متاح الآن! بحث تيليغرام 2025 — أهم رؤى العام 
AI Post — Artificial Intelligence
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🤖 The #1 AI news source! We cover the latest artificial intelligence breakthroughs and emerging trends. Manager: @rational
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“🤖 The #1 AI news source! We cover the latest artificial intelligence breakthroughs and emerging trends.
Manager: @rational”
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منشورات القناة
Repost from Robots & Art — AI Images and Pictures
This is Seedance 2.5, and it is Hollywood level stuff.
Robots & Art 🌈
| 2 |  🤖 It looks like we’re getting a whole range of new GPT models this Thursday:
• GPT-5.6
• 5.6 Pro
• And a new bidirectional voice model.
@aipost 🏴 | 1 756 |
| 3 |  ⚡️OpenAI’s new GPT-5.5-Cyber just outscored Mythos 5 on CyberGym, a benchmark that tests whether AI agents can reproduce real-world software vulnerabilities.
@aipost 🏴 | 1 634 |
| 4 |  We talk about scaling compute. Peter Diamandis is talking about scaling humanity itself.
@aipost 🏴 | 2 431 |
| 5 | ⚠️Everyone can tell when you used AI to write.
It is not the words. It is the rhythm, here is how to fix it:
1. AI gets caught because its writing can be predictable:
Detectors measure how evenly the text flows. Same sentence length, same shape, line after line. That evenness is what flags it.
2. Em dashes. The word "delve." A typo dropped in on purpose:
People scrub all of it to hide AI and it changes nothing. That wasn't the part giving your writing away.
3. The real giveaway is that every sentence is the same length:
Look at how people actually write. A long thought that builds and winds for a while. Then a short one. A fragment. Then a line that keeps going past where a model would have stopped.
4. So make it uneven on purpose:
"Rewrite this so the sentence lengths are all over the place. Some very short, some long. Wherever three in a row feel the same, break one. Do not tidy it up afterward."
5. The other giveaway is that AI will not commit:
It weighs both sides. It softens every claim. It writes like it is scared of being wrong. People pick a side and say it.
6. Fix:
"Rewrite this with an actual opinion. Pick a side, say it plainly, cut the hedging. Sound like someone who already made up their mind."
7. Last one. AI writing is too clean:
People leave fingerprints. An aside that cuts into the point. A sentence that trails off. A detail so specific nobody would bother to invent it. "Put the human mess back in. An aside, a blunt line, something slightly off. Leave it."
None of this is about beating a detector. A detector and a real reader are checking for the same thing, whether a person was behind the words. Fix that and both stop flagging you.
@aipost 🏴 | 2 797 |
| 6 |  "Can AI ever be Newton? Can AI ever be Einstein? Can AI ever be Picasso?"
Dr. Fei-Fei Li gives a very simple explanations of how today's AI still has a long way to go.
@aipost 🏴 | 3 118 |
| 7 |  SpaceX has finalized a major agreement with open-source AI startup Reflection, granting the company access to Nvidia GB300 GPUs through a compute lease valued at $150 million per month, beginning in July 2026. Total payments could reach approximately $6.3 billion if the contract is maintained until 2029.
Either SpaceX or Reflection can terminate the deal with 90 days’ notice after the initial three months. This marks a shift for SpaceX, which is now positioning itself as a GPU cloud provider, leveraging its Colossus infrastructure for external clients.
Reflection’s business model centers on building and customizing frontier open-weight AI models for government and enterprise use, requiring substantial compute capacity to train these foundational models. Recent changes by other AI providers have increased demand for open-access model solutions, supporting Reflection’s approach.
📰 @aipost | 3 397 |
| 8 | ❗️Vivek, a researcher at Anthropic, published an article on how to be good at research.
Here are the 7 core principles as prompt instructions:
1. "Restate the problem in your own words and define what a successful answer looks like before responding."
From John Schulman's advice: reason backwards from the outcome you want instead of forwards from the most obvious approach. In a prompt, this forces the model to set success criteria before it starts generating, which prevents the generic default response.
2. "Predict the most common answer to this question. Then tell me what it gets wrong or misses."
Vivek describes taste as a muscle you train by predicting results before you see them. This instruction does the same thing inside the model: it makes it aware of its own baseline, then pushes past it.
3. "Prioritize foundational sources over recent or popular ones. Older proven frameworks beat trending takes."
LLMs have the same problem researchers do: shared reading lists produce shared ideas. This redirects the model toward durable knowledge instead of whatever was most common in the training data.
4. "Show your reasoning chain. State each assumption and flag where your logic is weakest."
Paul Graham's observation: an idea feels complete until you try to write it down, and then the gaps show up. This instruction makes the model find those gaps before you have to.
5. "Start with the smallest possible version of this problem. Solve that first, then add complexity one piece at a time."
Claude Shannon's 1952 technique: shrink a problem until it's trivial, crack it, then reintroduce difficulty. Karpathy's version is "overfit a single batch before training at scale." In a prompt, this prevents the model from trying to solve everything at once, which is where vague answers come from.
6. "After answering, list the three strongest objections to your own response and tell me which one is most valid."
Darwin kept a log of every fact that contradicted his theory because he caught his own memory deleting inconvenient evidence. This instruction builds that discipline into the model's output.
7. "Draw from at least one adjacent field outside the obvious domain for this question."
Interpretability borrows from neuroscience. Eval design borrows from mechanism design. This breaks the model out of single-domain thinking, which is where non-obvious connections live.
@aipost 🏴 | 3 133 |
| 9 |  🧠 Sam Altman just dropped a brutal observation about AI.
He said some of the smartest scientists in the field were also the people who slowed it down the most.
Not because they lacked talent. Because they were too sure they were right.
For years, respected experts insisted scaling wouldn’t work. Bigger models wouldn’t lead to major breakthroughs. The approach had hit a wall.
Then reality showed up.
The models kept getting better. New capabilities kept emerging. The predictions kept failing. But many critics didn’t change their minds.
Altman says that’s what happens when a belief becomes part of your identity.
Once your reputation is built around a particular view, updating your opinion starts to feel like admitting defeat. And the smarter you are, the better you become at defending a position long after the evidence has moved on.
He even compared it to a kind of insanity: seeing the data change while repeating the same conclusion.
But his warning wasn’t aimed only at the skeptics. It was also aimed at the people winning today.
Because the moment any belief becomes who you are, instead of something you hold, you lose the ability to update.
And in a field moving this fast, the ability to change your mind may be the most valuable skill of all.
@aipost 🏴 | 3 041 |
| 10 |  🗣Demis Hassabis believes AI could revolutionize medicine within the next few years.
- AI already solved the 50-year protein folding challenge with AlphaFold.
- Future AI systems could design new drugs automatically.
- Drug discovery could shrink from 10 years to months, weeks, or even days.
- Most experiments may happen in simulations before human validation.
- Personalized medicines tailored to individuals could become possible.
- Demis believes AI could bring all diseases within reach of treatment.
Future of healthcare could be extraordinary!
@aipost 🏴 | 3 280 |
| 11 |  Day 1 of vibecoding 😂
@aipost 🏴 | 3 625 |
| 12 |  🗣Dario Amodei, anthropic's CEO, just answered the question everyone is asking. Should you still learn to code?:
1. Coding is going away first. the AI models are doing it already. The broader task of software engineering takes longer but that's going too. If you're learning to code purely for job security, you're learning the wrong thing.
2. Even at 5% of the task you're still valuable. If AI does 95% and you do 5%, you become 20 times more productive. Comparative advantage is surprisingly powerful even when the gap is massive.
3. The professions with the most runway are human-centered ones. Things that mix people, the physical world, and analytical skills together. He uses the radiologist example. The doctor who understands patients and context, not just reads scans.
4. Critical thinking might be the most important skill of the next decade. When AI can generate anything, the ability to tell what's real from what's fake becomes rare and valuable. You don't want false beliefs. You don't want to get scammed. That’s his actual advice to a 25 year old.
5. AI can make you stupider if you use it carelessly. Anthropic ran studies on this. depending on how you use the model, de-skilling in coding is measurable and real. The tool doesn't cause it, carelessness does.
6. The semiconductor space is his pick for a capitalistic win in the next decade. Physical world, traditional engineering, direct AI tailwind, not software but chips.
@aipost 🏴 | 3 688 |
| 13 | 🔥Sakana AI just launched a model that doesn’t act like a model.
Meet Fugu: a single API that secretly runs an entire team of AI agents behind the scenes.
Instead of answering everything itself, Fugu can:
• Pick the best model for the job
• Delegate tasks to specialist agents
• Verify results
• Combine everything into one final answer
The wild part? Fugu can even call copies of itself recursively. To developers, it looks like one model. Under the hood, it’s an AI manager coordinating an entire workforce of AIs.
Sakana says its flagship Fugu Ultra performs alongside frontier models like Fable and Mythos on demanding reasoning, science, and engineering benchmarks without the export-control headaches tied to some cutting-edge systems.
Think of it as the difference between hiring one genius and hiring an entire company through a single email address.
The age of “one model = one brain” may be ending. The future might be AI teams masquerading as a single model.
Source.
@aipost 🏴 | 3 500 |
| 14 |  +2⚡️Is Tesla secretly turning its charging stations into AI data centers?
Tesla quietly filed an application for MEGAPOD, a modular system designed for AI computing. On the surface, it looks like another piece of infrastructure. But the bigger idea could be far more ambitious.
Imagine Supercharger sites doing double duty: charging EVs during peak hours and supplying compute power for AI workloads when demand is low.
Tesla already has thousands of locations with power connections, networking, cooling expertise, and energy storage. Add AI hardware, and those sites start looking a lot like a distributed network of mini data centers.
If that vision becomes reality, Tesla wouldn’t just own a fleet of vehicles. It would own one of the world’s largest decentralized AI infrastructure networks.
@aipost 🏴 | 3 479 |
| 15 | ⚠️Silicon Valley’s richest people are betting that aging is a bug and AI might be the fix.
Anthropic CEO Dario Amodei thinks AI could compress 100 years of biological progress into just 5–10 years. Sam Altman has personally invested $180 million into Retro Biosciences, a startup trying to extend healthy human lifespan.
Peter Thiel has spent years funding longevity research, backed the Methuselah Foundation, experimented with human growth hormone, and has openly discussed young blood transfusions as a potential anti-aging therapy.
Jeff Bezos poured $3 billion into Altos Labs, making it one of the most heavily funded biotech startups ever. He has also backed Unity Biotechnology, Sana Biotechnology, and Denali Therapeutics.
Coinbase founder Brian Armstrong argues aging is the world’s biggest killer, claiming it causes more than 100,000 deaths every day. He launched NewLimit to develop treatments that could slow or reverse the process.
Larry Ellison has funded anti-aging research for more than two decades.
Even Elon Musk, despite warning about the risks of leaders living forever, says longevity is an “extremely solvable” problem.
The biggest race in tech may no longer be building AI, it may be using AI to make aging optional.
@aipost 🏴 | 3 978 |
| 16 |  Yann LeCun says Elon Musk’s AI ambitions are already falling behind.
The Meta chief AI scientist called xAI a failure and predicted Musk’s AI push will ultimately collapse, arguing that the company can no longer keep pace with the industry’s leading labs.
LeCun pointed to reports that xAI is leasing capacity from its massive Colossus data centers to rivals such as Anthropic and Google, calling it a sign of weakness rather than strength.
He also argued that attracting top AI talent will become increasingly difficult, claiming Musk’s treatment of some of xAI’s original cofounders has hurt the company’s reputation among researchers.
@aipost 🏴 | 3 655 |
| 17 |  Amazon may be making its biggest move yet against Nvidia.
The company is reportedly exploring selling its Trainium AI chips directly to data centers and enterprises, instead of keeping them exclusive to AWS.
That would be a major shift. Today, companies need Nvidia GPUs or cloud access to train AI models. Tomorrow, they could buy Amazon’s chips and run them on their own infrastructure.
The timing is notable: OpenAI, Anthropic, and Uber are already using Trainium through AWS, and Amazon says demand for its AI chips is surging.
The bigger threat to Nvidia? Amazon isn’t alone. Google is also starting to offer its TPUs to external customers.
For years, hyperscalers were Nvidia’s biggest customers. Now they’re becoming its biggest competitors.
@aipost 🏴 | 3 767 |
| 18 | The AI prompt cheatsheet nobody taught you:
Role → Tell AI who to be
Context → Give background
Task → Be painfully specific
Format → List, table, or paragraph
Constraint → What to avoid
This structure cuts rework by 60-70%. Five lines. Better than 90% of prompts.
@aipost 🏴 | 4 178 |
| 19 |  Amazon just dropped its Sam Altman movie and the timing is raising eyebrows.
Amazon MGM has reportedly walked away from Artificial, Luca Guadagnino’s film about the dramatic 2023 OpenAI boardroom coup that briefly saw Sam Altman fired and then reinstated as CEO.
The twist? Amazon recently deepened its partnership with OpenAI, committing tens of billions of dollars to the AI company. Soon after, the studio decided the film would be “better served” by another distributor.
The movie stars Andrew Garfield as Altman, Ike Barinholtz as Elon Musk, and Yura Borisov as OpenAI co-founder Ilya Sutskever. Reports suggest the film portrays several AI leaders in a less-than-flattering light.
So a film about one of Silicon Valley’s biggest power struggles is now looking for a new home, right after the studio behind it became one of OpenAI’s biggest partners.
@aipost 🏴 | 4 081 |
| 20 |  The world's first Museum of AI Art has officially opened in Los Angeles.
DATALAND is now showcasing AI-generated work in a dedicated space. What started as a niche experiment has moved into a full museum setting, signaling a major shift toward mainstream acceptance.
The AI art era is already here.
@aipost 🏴 | 4 731 |
