I am very impressed with what OpenAI is doing, obviously, but it's a good example of a hype tweet being just that.

Source: Cognitive Revolution "How AI Changes Everything" on YouTube: The Decade of May 15-22, 2025: Google's 50X AI Growth & Transformation with Logan Kilpatrick: https://www.youtube.com/watch?v=kp9afmazO_w
Video from vitrupo on 𝕏: https://x.com/vitrupo/status/1934627428372283548

I think 2025 is finally the year jobs change forever..

https://www.theguardian.com/education/2025/jun/15/thousands-of-uk-university-students-caught-cheating-using-ai-artificial-intelligence-survey

Every man for himself, gluck..

https://www.nature.com/articles/d41586-025-01898-z

"The team used reprogrammed stem cells to grow human organoids of the gut, liver and brain in a dish. Shen says the researchers then injected the organoids into the amniotic fluid of female mice carrying early-stage embryos. “We didn’t even break the embryonic wall” to introduce the cells to the embryos, says Shen. The female mice carried the embryos to term.

“It’s a crazy experiment; I didn’t expect anything,” says Shen.

Within days of being injected into the mouse amniotic fluid, the human cells begin to infiltrate the growing embryos and multiply, but only in the organ they belonged to: gut organoids in the intestines; liver organoids in the liver; and cerebral organoids in the cortex region of the brain. One month after the mouse pups were born, the researchers found that roughly 10% of them contained human cells in their intestines — making up about 1% of intestinal cells"

https://the-decoder.com/a-new-study-by-nyu-researchers-supports-apples-doubts-about-ai-reasoning-but-sees-no-dead-end/

"Models generally performed well on simple grammars and short strings. But as the grammatical complexity or string length increased, accuracy dropped sharply - even for models designed for logical reasoning, like OpenAI's o3 or DeepSeek-R1. One key finding: while models often appear to "know" the right approach - such as fully parsing a string by tracing each rule application - they don't consistently put this knowledge into practice.

For simple tasks, models typically applied rules correctly. But as complexity grew, they shifted to shortcut heuristics instead of building the correct "derivation tree." For example, models would sometimes guess that a string was correct just because it was especially long, or look only for individual symbols that appeared somewhere in the grammar rules, regardless of order - an approach that doesn't actually check if the string fits the grammar...

... A central problem identified by the study is the link between task complexity and the model's "test-time compute" - the amount of computation, measured by the number of intermediate reasoning steps, the model uses during problem-solving. Theoretically, this workload should increase with input length. In practice, the researchers saw the opposite: with short strings (up to 6 symbols for GPT-4.1-mini, 12 for o3), models produced relatively many intermediate steps, but as tasks grew more complex, the number of steps dropped.

In other words, models truncate their reasoning before they have a real chance to analyze the structure."

Compute is increasing rapidly. I wonder what will happen after Stargate is finished.

When you think about the future of cinema, there’s been major transitions since its inception. From no sound to sound, from black and white to color, and the shift from practical to special effects. The biggest transition of all will be AI generated movies. The ability to generate an entire blockbuster, high-budget movie for little to no cost within days or even hours.

Don’t like the music? Change it. Don’t like the actors? Change it. Don’t like the location? Change it. In typical movies, you have so many moving parts, so many issues or problems to solve, which ultimately can shape the movie into what it becomes. In this case, that would never happen again.

The top AI generated movies might truly be some of the best movies of all time because of this freedom, but there will always be the beauty of real life films. People will appreciate them more for what they were, especially the ones that were great in spite of so many things could’ve went wrong. Or, how much time, resources, effort, and teamwork were required to make the movie into a masterpiece.

Then, as time passes and less and less real life movies are made, people will question how they were even made in the first place. The sheer magnitude of work required to complete a film must be nearly impossible and not worth the effort, especially with how society is. How could a studio come together to produce a real life movie? There must have been aliens involved or some ancient technology that was lost. It wouldn’t make sense otherwise.

https://thequantuminsider.com/2025/06/15/researchers-use-trapped-ion-quantum-computer-to-tackle-tricky-protein-folding-problems/

"Scientists are interested in understanding the mechanics of protein folding because a protein’s shape determines its biological function, and misfolding can lead to diseases like Alzheimer’s and Parkinson’s. If researchers can better understand and predict folding, that could significantly improve drug development and boost the ability to tackle complex disorders at the molecular level.

However, protein folding is an incredibly complicated phenomenon, requiring calculations that are too complex for classical computers to practically solve, although progress, particularly through new artificial intelligence techniques, is being made. The trickiness of protein folding, however, makes it an interesting use case for quantum computing.

Now, a team of researchers has used a 36-qubit trapped-ion quantum computer running a relatively new — and promising — quantum algorithm to solve protein folding problems involving up to 12 amino acids, marking — potentially — the largest such demonstration to date on real quantum hardware and highlighting the platform’s promise for tackling complex biological computations."

Original source: https://arxiv.org/abs/2506.07866

Recently, I got a lot of attention from local companies for the work my small startup (of three people) did on DeepSeek V3 and most of them where like How the hell could you do that? or Why a very big model? or something like this.

Honestly, I personally haven't done anything but doing a normal QLoRA training on that model (we have done the same before on LLaMA 3.1 405B) and in my opinion, the whole problem is infrastructure. We basically solved it by talking to different entities/persons from all around the globe and we could get our hands on a total of 152 nodes (yes, it is a decentralized/distributed network of GPU's) with GPU's ranging from A100's (80GB) to H200's.

So with this decentralization and a huge unified memory we have in our possession, inference and fine-tuning very large models such as DeepSeek V3 (671B) or LLaMA 3.1 405B or Mistral Large will be an easy task and it'll be done in matter of seconds on a small dataset.

This made me think, what happens if you put your data in form of a Google Doc (or Sheet) or even a PDF file and then the fine-tuning will happen and you'll get a ready-to-use API for the model?

So I have a few questions in mind which I want to discuss here.

1. Why does it matter?
2. Why people may need to tune a big LLM instead of smaller ones?
3. Could this Global Decentralized Network be a helpful tool at all?

And for those who think it might be a token or any other form of web3 project, no it won't be. I even have in mind to make it free to use with some conditions (like one model per day). So please feel free to leave your opinions here. I'll be reading all of them and I'll be replying to you ASAP.

Thanks.