StizzurpXDD

I have written how is impact the AI Industry in Nasengetu Tech News website.

Looks very cool, and would love to see on road, but it simply fails as an actual moon buggy.

Firstly, the tyres look like standard air filled ones. The difference in air pressure in the tyres compared to the surrounding vaccum will cause it to explode. For lunar rovers, tyres with no air inside is what works. Secondly, the steering wheel, ankles, suspension, joints, etc. are completely exposed, which would either boil off or freeze standard lubricants, which rely on atmospheric pressure to stay on. Third, the fabric roof, and the rubber tyres and the seat cover would either melt or break like glass during lunar day or night, which are the points of extreme temperatures on the moon.

This is the most generic AI guidelines I have seen since ChatGPT was first launched.

DeepSeek is, as I feel currently, the sole AI company which is actually trying to innovate rather than top mere benchmarks. Others like OpenAI, Anthropic and Google are mostly just competeing with each rather than keep innovating around the clock.

News sources. I would have posted a link but as a new account I can't.

A good way to send encrypted pictures. Just convert the images into audio, and nobody would even think to look at the spectrogram of the audio file. Nice.

If your MacBook has 36GB of memory or less, then the GPU can utilize 66% or 2/3rds of your total memory. If, however, your MacBook has 36GB or more memory, then it can utilize 75% or 3/4ths of your total memory. This is just a software limit, so when I want to run AI models on my MacBook, I just increase the allocation using Terminal.

Dedicated GPU memory is fixed. It can't be altered at all and cannot be used for other tasks either. For example, in MacBooks, if the GPU is idle, the CPU can utilize almost 100% of the total memory because they share a common memory pool, but in a dedicated GPU setup, if the GPU is idle, then the fixed VRAM memory (8GB, 12GB, 16GB, etc.) cannot be utilized by any other component of the PC. Also, in a dedicated GPU setup, the system RAM and VRAM are physically separated. If a CPU loads a large dataset, it has to copy it across the motherboard to the GPU. Because MacBooks have a shared memory pool for both the CPU and the GPU, no copying is required, which makes dataset handoffs much faster than in a dedicated GPU setup.

The title of the first post is crealy readable, while the rest of the texts and numbers are glitched.

It's cool though.

What are the sources of 'Win it', 'Reach Finals', and 'Reach Semis' predictions?

At Google I/O in May 2026, CEO Sundar Pichai announced that Google's upcoming frontier model, Gemini 3.5 Pro, would launch in June. However, the company has now delayed the release to July, citing the need to “gather more real-world feedback”.

I write news articles on nasengetu.com, and make calculators & tools for a living.

With a "dormant May" and Google about to release Gemini 3.5 Pro, I have explained how this can make the AI race "wilder".

I have the website which I built in which it got stuck in a thinking loop. But it won't show any of the 100 tests I did. I am thinking of writing an article on this though. If you want to see, the site's Nasengetu. It is indexed so you can find it in Google search.

I have multiple examples. Just yesterday, when I was trying to implement a caching system for my website Nasengetu, and it completely stuck in a loop-where it runs a log fetch using the git command and then thinks about the result, then runs the command again, and think again. And this repeated until I stopped it when it almost completely exhausted my five hour request limit.

It has become too common recently, even when the models used are same.

All the tasks done were in Google Antigravity 2.0.

This just shows that Google needs to double down on its AI models fast. Even open source chinese models are beating 3.1 Pro and 3.5.Flash in almost everything.

Is this not even more of a concrete proof directly from Google that AI will, in future, become malcious and dangerous? Isn't this just telling us that with the current pace of things, AI will become rogue in the future, capable of carrying cyberattacks?.........

.....yes it is.

Exactly. Google won't like it if they spend millions to make Gemini 3.5 Pro's thinking the best in the world, only for Anthropic or OpenAI to copy it by just seeing the thinking process.

This is not just Anthropic. Almost all big AI companies, including OpenAI and Google, hide their model's actual reasoning. This is because revealing the raw reasoning exposes exactly how the AI processes information. These companies spend in huge amounts on R&D to develop a thinking process that is superior to their competition. Exposing those thinking mechanics to competitors would completely defeat the purpose of their spending. They simply won't do it. It's like you telling your exact location to someone who is trying to hunt you down.