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There is a lot of technological advancement. One I think we might see soon is prompt films. You write the description of the movie you want and you get it to the details. How far do you think we are to achieve this? How will it affect the film industry?

https://fortune.com/2025/07/24/sam-altman-theo-von-podcast-ai-fears-humanity/

Technology like smartphones and GPS completely transformed how we live, communicate, and navigate the world, things we now take for granted. Looking ahead, what emerging tech do you think will have a similar impact by 2035? Will it be AR glasses, brain-computer interfaces or something we haven’t imagined yet?

https://preview.redd.it/3adt203tawef1.png?width=1600&format=png&auto=webp&s=3e5e8c4d0b13b7287d7a1b9ca57368a9b0cd787d

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Hi everyone,
I’m managing an international website that used to have multiple language versions. Those translated versions (about 800 URLs) have been removed and **won’t be coming back**.

Currently, these URLs are showing as **404 errors in Google Search Console**.
I’m considering two options:

1. **Redirect all of them to the main language homepage**
2. **Leave them as 404s**

Thanks

Qubic is building something different:
A decentralized AGI that evolves, thinks, and discovers.

Here’s what makes AIGarth the most radical AI project alive:

Today’s AI systems—ChatGPT, Gemini, Tesla Vision—are narrow tools.
They do one thing well.
But they can’t learn from the real world, grow on their own, or truly understand anything.

AIGarth is built to change that.

Instead of memorizing datasets, AIGarth discovers patterns through interaction.
It decodes the world, rather than consuming prepackaged data.

The goal isn’t the right answer.
It’s the next better one.

Powering this evolution is Qubic’s decentralized compute layer.
Thousands of miners provide real compute power.

The reward? Qubic.
The result? A global, unstoppable AI engine ranked among the world’s top five supercomputers by capacity.

Inspired by neuroscience, AIGarth learns like a brain.
It doesn’t just predict text—it simulates cognition:
Memory. Sensory feedback. Prediction loops.

The aim: AGI with awareness, not autocomplete.

Qubic isn’t owned by Big Tech.
No shareholders. No gatekeepers.

It’s governed by the people who run it and powered by Qubic.
AIGarth is open by default—designed for everyone.

LLMs hallucinate.
AIGarth learns.

Its researchers are even working on new ways to measure machine consciousness, inspired by how we detect awareness in animals.

Qubic isn’t riding the AI wave.
It’s building the ocean.

AIGarth is more than a model.
It’s a movement toward AGI that’s open, ethical, and truly intelligent.

I’ve been sourcing overstock pallets of name-brand products, including Star Wars toys, Nespresso machines, Hart tools, and other well-known brands. However, I’ve run into challenges when advertising them—Google and Facebook restrict these listings to authorized dealers only.

Since I recently launched a website to sell these products, I’d love to hear suggestions on the most effective ways to market them without violating platform policies. What strategies have worked for others in similar situations?

Any advice on alternative advertising channels, SEO tactics, or workarounds would be greatly appreciated!

Also, if there are any suggestions on how to better optimize my website or any appearance issues I should fix feel free to let me know, thanks!

Website: [https://overstockhq.com/](https://overstockhq.com/)

Hey folks,

I have been trying to implement a research paper that utilized differential transformer block  attention [https://arxiv.org/abs/2502.13189](https://arxiv.org/abs/2502.13189) as a means to denoise background noise from  biological sounds, While training the model I am constantly running into numeric instability (nan loss), specifically this step : —

lambda\_val = torch.exp(lambda\_q1\_dot\_k1) – torch.exp(lambda\_q2\_dot\_k2) + self.lambda\_init

Most probably due to exponential terms assuming large values. I did try clamping the lambda values to avoid this but doing this is resulting in diverging loss values after few epochs.  Anybody how might  have tried this block can suggest any fixes or whether the clamping approach is the right way in terms of loss optimization (I know  clamping is not the best thing for loss optimization ) ?

For the last decade or so, all of my websites were ranking pretty well on Google, but not ranking at all on Bing. However since 2023-24, my Google rankings started to decline, while all of my websites started to rank really well on Bing (1st – 3rd position).

It seems that most of the ranking factors are pretty much inverted on Bing compared to Google, at least in my case.

Has anyone experienced this phenomenon?