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America just had its AI Sputnik moment. Kimi K3 dropped a bombshell: 2.8 trillion parameters, the largest open-weight model ever built headed. straight to number one. Possible built on chips two generations behind NVIDIA's best, under US export controls designed to make exactly this impossible -- K3 is #1# in front-end code and six other domains: marketing, design, data analytics, consumer products, simulations, and content creation. -- The model designed its own chip for its next generation and wrote its own inference kernels. Recursive -- 70% of elite AI researchers are not US citizens. The founder of Moonshot AI earned his PhD at Carnegie Mellon and started a Chinese AI startup one year into his US program. -- Bonsai 27B: a GPT-5 class model now runs entirely on a smartphone at 6 gigabytes.

Kimi's CEO🇨🇳 says every AI lab has the wrong obsession Zhilin Yang, on why K3 beat the frontier: "Every lab, like Claude, thinks the model matters most. That's wrong. It's how you organize the people building it that wins." Then Moonshot proved the philosophy. K3 sold out every plan on purpose, cutting their own revenue instead of throttling existing users. When Anthropic hit that wall in April, they cut users' usage 50% at peak. His one big idea: long context is the AI era's RAM. The 128K-to-gigabytes jump, compressed into 2 years instead of 40. The real moat: "your biggest advantage, perhaps your only advantage, is your organization." Model, or the team behind it: which actually wins?

MidJourney just announced... a full body ultrasound! Yup... read on because it's as crazy as it sounds. "As powerful as MRI and as casual as a trip to the spa" They are calling it "the @midjourney scanner" Insane details: - First, the scale. The device uses 8,960 individual transducers arranged in a ring around your body - The precision is the most jaw-dropping part: it resolves motion at the picometer range. It can image internal tissues finer than the width of an atom. We are talking sub-atomic level diagnostic capability - The compute requirement is massive. The system processes 17 gigabytes of data per second. It takes 40GB of raw data to reconstruct just one cross-sectional slice. And they are planning to scan 100 slices? - Midjourney claims that fewer than 12 of these machines could perform more full-body scans than every MRI machine on Earth combined. Welcome to the future of healthcare! Not only these scanners are announced, they will exist in a "Midjourney SPA" - with hot tubs, saunas, cold plunges, and 9-10 whole body scanners.

You paid $1,000 for a Samsung phone. Samsung pre-installed dozens of apps you never asked for. Facebook. Microsoft Office. Netflix. Samsung's own browser. Samsung's own email. Samsung's own calendar. Samsung's own notes. Samsung's own cloud. Samsung's own payment app. Samsung's own voice assistant. Duplicates of apps you already use. Running in the background. Eating your battery. Using your storage. You cannot delete them. The delete button is greyed out. You paid for the hardware. They control the software. Xiaomi is worse. Pre-installed games. Shopping apps. Some serve full-screen ads on your phone. Carriers add more. T-Mobile, Verizon, AT&T each pre-install apps you will never open. Uninstall button? Greyed out. Someone built a tool that removes every single one. No root needed. It's called Universal Android Debloater. Written in Rust. Cross-platform GUI. Plug your phone in. Click. Gone. → Remove any pre-installed app. Samsung, Xiaomi, OnePlus, Oppo, Vivo, Huawei, Motorola, Nokia. → Remove carrier bloatware. T-Mobile, Verizon, AT&T, and carriers worldwide. → No root required. → Restore anything you removed. One click. → Battery life improves immediately. → Storage freed. Gigabytes recovered. → Privacy improved. Fewer apps tracking you. → Community-maintained database of known bloatware. → Works on Windows, macOS, and Linux. Here's the wildest part: The phone you paid for is not yours. It is a billboard you carry in your pocket. The manufacturer sold you hardware and then sold your attention to their partners. This tool gives you YOUR phone back. GPL-3.0. Written in Rust. Community-maintained. But DO NOT use Universal Android Debloater. We should all keep running dozens of apps we never installed on the phone we already paid for.

An absolute legendary moment at #COMPUTEX2026#! @nvidia's CEO Jensen Huang stopped by our booth to celebrate #GIGABYTE#'s 40th anniversary milestone, marking four decades of innovation as we head into a future of infinite possibilities. ✨ #ENTERINFINITY#

OpenAI just ordered 100,000 Blackwell GPUs from NVIDIA. A Chinese developer put one of NVIDIA's $3,999 desktop AI boxes on his office desk and ran the same robot simulator the big labs train on $50,000 racks. The demo was a single empty cube floating in an empty world. Fifty frames per second. He posted the clip with one line. Is this how home robotics starts or is this an expensive toy. The clip went viral the same week NVIDIA shipped the second batch of Sparks to developers. 1.8 million views in 72 hours. Every American hardware engineer shared it as proof you could finally own the rig. Every Chinese commenter left the same Mandarin reply: pause at 1:42. Pause at 1:42. Ignore the empty cube on the screen. Ignore the FPS counter. Look at the memory readout in the top bar. 2.4 GiB used. 87.4 GiB available. The cube is sitting in three percent of the memory. The empty 84 gigabytes of memory was not headroom for a future robot scene. The empty 84 gigabytes was already running. ColdMath. $138,168 profit. Joined November 2025. Bio: Edge Compounds. He had not bought the Spark to train robots. He had bought it because robots were the only workload NVIDIA shipped a 128 gigabyte chip for. The slow memory that ruined the box for real robotics was perfect for what he was actually running. Twelve hundred ensemble weather simulations in parallel. Robot training needs fast memory because every frame is a step in a training loop. Weather ensembles need huge memory because every city is a parallel simulation that does not talk to the others. The Spark's chip is six times slower than a gaming card. It is also five times larger. The trade off only matters if you know what you are running. He knew. Wellington 16C on March 28. Tokyo 16C on March 20. Every city in the wallet was a city the ensemble had simulated three hours before the public forecast posted. Comments turned into a detective board. Someone slowed the clip to 0.25x. Someone else compared the wallet's trade timestamps to the timestamps the public forecast services updated their data. Every trade landed during the three hour gap. The Spark had been catching it. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. The developer in the office cubicle had been one of them. He had wired the agent into the Spark the same week NVIDIA shipped his box. The empty cube was not a benchmark. The empty cube was a screen saver running while the agent occupied the other 84 gigabytes. The Isaac Sim install was not the project. The Isaac Sim install was the proof he could justify buying the box on company expenses. The question about whether this was a real tool or an expensive toy was the only thing in the video designed to be answered by the audience. He was not a Chinese developer testing whether home robotics had arrived. He was the first developer to figure out that the box NVIDIA had marketed for the wrong workload was the cheapest weather simulator on the market. The clip is at 1.8 million views. The forum thread is still arguing about the six times memory penalty. The Spark on his desk is still running. The wallet is still hitting cities the public forecast services have not updated yet. The cube is still floating in three percent of the memory. The country with the better robotics demo has the smaller wallet. The dev with the wrong tool for the job has the bigger one. He just had to install a robot simulator for one afternoon.

$NVDA Vera CPU partner list from GTC Taipei. Early Adopters: OpenAI, Anthropic, SpaceXAI Cloud: $NBIS $ORCL $CRWV Manufacturing: $DELL $HPE $SMCI $SNX Taiwan ODMs: Foxconn, Quanta, Wiwynn, Wistron, Gigabyte, ASUS, Compal, Pegatron, Inventec, MSI

Riot Games has released a new update for Valorant’s Vanguard anti-cheat system aimed at stopping advanced DMA (Direct Memory Access) cheating hardware. Vanguard anticheat now damages the firmware of these devices upon detection. It can be repaired, but bricking a person's hardware without their consent or knowledge is highly illegal The update targets certain SATA and NVMe firmware setups commonly used in expensive DMA cheating devices. These cheats rely on external hardware to read game memory while bypassing traditional anti-cheat detection methods. Some users claim Vanguard now triggers IOMMU-related warnings that can temporarily block affected hardware until system settings or firmware are updated. These DMA cheats have become popular in competitive games because they bypass many kernel-level anti-cheat systems. Riot has worked with motherboard manufacturers including ASUS, MSI, Gigabyte, and ASRock to address firmware vulnerabilities related to DMA access.