🤖 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.

🤖⚡️ 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.

🔥 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.

⚡️ 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.

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.

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.

@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.

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.

💻🧯 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.

It sounds like a joke, but Google Research claims a prompting trick that actually works: paste your prompt twice, exactly the same, before sending it 🤖📋

In a recent preprint, they show that turning <QUERY> into <QUERY><QUERY> improves results when the model is not asked to reason. Across many benchmarks and models, repetition wins most comparisons and never clearly loses.

They also report that outputs don’t get longer and latency usually stays the same, since the extra cost is in reading the prompt, not generating the answer. Important caveat: once you ask the model to “think step by step,” the effect is mostly neutral. And padding the prompt with junk doesn’t work, so it’s not just about more context.

If you try it, repeat the entire prompt, not just the question. Just keep in mind you’re doubling input tokens, so you pay more and use up context faster.

ChatGPT is still the nº1 AI site, but its lead is shrinking fast. New Similarweb numbers show ChatGPT dropping to 64.6% global traffic share while Google’s Gemini jumped to 22%, and that’s the first time since 2023 ChatGPT has been under 65%.

The story isn’t "ChatGPT is falling apart", it’s Google finally turned distribution into growth. Gemini is everywhere in Google’s ecosystem, and that kind of default placement is brutally effective. If this trend holds, the market won’t be one king anymore, it’ll be two giants and a long tail fighting for scraps.

Google just released Genie, a real time world simulator you can actually walk through 🌍 Text or images turn into explorable worlds with object permanence and environments that keep expanding instead of collapsing.

This isn’t a game engine or a video model. It’s a foundation model for worlds 🧠 Today you move and look around. Tomorrow, those worlds respond.

Learn more: https://labs.google/projectgenie

Someone just built a Claude skill that clones any website with a single prompt 🤯
It reverse engineers the design, extracts assets, and rebuilds the full layout automatically. It's also open-source.

GitHub:
https://github.com/JCodesMore/ai-website-cloner-template

China is pushing AI into classrooms as a major nationwide experiment, aiming to use the tech for things like grading, lesson design, supporting rural educators, and monitoring student progress.

Local schools and counties are now free to choose commercial AI systems and integrate them into daily education for four key areas: assisting teachers, streaming better lessons to remote areas, tracking how students do, and keeping an eye on student behavior or mental health.

Overall, this rollout isn’t heavily funded—Beijing sets the direction and goals, tech companies provide solutions, and it’s up to local officials to innovate with their existing resources.

Morphic introduces 72 unique workflows to their platform, making creativity more accessible for everyone.

Ever had a great idea but got stuck on how to prompt for it? These new workflows are designed to solve that—no more creative roadblocks. Now, tasks like storyboarding, creating UGC ads, or applying a color grade can be done in just a few clicks.

You can easily run workflows across filmmaking, social content, fashion shoots, marketing, animation, and more. Simply choose your assets and configure options—no complicated prompting required. It’s about making repeatable creative steps effortless to set up and reuse.

More workflows are on the way, so there’s always something new to discover. Give it a try and see what you can make! 🚀

OpenAI researchers have made significant progress by solving five more Erdős problems with their internal AI model. This achievement highlights how artificial intelligence is advancing in serious mathematical reasoning. 🔍

Personally, I found the solution to Erdős Problem 1091 especially fascinating. The question considers whether a graph with chromatic number 4, but all its smaller subgraphs having chromatic number at most 3, must contain an odd cycle rich in diagonals. OpenAI's model delivered a clear counterexample, shedding light on the issue.

For those interested, it’s a great challenge to try and reconstruct the proof from Figure 5—it's an enjoyable exercise for math enthusiasts.