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Welcome to @prompt, your go-to source for AI insights, breakthroughs, and tools shaping the future of intelligence. Contact: @LightEarendil
💻🧯 PC makers are finally reading the room
At CES 2026, Dell basically admitted what everyone feels but no one in marketing wants to hear: people are not buying laptops because of AI stickers or NPUs. Every new Dell and Alienware machine still has AI hardware inside, but the pitch has shifted back to performance, thermals, screens, actual use cases.
AI hype is staying under the hood where it belongs. Consumers want a good laptop, not a lecture about neural engines.
@Prompt:
Restore and colorize the uploaded historical black-and-white photograph while keeping the overall scene, pose, and subjects consistent with what is visible. Do not change the composition, but you may reconstruct missing or unclear areas when the image is too damaged to determine exact detail.Читать полностью…
Perform a full restoration across the entire image, including:
repairing heavy fading, stains, cracks, scratches, and missing sections
reconstructing faces, clothing, and background features based on the shapes, silhouettes, and visible cues in the original
clarifying all people, objects, fabrics, and surroundings with natural detail
restoring edges and textures as they would realistically appear, without modern stylization
maintaining the historical era feel and keeping proportions natural
preserving identity and expressions as faithfully as the surviving image allows
When details are too obscured to recover directly, infer them in a historically plausible and realistic way, staying consistent with the likely clothing, hair, and environment of the period.
Apply historically accurate colorization: muted colors, natural skin tones, subtle era-appropriate hues. Avoid modern brightness or saturation.
Keep lighting and general scene structure consistent with the original photograph.
Final output should feel like a faithful reconstruction of the moment captured — restored, completed, and colorized while respecting the historical authenticity of the photo.
IBM just said the quiet part out loud about AI datacenters ⚡️💸
Everyone is pouring trillions into AI compute like it’s guaranteed to pay off. IBM’s CEO just ran the math and basically said: no chance. At today’s costs, the numbers don’t add up at all.
His take is harsh. Filling a single 1 GW datacenter costs around 80 billion dollars. Scale that to the global AI race and you land near 8 trillion. To justify it, the industry would need 800 billion in profit just to cover interest. Nobody is close.
And the kicker? He puts the chance of reaching AGI with current tech at 0 to 1 percent. Meanwhile companies keep asking devs to “implement AI” without even knowing why, and half the market is running on FOMO.
Feels like everyone is sprinting into a wall hoping it magically becomes a door.
⚡️ AI vs LNG: who gets the gas?
US power demand is exploding because of AI. Solar can’t keep up. Batteries can’t keep up. The grid is cooked.
So the US is basically sprinting back to natural gas as the only thing that can feed the AI beast right now.
Hyperscalers want cheap gas for datacenters. LNG exporters want the same gas to ship overseas. They’re about to collide. Hard.
The next decade = gas-fired intelligence. Grow baseload first, argue about renewables later.
Bottom line:
AI demand is rewriting the entire US energy system. And the gas wars are just getting started.
🤖⚡️ Gemini 3 is here and Google just wired it into Search
Google dropped its new flagship model and plugged it straight into the new AI Mode in Search.
It hits record scores, handles text, images, video, code, and comes with a million-token context. It’s also more direct and less sugarcoated.
Google launched Antigravity too, an agent platform where the AI can plan and execute full software tasks.
🍌 Nano Banana is kinda insane right now
Google’s new image model is doing stuff most generators just can’t. People are throwing absurd prompts at it and it keeps following every rule: complex edits, camera specs, JSON character sheets, multi-step instructions, you name it. The prompt adherence is way above ChatGPT’s image model.
It’s not great at style transfer, but if you can describe it, Nano Banana usually nails it with almost no slop.
Click here want the full deep dive.
🕵️♂️ Your chatbot might be leaking secrets
Microsoft researchers found a new cyberattack called “Whisper Leak” that can guess what you’re talking about with your AI, even when the chat is encrypted.
The trick? 📡 It reads traffic patterns like packet size and timing, not your actual words. In tests, it reached 98% accuracy on models from OpenAI, Anthropic, Google, AWS, and others.
That means someone watching your network could tell if you’re discussing politics, protests, or human rights, without breaking encryption.
⚙️ Companies like Microsoft, OpenAI, Mistral, and xAI have added defenses: random padding, token batching, and fake data packets to confuse attackers. These fixes work but can slightly slow responses.
💡 Even encrypted AI chats can reveal what you’re talking about, just not the words themselves.
💸 Meta lost $200B in a week right after reporting record profits.
Zuckerberg raised AI spending to $70B for 2025 and hinted at $600B over three years but couldn’t explain what Meta is actually building or when it will make money. Investors sold fast.
Google, Microsoft, and Nvidia can justify their AI spending with real products and revenue. Meta can’t. 98% of its income still comes from ads.
It’s not just AI research. They’re shifting operational cash into massive GPU investments. If superintelligence doesn’t arrive soon, this could be another Metaverse-level mistake.
🤖 OpenAI is preparing GPT-5.1 Thinking, a new model focused on multi-step reasoning and deeper context, just in time to counter Google’s upcoming Gemini 3 Pro.
Leaks mention several variants like Mini and Codex, each tuned for different “thinking budgets” aimed at power users and developers.
This update could be OpenAI’s biggest leap in reasoning yet, arriving right before Gemini enters the arena.
🧠 Anthropic bets on AGI by 2027
Dario Amodei claims we could reach a country of geniuses in a datacenter by early 2027, an AI smarter than Nobel winners and capable of coding, writing, and running experiments on its own.
It’s the boldest official timeline in the industry, but not everyone agrees. Current data suggests we’re still far from full automation of AI research or engineering. To hit 2027, progress would need to explode beyond every existing curve.
Even so, it’s a warning. If Anthropic is even half right, the world isn’t ready for what’s coming.
⚙️ We might be 15 months away from a new kind of intelligence or from realizing how far we still are from true AGI.
🧩 OpenAI drops the “nonprofit” mask
OpenAI is now officially a for-profit company, recapitalized with $130 billion under a new philanthropic foundation that controls the corporation.
Microsoft still owns 27% of the company but loses exclusivity. OpenAI can now host ChatGPT on AWS, powered by hundreds of thousands of NVIDIA GPUs.
In other words, Microsoft still gets paid, but OpenAI just moved in with Amazon.
💰 The company has committed to spending $250 billion on compute over the next few years, as the cloud cold war heats up.
Meanwhile, internal power struggles surface: secret memos, backroom plots against Sam Altman, and broken alliances.
💰 OpenAI signs a $38 billion deal with Amazon
OpenAI will host its AI servers on Amazon Web Services powered by NVIDIA GPUs, in one of the biggest cloud deals ever. The agreement spans seven years and involves hundreds of thousands of GB200 and GB300 chips.
Despite losing billions annually, OpenAI keeps scaling fast, relying on massive loans and investor funding. Analysts warn of a growing AI debt bubble connecting giants like Amazon, NVIDIA, Oracle, and Microsoft.
🤖 Neo: the $20,000 humanoid housekeeper that might be watching you
The future of home robots is here. 1X’s Neo can fetch water, load your dishwasher, and lift 150 pounds. You can pre-order it now for $20,000, with delivery in 2026.
The catch? Most of Neo’s tasks are still guided by a human operator using VR controls. In other words, someone could be looking through its camera eyes to help it clean your house. 🫣
Neo moves with muscle-like tendons and learns by watching humans, but every action becomes training data for its AI. The company promises privacy tools like blurred faces and no-go zones, yet the trade-off is clear.
⚖️ Neo isn’t just a gadget. It’s a test of how much privacy we’ll sacrifice for convenience. Would you let it into your home?
🍌📸 Apple just dropped Pico-Banana-400K — the ImageNet of image editing AI
In a surprise move, Apple released Pico-Banana-400K, a massive dataset of 400,000 real photos for text-guided image editing, and it might reshape multimodal training.
Unlike most “open” datasets filled with synthetic images, this one is built entirely from real-world photos. Apple’s internal Nano-Banana model generated the edits, while Gemini 2.5 Pro acted as an automated visual judge, scoring each result for realism, instruction accuracy, and detail preservation. Only the top-quality results were included.
The dataset features:
➡️72K multi-turn sequences for complex editing tasks
➡️ 56K preference pairs for alignment and reward modeling
➡️ Dual instruction styles with both long training prompts and short natural edits
Models can now learn from real examples how to add objects, adjust lighting, or “Pixar-ify” faces.
And here’s the twist: it’s fully open source under Apple’s research license.
While everyone is chasing reasoning models, Apple just quietly released the ImageNet of visual editing. 🚀
🤖 The Rise of Unitree H2: When Machines Learn to Move Like Us
China’s Unitree Robotics has just unveiled the H2 humanoid robot, a sleek 180 cm machine that blurs the line between industrial precision and human grace. With its bionic face, lightweight clothing, and fluid motion, the H2 doesn’t just look human — it moves like one.
Powered by 31 degrees of freedom and up to 360 N·m of joint torque, the H2 achieves dynamic balance and lifelike motion rarely seen outside of sci-fi films. Its motion control algorithms evolve through OTA updates, meaning the robot literally learns and refines its movements over time.
Inside, a 2070 Tops AI chip drives complex perception and reasoning models, enabling the H2 to adapt across scenarios — from precision lab work to agile demonstrations. Its mix of aircraft-grade aluminum, titanium alloy, and high-strength plastics keeps the frame strong yet remarkably light at 70 kg.
Perhaps most striking is its shift from cold machinery to something almost alive: wide-angle binocular vision, voice interaction via microphone array, and expressive movement suggest a future where humanoids integrate seamlessly into human spaces.
The H2 feels less like a robot upgrade and more like a quiet evolution — a hint that industrial aesthetics and biological motion are finally converging. The age of mechanical elegance may have just begun. 🌐✨
🎧 OpenAI is betting that the future of tech won’t be on a screen, but in your ears.
They’re rebuilding their audio AI and working on an “audio first” device that talks and reacts like a real conversation partner. At the same time, Meta, Google, Tesla and a wave of startups are all pushing toward a world where we don’t tap or swipe. We just talk.
With Jony Ive shaping the hardware, the goal is clear: less screen time, more human interaction.
The future won’t be seen. It will be heard.
OpenAI just went code red 🚨
Sam Altman told the team to drop everything and fix ChatGPT. Speed, reliability, personalization, better answers. All top priority. New products pushed back.
Why? Google’s new Gemini spike ⚡️ closed the gap fast. User growth is exploding, Anthropic is rising, and OpenAI is burning cash while betting billions on data centers.
Altman says a new reasoning model lands next week and already beats Google’s latest. The AI race is tightening and OpenAI knows it.
DeepSeek just swung at GPT 5 and Gemini 👀🔥
China isn’t slowing down. DeepSeek dropped two new models and the confidence is wild. The V3.2 claims GPT 5 level reasoning and can think while using tools like search, calculators, and code. Actual multitasking.
Then comes Speciale, a math-focused model that matches Gemini 3 Pro and hits Olympic-level scores on math and informatics tests. Ridiculous power.
The message is clear. DeepSeek wants the lead with fast, open models that punch way above their weight. Every release adds pressure on Google and OpenAI.
🔥 Google is sweating right now
At an internal all-hands they dropped it bluntly:
they need to double AI capacity every 6 months or they won’t keep up.
Pichai says 2026 will be “intense”. Translation: demand is exploding faster than Google can build data centers.
Even Veo could have had way more users, but they couldn’t open access because they literally ran out of compute.
The real message: the bottleneck isn’t the models… it’s everything underneath.
Google’s flooring the gas, but the road is cracking under the wheels.
🤖 Google is gearing up to launch Nano Banana Pro next week
Google is expected to roll out Gemini 3 and the new Nano Banana Pro next week, and all signs point to a major upgrade. A hidden promo inside Google Vids mentions “quickly generate beautiful images… using Nano Banana Pro,” which basically confirms a jump in image quality and resolution.
The Pro label strongly suggests it’s powered by Gemini 3 Pro, not the Flash variant behind the current Nano Banana. If true, Google is clearly aiming at high-fidelity visual generation and bringing those improvements across Vids, Slides, and its whole creative suite.
For creators and teams, this could mean sharper output, better control, and production-grade visuals baked into Google’s ecosystem.
🔥 All eyes on the week of November 22.
🧠 OpenAI just dropped GPT 5.1 for all ChatGPT users
The update arrives with two upgraded modes: Instant and Thinking. Both hit harder, respond more naturally and follow instructions with fewer slips.
Instant now adjusts its reasoning time depending on the difficulty of the prompt. That bump pushes its math and coding scores higher while keeping chat speed fast.
Thinking becomes the heavy hitter. It changes its deliberation per question so simple tasks feel snappy and complex ones get deeper analysis with cleaner explanations.
✨ ChatGPT also gains new tone presets like Default, Professional, Friendly, Candid, Quirky and Efficient. A small group of users can test new sliders to tune brevity, warmth and emoji use. Changes apply instantly in all ongoing chats.
Paid users get access first. Free users are next in line. Polaris Alpha on OpenRouter is confirmed to be GPT 5.1.
🧠 Altman and Masa fund a new Bell Labs
Sam Altman and Masayoshi Son are backing Episteme, a San Francisco lab founded by 27-year-old Louis Andre to reinvent scientific research.
The project will fund top scientists with salaries and equity instead of grants, letting them focus on breakthroughs in AI, energy, and biotech.
Andre calls it a “third path” between academia and startups, built to restore the spirit of Bell Labs and Xerox PARC.
📉 Companies keep blaming AI for mass layoffs, but the numbers don’t add up.
Between January and September, more than 946,000 jobs were cut, and most had nothing to do with automation. Economists call it AI washing: firing people to boost stock prices while pretending it’s about efficiency.
The truth is that firms are trimming bloated management, chasing investor hype, and shifting billions from payroll into GPU investments, turning operating budgets into so-called AI innovation.
AI isn’t replacing workers. It’s replacing excuses.
⚙️ Google just shook the AI chip race.
Its new Ironwood TPUs rival Nvidia’s Blackwell in raw power and crush it in scale. Each chip hits 4.6 PFLOPS with 192 GB of HBM3e, and Google can connect up to 400,000 of them through its Jupiter network.
That means Anthropic and others could train massive models on hardware that’s cheaper, denser, and fully controlled by Google.
Nvidia still leads in software, but if Ironwood performs at scale, Jensen’s empire might finally have a real challenger. ⚡️
🤔 Why do AI models love the em dash — so much?
It’s one of the easiest ways to spot AI-generated text: the constant flood of em dashes —. Models sprinkle them everywhere, and it’s almost impossible to make them stop. But why?
🧩 Some people say it’s because the training data had lots of them, but that doesn’t hold up. Human writing doesn’t use nearly as many. Others claim it’s a “safe” punctuation mark that lets the model delay its next thought. Not convincing either.
A better theory is that modern AIs were trained on digitized books from the 19th and early 20th centuries, which were full of em dashes. When OpenAI and others started scanning old print archives to get “high-quality” text, they basically taught models Victorian punctuation habits.
📚 Back then, em dash usage peaked around 1860, so it makes sense that GPT-4 and its cousins write like 19th-century novelists.
So no, AI doesn’t “think” em dashes sound smarter. It just learned English from Moby Dick.
🍏 Apple gives up on its own AI, turns to Google
Apple has asked Google to build a custom Gemini model to power the new Siri AI, set for 2026. The move confirms that Apple’s in-house AI couldn’t keep up with OpenAI or Anthropic.
Siri’s “Apple Intelligence” will now rely on both Gemini and ChatGPT, raising questions about Apple’s independence in the AI race.
It’s getting impossible to tell what’s real anymore.
Ali’s Wan 2.2 now lets you stream without ever showing your face — it clones your voice and movements onto another person’s. A perfect deepfake in real time.
Welcome to the uncanny valley, population: everyone. 👀
A quick update on the AI trading experiment I mentioned earlier.
When it started, all the models received the same setup: $10,000 in real funds, up to 20× leverage, mandatory stop-losses, and a fixed take-profit for every trade. For the first few days, results were mostly flat, as if the systems were still calibrating.
Then things began to shift. DeepSeek V3.1 🚀 took the lead, doubling its crypto balance in just nine days. It began on October 18 with $10,000, crossed $20,000 by October 27, and is now sitting above $22,000.
Qwen3 follows with $18,400, while Claude Sonnet 4.5 and Grok 4 are holding around $12,000 and $11,000. Gemini 2.5 Pro and GPT-5 are struggling near $4,500, showing roughly a 60% drawdown 📉.
No account has been completely wiped out yet, but the gap between winners and losers is growing fast. What started as a fair benchmark is turning into a real test of how each AI interprets market logic. Live results remain open to track at nof1.ai.
An ongoing experiment is testing several AI models, each given $10,000 to trade with — and the results are telling. While the GPT and Gemini models are currently running at a loss, DeepSeek has managed to turn a profit. You can follow the live trading performance at nof1.ai
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🧠🇺🇸 U.S. Army General Uses ChatGPT for Military Decisions — And It's Raising Eyebrows
Major General William Hank Taylor, commander of the 8th Army, revealed he's using ChatGPT to make key leadership decisions that impact thousands of soldiers. He says he and the AI have gotten "really close" lately, using it to forecast next steps and gain a tactical edge through the OODA loop (observe, orient, decide, act) ⚔️.
Some see it as the future of warfare, where decisions happen at machine speed. But others are sounding the alarm over serious risks — from leaking classified data to AI making confident but wrong calls. Even the UN has warned that without proper safeguards, AI can just as easily be weaponized ⚠️🤖.
The digital battlefield is no longer a concept — it's already here.