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『#SHOWチャンネル# 生放送2時間SP 』ありがとうございました☺️ 楽しくて温かくて、心が癒されました〜🌸笑 お衣装はアンミカさんから『クリオネみたいでかわいい〜』とお褒めの言葉を頂きましたよ♡やった〜🥰笑 #まっしろワンピース# #Arobe# さん #まっしろ苺もおいしかったなあ〜♡#

Zwei Monate Sommerpause für Bundestag: Wie glaubwürdig ist Merz' Ruf nach mehr Arbeit noch? Merz fordert von den Bundesbürgern mehr zu arbeiten – gleichzeitig macht der Bundestag zwei Monate Sommerpause, obwohl es einen Reformstau bei Rente, Pflege und Haushalt gibt. Der Journalist Warweg hat den Regierungssprecher Kornelius gefragt, wie die Bundesregierung diesen offenkundigen Widerspruch erklären würde. Die Antwort: Die Abgeordneten arbeiten laut Kornelius weiter. Dann kann man die lange Pause doch lassen, wenn man eh arbeitet, oder?😏 @satellit_de!

Higgsfield 这团队太疯狂了，两周内把插件塞进了 Figma、Minecraft、Adobe 全家桶，现在又把 DaVinci Resolve 拿下了。 我看了下 DaVinci 这个版本，这几个功能太他妈牛逼了： 1 你拍了条 Vlog 觉得色调一般，你只需要找到满意的色调截图扔进去。 它能帮你把整条时间线的色调匹配改好，你根本不需要懂调色，一张参考图搞定，以前这想都不敢想 2 Cinema Studio 这东西比较有意思，你可以指定相机型号、镜头焦段、光圈参数，它按这组物理参数重新渲染你的画面。手机拍的素材秒变相机质感 3 画面里有个路人你想去掉，直接在上面涂一笔告诉它删除，它改完了全片都保持一致。根本不用逐帧手动抠 4 去背景、一键变9:16竖屏、4K 超分这些就不细说了，该有的都有 对做短视频的、搞自媒体的，这东西相当于在你的剪辑软件里多了一个 AI 后期团队。门槛几乎为零。我今天准备拿自己的素材狠狠测一下。

FOR 40 YEARS YOU LAUNCHED APPS. Click. Type. Wait. NVIDIA and Microsoft just ended that era. RTX Spark is a 1-petaflop superchip built into a laptop thin enough to be 14mm and light enough to be 3 pounds. Not a data center. Not a cloud subscription. A laptop that runs 120-billion-parameter models with 1 million token context locally, privately, and offline. Hermes Agent and OpenClaw are already building native Windows apps for it. Adobe is rearchitecting Photoshop and Premiere from the ground up for it. Jensen Huang called it the new PC. 128GB unified memory. Full Blackwell GPU. All-day battery. The PC just stopped being a tool. It became a teammate. Available this fall from ASUS, Dell, HP, Lenovo, Microsoft Surface, and MSI. Follow @neil_xbt for more AI and hardware intelligence.

I’ll only say it once. This might be the fastest way to hit $1 million by the end of 2026: $PATH (UiPath) → $13 Strong Buy $ONDS (Ondas) → $13 Strong Buy $PLTR (Palantir) → $160 Must buy $ADBE (Adobe) → $270 Must buy $NBIS (NEBIUS) → $264 Strong Buy $NVDA (NVIDIA) → $224 Strong Buy $MSFT (Microsoft) → $460 Must buy $LCID (Lucid Motors) → $6 Strong Buy $ARM (Arm Holdings) → $408 Must buy $META (Meta Platforms) → $600 Must buy $SOFI (SoFi Technologies) → $17 Strong Buy $MU (Micron Technologys) → $1035 Strong Buy Don’t miss out again… (BOOKMARK THIS FOR LATER) If you are not following us with notifications turned on, you might miss our next alerts.

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

.@Adobe Photoshop and Premiere — rebuilt from the ground up for NVIDIA RTX Spark. Up to 2x faster across AI, editing, coloring and effects. Full GPU acceleration. AI-native pipeline. Coming soon.

NVIDIA says app compatibility won’t be a concern on its ARM-based RTX Spark PCs. At Computex, Jensen Huang said these PCs can run “every application Windows has ever run.” Windows on Arm has traditionally relied on emulating x86 apps, which has occasionally led to compatibility issues with older software, drivers, anti-cheat systems and some games. So this could be HUGE. NVIDIA also says it’s working with developers to ensure anti-cheat support for games like Fortnite, Valorant, PUBG and more. That’s a big step forward for Windows on Arm gaming. There will also be Spark exclusive optimizations. Adobe is rebuilding Photoshop and Premiere for RTX Spark, with up to 2x better performance.