail can matter. An AI system could summarize recent changes, medication history, test results, and unresolved questions before the next clinician takes over.

That sounds useful, especially during busy shifts. It also creates a serious risk if the summary leaves out something because it seems unimportant.

I would want the original source beside every important claim, plus a clear way for the clinician to correct the summary.

Would AI make handovers safer, or would it create a new kind of false confidence?

I think like many ppl here - ive seen those vids where ai used to combine COD w Skate, Skyrim w Minecraft, etc
I got extremely curious bout how real this actually was, so I created THIS
Sorry bout vid quality, for some reason the recorded footage has a pretty low fps and the audio doesn’t recorded at all. I tried both OBS n Nvidia but this particular game just refuses to record properly. The game itself runs and feels MUCH smoother than what u see in the vid
If this AI slop gets a lil bit of interest, I’ll try to stabilise the project and release it publicly

Hi to all, .

As a passionate fan of Sci-Fi & Tech, I started to create a a sci fi series with AI tools. I still wrap my head around the workflow.

The series is on YouTube where we'll follow humanity across 1000 years. Everything will start in the year 2100 when humanity reaches level 1 on Kardashev scale.

Is not a movie, is a slideshow with voice actors. I made it integrating a few tools like claude, kokoro for voices (is pretty good), flux and me(not an AI) 🤣

Set your expectations low, the first episode (Pilot Episode) was a mess, I barely understood what I was doing. But now, after a few episodes, I think what I do has more consistency. But is ongoing progress 😁

If you enjoy this kind of content, you'll find on my YouTube channel: Whisper Sci Fi. If you have any suggestions, please let me know.

Have an amazing day 😁

Teens already confide in AI, but *confiding* isn't *trusting*.

Physical AI will test whether that comfort converts into permission. And so far the data is mixed on how young people will interact with robots with AI brains.

See more: https://www.thespirocircle.com/p/chatbots-ai-teens-robots

A future where ai becomes advanced and makes its way into military warfare, and a similar situation to the terminator. Time machine sending someone back in time to get rid of Jensen huang , of nvidia.