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🤖 The #1 AI news source! We cover the latest artificial intelligence breakthroughs and emerging trends. Manager: @rational
Mostrar más📈 Análisis del canal de Telegram AI Post — Artificial Intelligence
El canal AI Post — Artificial Intelligence (@aipost) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 791 215 suscriptores, ocupando la posición 97 en la categoría Tecnologías y Aplicaciones y el puesto 20 en la región EEUU.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 791 215 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -34 983, y en las últimas 24 horas de -725, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.68%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.46% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 5 365 visualizaciones. En el primer día suele acumular 3 653 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 503.
- Intereses temáticos: El contenido se centra en temas clave como openai, airline, cell, claude, patient.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“🤖 The #1 AI news source! We cover the latest artificial intelligence breakthroughs and emerging trends.
Manager: @rational”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
791 215
Suscriptores
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-6 1937 días
-34 98330 días
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Publicaciones del Canal
🗣️ Recursive self-improvement: Anthropic co-founder Jack Clark says it could arrive by 2028.
AI systems could help invent their own successors with Claude 10 building Claude 11, and so on potentially “without any researchers involved.”
@aipost 🏴
| 2 |  A Texas-based company, Minicircle, is preparing to offer an injectable gene therapy focused on increasing longevity. This new treatment aims to stimulate the production of klotho, a protein linked to anti-aging processes.
Minicircle plans to launch the therapy outside the U.S. regulatory framework, specifically in regions such as Honduras, the Bahamas, and Panama. The intention is to bypass comprehensive trials typically required by the FDA and instead operate in locations with less stringent oversight.
The company’s approach reflects a trend where biomedical innovation may shift to countries with more flexible regulatory environments if others choose a slower or more cautious path.
📰 @aipost | 1 501 |
| 3 |  The intelligence alliance known as Five Eyes has issued a rare public alert, warning that powerful AI models able to carry out damaging cyberattacks may become available within the next few months.
This coalition, which includes Australia, the United States, the United Kingdom, Canada, and New Zealand, expressed concern that advanced AI systems could simplify the execution of major cyberattacks against both government and business targets.
According to the statement, such models have the capacity to automate specialized cyber functions. They can analyze code, identify vulnerabilities, propose exploits, and coordinate actions across networks—all tasks previously requiring expert human intervention.
📰 @aipost | 1 962 |
| 4 |  ❗️AI data centers may be using far less water than most people think.
According to the Manhattan Institute, data centers account for just 0.2% of daily U.S. water consumption and that figure is falling as the industry shifts to liquid cooling.
The big change is 45°C liquid cooling, which allows many AI facilities to use dry coolers instead of water-intensive cooling towers.
The result? Cooling water usage can drop from roughly 2.6 million gallons per MW per year to nearly zero.
And the benefits go beyond water savings. Liquid cooling is also more energy efficient and makes it easier to capture and reuse waste heat, turning AI factories into potential assets for local communities and power grids.
The future AI data center may consume less water than critics expect and provide more value than just compute.
@aipost 🏴 | 2 116 |
| 5 |  🇪🇺 Mistral CEO Arthur Mensch just gave Europe a stark warning:
Build your own AI infrastructure within two years or send over $1 trillion to American tech companies.
His argument is simple. Global wages are about $50 trillion, and AI could absorb roughly 10% of that value. Europe’s share is around $9 trillion, meaning more than $1 trillion in AI spending is up for grabs over the next five years.
The question is: who gets paid?
Europe already spends hundreds of billions on US digital services every year, helping fund American R&D while falling further behind in the technologies driving future productivity.
Mensch compared AI to energy dependence. By the time a crisis exposes the risk, it’s already too late.
The chips are being allocated. The data centers are being built. The AI economy is being carved up now.
This isn’t really a technology debate anymore. It’s a sovereignty debate.
@aipost 🏴 | 2 758 |
| 6 |  This is Seedance 2.5, and it is Hollywood level stuff.
Robots & Art 🌈 | 3 137 |
| 7 |  🤖 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 🏴 | 4 008 |
| 8 |  ⚡️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 🏴 | 3 303 |
| 9 |  We talk about scaling compute. Peter Diamandis is talking about scaling humanity itself.
@aipost 🏴 | 3 584 |
| 10 | ⚠️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 🏴 | 3 820 |
| 11 |  "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 🏴 | 3 760 |
| 12 |  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 | 3 875 |
| 13 | ❗️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 🏴 | 3 571 |
| 14 |  🧠 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 🏴 | 3 458 |
| 15 |  🗣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 🏴 | 3 660 |
| 16 |  Day 1 of vibecoding 😂
@aipost 🏴 | 3 884 |
| 17 |  🗣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 🏴 | 4 032 |
| 18 | 🔥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 🏴 | 3 799 |
| 19 |  +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 🏴 | 3 905 |
| 20 | ⚠️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 🏴 | 4 231 |
