Apple's in-house Wi-Fi chip doesn't set records, but it's a reliable performer.

تم اختبار شريحة Wi-Fi N1 الجديدة من آبل وأظهرت تفوقها على شرائح Broadcom القديمة في جميع الجوانب. على الرغم من أنها لا تسجل أرقامًا قياسية جديدة، إلا أنها تُعتبر أداءً موثوقًا في قطاع التكنولوجيا.

El nuevo chip Wi-Fi N1 de Apple ha sido probado y ha demostrado superar a los antiguos chips de Broadcom en todos los aspectos. Aunque no establece nuevos récords, se reconoce como un rendimiento confiable en el sector tecnológico.

La nouvelle puce Wi-Fi N1 d'Apple a été testée et s'est révélée supérieure aux anciennes puces Broadcom dans tous les domaines. Bien qu'elle ne batte pas de records, elle est reconnue comme un performer fiable dans le secteur technologique.

Apple's new N1 Wi-Fi chip has been tested and shown to outperform older Broadcom chips in all aspects. While it may not set new records, it is recognized as a reliable performer in the technology sector.

Testing shows Apple N1 Wi-Fi chip improves on older Broadcom chips in every way

The story of the Ghost in the Shell’s main villain the Puppet Master hinted at a future where governments use hackers for espionage, at a time when most of the world had never connected to the internet.

الأنمي الكلاسيكي 'Ghost in the Shell' يقدم شخصية Puppet Master، التي تتنبأ بمستقبل تستخدم فيه الحكومات القراصنة للتجسس. ظهرت هذه التنبؤات في وقت كانت فيه غالبية سكان العالم لم تتصل بعد بالإنترنت، مما يبرز رؤية العرض لمشكلات الأمن السيبراني.

El clásico anime 'Ghost in the Shell' presenta al Puppet Master, un personaje que anticipa un futuro en el que los gobiernos utilizan hackers para el espionaje. Esta predicción surgió en un momento en que la mayoría de la población mundial aún no estaba conectada a Internet, destacando la previsión del programa sobre los problemas de ciberseguridad.

L'anime classique 'Ghost in the Shell' présente le Puppet Master, un personnage qui préfigure un avenir où les gouvernements utilisent des hackers pour l'espionnage. Cette prédiction est survenue à une époque où la majorité de la population mondiale n'était pas encore connectée à Internet, soulignant la prévoyance de l'émission concernant les problèmes de cybersécurité.

The classic anime 'Ghost in the Shell' features the Puppet Master, a character that foreshadows a future where governments utilize hackers for espionage. This prediction emerged at a time when the majority of the global population had yet to connect to the internet, highlighting the show's foresight regarding cybersecurity issues.

How the classic anime ‘Ghost in the Shell’ predicted the future of cybersecurity 30 years ago

<p>Text-to-image diffusion models have become the workhorses of generative imaging. They can paint photorealistic scenes, mimic art styles, and blend concepts in ways that were science fiction a few years ago. Yet they stumble embarrassingly on a skill that even small children master: basic spatial reasoning.</p>

<p>Ask a state-of-the-art model for “a dog to the right of a teddy bear” and you often get:</p>

<ul>
<li>The dog on the left</li>
<li>One of the objects missing</li>
<li>Or a bizarre hybrid where dog and teddy are fused into a single creature</li>
</ul>

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F49rtb08366xdl284o4z0.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F49rtb08366xdl284o4z0.jpg" alt=" " width="800" height="532"></a></p>

<p>These failures become more severe for unusual compositions like “a giraffe above an airplane”. Traditional fixes range from expensive fine-tuning to brittle, hand-written loss functions at inference time—but both options come with significant downsides.</p>

<p>NVIDIA’s Learn-to-Steer framework (accepted to WACV 2026) proposes a different path: instead of hard-coding spatial rules or retraining the entire model, it learns a data-driven objective that can “steer” diffusion at inference time. The method reads the model’s own cross-attention maps, trains a lightweight classifier to detect spatial relations, and then uses that classifier’s gradient as a learned loss to nudge the generation towards layouts that match the prompt.</p>

<p>In this blog, we’ll unpack:</p>

<ul>
<li>What makes spatial reasoning so fragile in current diffusion models</li>
<li>How Learn-to-Steer learns spatial constraints from the model itself</li>
<li>How it steers images during generation without changing model weights</li>
<li>The top gains on spatial benchmarks like GenEval and T2I-CompBench</li>
<li>The trade-offs in compute cost and generality, and what this implies for future generative systems</li>
</ul>

<h1>
  
  
  Why Spatial Reasoning Fails in Text-to-Image Diffusion
</h1>

<h2>
  
  
  What Makes Spatial Relations So Difficult for Diffusion Models?
</h2>

<p>Modern diffusion models (e.g., Stable Diffusion, Flux) are excellent at what should appear in an image—objects, styles, textures—but much less reliable at where those objects should be.</p>

<p>Several factors contribute:</p>

<h3>
  
  
  Weak supervision of spatial language
</h3>

<ul>
<li>Training data rarely comes with precise annotations like “object A is left of object B”.
</li>
<li>Captions often describe content loosely, so phrases like “on top of” or “to the right of” are under-specified.</li>
</ul>

<h3>
  
  
  Entangled visual concepts
</h3>

<ul>
<li>When two objects frequently co-occur, models may treat them as a single visual blob.</li>
<li>This leads to object fusion, where a “cat on a bookshelf” becomes a cat-bookshelf chimera.</li>
</ul>

<h3>
  
  
  Benchmark saturation without spatial coverage
</h3>

<ul>
<li>Many standard text-to-image benchmarks emphasize realism and style, not relational accuracy.</li>
<li>Models can score highly while still being spatially confused.</li>
</ul>

<p>Empirical studies confirm three recurring failure modes on spatial benchmarks:</p>

<ul>
<li>Incorrect placement: Objects appear in the wrong relative position.</li>
<li>Missing entities: One or more requested objects never appear.</li>
<li>Merged entities: Two objects get mashed into a single, incoherent form.</li>
</ul>

<p>The model “knows” the objects you asked for, but it doesn’t reliably understand where to place them.</p>

<h1>
  
  
  Why Fine-Tuning and Handcrafted Losses Are Not Enough
</h1>

<p>Two broad strategies have tried to patch this gap:</p>

<h2>
  
  
  Fine-tuning for spatial awareness
</h2>

<ul>
<li>Retrain the diffusion model on datasets with explicit layouts or spatial annotations.</li>
<li>Methods like COMPASS show that this can significantly improve spatial accuracy.</li>
<li>But this comes at a cost: expensive retraining, sensitivity to dataset bias, and often regressions in other capabilities such as color fidelity or counting.</li>
</ul>

<h2>
  
  
  Handcrafted test-time losses
</h2>

<ul>
<li>At inference, inject extra loss terms that penalize spatial errors (e.g., overlapping activation maps, incorrect ordering).</li>
<li>These losses must be manually designed to approximate relations like “left of” or “above”.</li>
<li>In practice, these heuristics are fragile, often over-fitting simple cases and failing on more complex layouts.</li>
</ul>

<p>In short, we’ve lacked a solution that is:</p>

<ul>
<li>Data-driven rather than rule-based</li>
<li>Plug-and-play at inference time (no full retraining)</li>
<li>Targeted enough to improve spatial reasoning without damaging other strengths</li>
</ul>

<p>This is where Learn-to-Steer enters.</p>

<h1>
  
  
  How Learn-to-Steer Works: Data-Driven Steering at Inference
</h1>

<h2>
  
  
  How Cross-Attention Maps Provide a Spatial Signal
</h2>

<p>During diffusion, at each denoising step, the model computes cross-attention maps that connect text tokens to image regions. For a prompt like “a dog to the right of a teddy bear”, you can think of:</p>

<ul>
<li>One set of attention maps for “dog”</li>
<li>Another set for “teddy bear”</li>
<li>Additional context around words like “right” or “of”</li>
</ul>

<p>These maps form a rich, high-dimensional signal describing where in the image the model currently believes each word should manifest. Prior work has used cross-attention to locate objects or edit images; Learn-to-Steer goes further by treating them as a feature space in which spatial relations can be learned.</p>

<h2>
  
  
  How a Relation Classifier Becomes a Learned Loss
</h2>

<p>The core idea of Learn-to-Steer is to train a small relation classifier that takes cross-attention maps for two objects and predicts the spatial relation between them (left-of, right-of, above, below, etc.).</p>

<p>The pipeline looks like this:</p>

<h3>
  
  
  Collect supervision
</h3>

<ul>
<li>Use images where the true relation between object A and object B is known (from datasets like GQA and synthetic layouts).</li>
<li>For each image, invert it through the diffusion model with a descriptive prompt to recover cross-attention maps for the relevant tokens.</li>
</ul>

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fi9dbjsdc4c8yjz2r88k4.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fi9dbjsdc4c8yjz2r88k4.jpg" alt=" " width="800" height="446"></a></p>

<h3>
  
  
  Train a classifier on attention patterns
</h3>

<ul>
<li>Input: attention maps for object A and object B.</li>
<li>Output: predicted relation (e.g., “A is left of B”).</li>
</ul>

<p>Naively, however, this leads to a subtle but serious issue: relation leakage.</p>

<h2>
  
  
  How Dual Inversion Solves the “Relation Leakage” Problem
</h2>

<p>If you always invert images with a correct prompt (e.g., “a dog to the left of a cat”), hints about the word “left” can leak into the attention patterns. A naïve classifier might then “cheat” by reading out linguistic artefacts instead of learning genuine visual geometry.</p>

<p>To prevent this, Learn-to-Steer uses a dual inversion strategy:</p>

<ul>
<li>For each image with a true relation (say, dog left of cat), create two prompts:

<ul>
<li>A positive prompt with the correct relation (“dog to the left of a cat”).</li>
<li>A negative prompt with an incorrect relation (“dog above a cat”).</li>
</ul>


</li>

<li>Run inversion with both prompts, obtaining two sets of attention maps.</li>

<li>Label both sets with the true relation (left-of), because that is what the image actually depicts.</li>

</ul>

<p>The classifier sees pairs of attention maps that share the same underlying geometry but differ in the relation words used in the prompt. To succeed, it must ignore the unreliable linguistic cue and zero in on the geometric evidence in the attention patterns. This breaks the leakage shortcut and yields a classifier that actually understands “left-of” in terms of where things appear in the model’s internal vision.</p>

<p>To improve robustness, NVIDIA combines:</p>

<ul>
<li>Real images (complex, natural scenes)</li>
<li>Synthetic images (simpler, cleaner attention patterns akin to generation scenarios)</li>
</ul>

<h1>
  
  
  How Learn-to-Steer Guides Images During Generation
</h1>

<h2>
  
  
  Step-by-Step: From Prompt to Steered Latent
</h2>

<p>Once the relation classifier is trained, Learn-to-Steer uses it at inference time as a learned objective:</p>

<h3>
  
  
  Parse the spatial prompt
</h3>

<ul>
<li>Extract subject, relation, and object from the text (e.g., subject = dog, relation = right-of, object = teddy bear).</li>
</ul>

<h3>
  
  
  Run diffusion as usual—but with checkpoints
</h3>

<ul>
<li>As the model denoises latent noise into an image, periodically extract cross-attention maps for the subject and object tokens.</li>
</ul>

<h3>
  
  
  Evaluate spatial correctness
</h3>

<ul>
<li>Feed these maps into the relation classifier, which outputs a probability distribution over relations.</li>
<li>Compare this distribution to the desired relation from the prompt, and compute a loss (e.g., cross-entropy).</li>
</ul>

<h3>
  
  
  Backpropagate into the latent
</h3>

<ul>
<li>Compute the gradient of this loss with respect to the latent representation at that timestep.</li>
<li>Nudge the latent in the direction that increases the classifier’s confidence in the correct relation.</li>
</ul>

<h3>
  
  
  Continue the diffusion process
</h3>

<ul>
<li>Let the denoising proceed from the adjusted latent.</li>
<li>Repeat this steering a number of times (often during the earlier half of the diffusion steps).</li>
</ul>

<h2>
  
  
  Support for Multiple Architectures and Relations
</h2>

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F578a9bjc7gmtemh0jbsj.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F578a9bjc7gmtemh0jbsj.jpg" alt=" " width="800" height="477"></a></p>

<p>A key advantage of Learn-to-Steer is that it’s architecture-agnostic:</p>

<ul>
<li>It has been demonstrated on both UNet-based models (like Stable Diffusion 1.4/2.1) and MMDiT-style models (like Flux).</li>
<li>The only requirement is access to a text-image alignment signal (cross-attention or similar).</li>
</ul>

<p>It can also handle prompts with multiple constraints, such as:</p>

<p>“A frog above a sneaker below a teapot.”</p>

<p>Here, Learn-to-Steer alternates attention between relations:</p>

<ul>
<li>At one timestep, optimize the frog–sneaker relation.</li>
<li>At another, optimize the sneaker–teapot relation.</li>
</ul>

يهدف Learn-to-Steer من NVIDIA إلى معالجة قيد كبير في نماذج الانتشار من النص إلى الصورة، التي تعاني من ضعف في التفكير المكاني الأساسي. يمكن لهذه النماذج إنشاء صور فوتوغرافية واقعية، لكنها غالبًا ما تضع الأشياء في غير موضعها، مثل وضع كلب على اليسار بدلاً من اليمين بجانب دمية دب. تهدف هذه الخطوة إلى تحسين دقة الصور المولدة من خلال تعزيز الفهم المكاني.

El Learn-to-Steer de NVIDIA busca abordar una limitación significativa en los modelos de difusión de texto a imagen, que luchan con el razonamiento espacial básico. Estos modelos pueden crear imágenes fotorealistas, pero a menudo colocan mal los objetos en relación entre sí, como poner un perro a la izquierda de un oso de peluche en lugar de a la derecha. Este avance tiene como objetivo mejorar la precisión de las imágenes generadas al mejorar la comprensión espacial.

Le Learn-to-Steer de NVIDIA vise à résoudre une limitation importante des modèles de diffusion texte-image, qui ont du mal avec le raisonnement spatial de base. Ces modèles peuvent créer des images photoréalistes mais placent souvent mal les objets les uns par rapport aux autres, comme mettre un chien à gauche d'un ours en peluche au lieu de la droite. Cette avancée vise à améliorer l'exactitude des images générées en renforçant la compréhension spatiale.

NVIDIA's Learn-to-Steer is set to address a significant limitation in text-to-image diffusion models, which struggle with basic spatial reasoning. These models can create photorealistic images but often misplace objects in relation to one another, such as placing a dog to the left of a teddy bear instead of the right. This advancement aims to enhance the accuracy of generated images by improving spatial understanding.

What Is Learn-to-Steer? NVIDIA’s 2025 Spatial Fix for Text-to-Image Diffusion

<p>The $5tn firm handily beat expectations, but analysts are awaiting projections for future demand for firm’s AI chips</p><p>Nvidia shares are rising in after-market trading after the company posted third quarter earnings that beat Wall Street estimates.<strong> </strong>All eyes were on Nvidia, the bellwether for the AI industry and the most valuable publicly traded company in the world, as analysts and investors hoped the chipmaker’s third-quarter earnings would assuage concerns about whether the high-flying valuations of AI firms have peaked.</p><p>“Blackwell sales are off the charts, and cloud GPUs are sold out,” said Jensen Huang, founder and CEO of Nvidia in a press release. “Compute demand keeps accelerating and compounding across training and inference – each growing exponentially. We’ve entered the virtuous cycle of AI. The AI ecosystem is scaling fast – with more new foundation model makers, more AI startups, across more industries, and in more countries. AI is going everywhere, doing everything, all at once.”</p> <a href="https://www.theguardian.com/technology/2025/nov/19/nvidia-earning-report">Continue reading...</a>

تجاوزت شركة إنفيديا توقعات وول ستريت مع نتائجها للربع الثالث، مما أظهر طلبًا قويًا على شرائح الذكاء الاصطناعي الخاصة بها. ارتفعت أسهم الشركة في التداول بعد السوق، مما يعكس ثقة المستثمرين وسط مخاوف بشأن تقييم سوق الذكاء الاصطناعي. وأبرز الرئيس التنفيذي جينسن هوانغ مبيعات قياسية ونظامًا بيئيًا سريع التوسع في مجال الذكاء الاصطناعي، مما يشير إلى نظرة إيجابية لمستقبل الشركة.

Nvidia superó las expectativas de Wall Street con sus ganancias del tercer trimestre, mostrando una fuerte demanda por sus chips de IA. Las acciones de la compañía aumentaron en el comercio posterior al cierre, reflejando la confianza de los inversores en medio de preocupaciones sobre la valoración del mercado de IA. El CEO Jensen Huang destacó las ventas récord y un ecosistema de IA en rápida expansión, indicando una perspectiva positiva para el futuro de la empresa.

Nvidia a dépassé les attentes de Wall Street avec ses résultats du troisième trimestre, montrant une forte demande pour ses puces d'IA. Les actions de l'entreprise ont augmenté lors des échanges après la clôture, reflétant la confiance des investisseurs face aux préoccupations concernant la valorisation du marché de l'IA. Le PDG Jensen Huang a souligné des ventes record et un écosystème IA en pleine expansion, indiquant une perspective positive pour l'avenir de l'entreprise.

Nvidia exceeded Wall Street expectations with its third-quarter earnings, showcasing strong demand for its AI chips. The company's shares rose in after-market trading, reflecting investor confidence amid concerns about the AI market's valuation. CEO Jensen Huang highlighted record sales and a rapidly expanding AI ecosystem, indicating a positive outlook for the company's future.

‘AI is going everywhere, doing everything:’ Nvidia beats Wall Street estimates amid market selloff and AI bubble fears

The SanDisk ExtremeFit USB-C flash drive is barely three grams, but offers 1TB of external storage and impressive speeds.

I refused to believe this coin-sized gadget was a storage drive, until I tried it for myself

<p>Swift 6.3 is bringing significant enhancements to Embedded Swift, the subset of Swift designed for resource-constrained environments like microcontrollers. Here's what's new:</p>

<h2>
  
  
  Key Improvements
</h2>

<h3>
  
  
  Libraries &amp; Standard Library
</h3>

<ul>
<li>
<strong>Floating-point printing</strong>: The <code>description</code> and <code>debugDescription</code> properties now work for Float, Double, and other floating-point types with a new all-Swift implementation</li>
<li>
<strong>Better diagnostics</strong>: New <code>EmbeddedRestrictions</code> diagnostic group warns about unsupported language constructs</li>
<li>
<strong>Swift MMIO 0.1.x</strong>: Includes code generation from SVD files and improved debugging with SVD2LLDB plugin</li>
</ul>

<h3>
  
  
  C Interoperability
</h3>

<ul>
<li>
<strong><code>@c</code> attribute</strong>: Define C-compatible functions and enums (from SE-0495)
</li>
</ul>

<div class="highlight js-code-highlight">
<pre class="highlight swift"><code><span class="kd">@c</span><span class="p">(</span><span class="kt">MyLib_initialize</span><span class="p">)</span>
<span class="kd">public</span> <span class="kd">func</span> <span class="nf">initialize</span><span class="p">()</span> <span class="p">{</span> <span class="o">...</span> <span class="p">}</span>
</code></pre>

</div>



<ul>
<li>
<strong>Improved type matching</strong>: Better tolerance for mismatching C signatures, eliminating cryptic deserialization errors</li>
</ul>

<h3>
  
  
  Debugging
</h3>

<ul>
<li>
<strong>Enhanced LLDB support</strong>: Better value printing for Embedded Swift types</li>
<li>
<strong>Core dump inspection</strong>: Dictionary, Array, and other common types now inspectable without a live process</li>
<li>
<strong>ARMv7m exception unwinding</strong>: Complete backtraces through exception frames</li>
</ul>

<h3>
  
  
  Linking &amp; Compilation
</h3>

<ul>
<li>
<strong><code>@section</code> and <code>@used</code> attributes</strong>: Control where globals are emitted and ensure symbols aren't stripped (SE-0492)</li>
<li>
<strong>Weak symbol definitions</strong>: Fixes duplicate symbol errors in diamond dependencies</li>
<li>
<strong><code>@export</code> attribute</strong>: Better control over function visibility (SE-0497)</li>
</ul>




<p><em>Want to dive deeper? Read the <a href="https://www.swift.org/blog/embedded-swift-improvements-coming-in-swift-6.3/" rel="noopener noreferrer">full announcement</a> on Swift.org</em></p>

تقدم Swift 6.3 تحسينات كبيرة على Embedded Swift، مما يعزز وظيفته في البيئات ذات الموارد المحدودة مثل المتحكمات الدقيقة. تشمل التحسينات الرئيسية قدرات جديدة لطباعة الأعداد العشرية، وتشخيصات أفضل مع مجموعة EmbeddedRestrictions، وإدخال Swift MMIO 0.1.x لتوليد الشيفرة وتصحيح الأخطاء.

Swift 6.3 presenta mejoras significativas en Embedded Swift, aumentando su funcionalidad para entornos con recursos limitados como microcontroladores. Las mejoras clave incluyen nuevas capacidades de impresión de números de punto flotante, mejores diagnósticos con el grupo EmbeddedRestrictions y la introducción de Swift MMIO 0.1.x para la generación de código y la depuración.

Swift 6.3 apporte des améliorations significatives à Embedded Swift, renforçant sa fonctionnalité pour les environnements à ressources limitées comme les microcontrôleurs. Les principales améliorations comprennent de nouvelles capacités d'impression de nombres à virgule flottante, de meilleurs diagnostics avec le groupe EmbeddedRestrictions, et l'introduction de Swift MMIO 0.1.x pour la génération de code et le débogage.

Swift 6.3 introduces significant upgrades to Embedded Swift, enhancing its functionality for resource-constrained environments like microcontrollers. Key improvements include new floating-point printing capabilities, better diagnostics with the EmbeddedRestrictions group, and the introduction of Swift MMIO 0.1.x for code generation and debugging.

Embedded Swift Gets Major Upgrades in Swift 6.3

<a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html" target="_blank"><img src="https://www.techspot.com/images2/news/ts3_thumbs/2025/11/2025-11-19-ts3_thumbs-252.jpg" width="800" height="560" style="padding: 15px 0" title="Judge dismisses lawsuit twice due to alleged deepfake video testimony" /></a><br />A California housing dispute is getting media attention over allegations that lawyers presented a deepfake video as witness testimony. NBC News reports that Judge Victoria Kolakowski became suspicious after the supposed witness showed signs that something was not right, including a monotone voice, fuzzy facial features, and repeated facial expressions....<br /><br /><a href="https://www.techspot.com/news/110317-judge-dismisses-lawsuit-twice-due-alleged-deepfake-video.html">Read Entire Article</a><br /><br />

تجذب نزاع سكني في كاليفورنيا الانتباه الإعلامي بعد ظهور مزاعم بأن المحامين قدموا فيديو مزيف كدليل شهود. أعربت القاضية فيكتوريا كولاكوفسكي عن شكوكها بشأن الفيديو، مشيرة إلى صوت الشاهد الأحادي، وملامح الوجه غير الواضحة، وتكرار التعبيرات. أدى ذلك إلى رفض الدعوى القضائية مرتين.

Una disputa de vivienda en California ha llamado la atención de los medios tras las alegaciones de que los abogados presentaron un video deepfake como testimonio. La jueza Victoria Kolakowski expresó su escepticismo sobre el video, señalando la voz monótona del testigo, rasgos faciales borrosos y expresiones repetitivas. Esto llevó al desestimado de la demanda en dos ocasiones.

Un litige immobilier en Californie suscite l'attention des médias après des allégations selon lesquelles des avocats auraient présenté une vidéo deepfake comme témoignage. La juge Victoria Kolakowski a exprimé des doutes sur la vidéo, notant la voix monotone du témoin, des traits faciaux flous et des expressions répétitives. Cela a conduit à l'annulation de la poursuite à deux reprises.

A California housing dispute has drawn attention after allegations surfaced that lawyers presented a deepfake video as witness testimony. Judge Victoria Kolakowski expressed skepticism about the video, noting the witness's monotone voice, unclear facial features, and repetitive expressions. This led to the dismissal of the lawsuit on two occasions.

Judge dismisses lawsuit twice due to alleged deepfake video testimony

<A HREF="https://economictimes.indiatimes.com/tech/funding/video-generation-platform-luma-ai-raises-900-million-valued-at-4-billion/articleshow/125441234.cms"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251119/i40.jpg"></A>
<P><A HREF="http://www.techmeme.com/251119/p40#a251119p40" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Vaibhavi Khanwalkar / <A HREF="http://economictimes.indiatimes.com/">The Economic Times</A>:<BR>
<SPAN STYLE="font-size:1.3em;"><B><A HREF="https://economictimes.indiatimes.com/tech/funding/video-generation-platform-luma-ai-raises-900-million-valued-at-4-billion/articleshow/125441234.cms">AI video startup Luma AI raised a $900M Series C led by Humain, sources say at a ~$4B valuation, and partners with Humain on a 2GW AI cluster in Saudi Arabia</A></B></SPAN>&nbsp; &mdash;&nbsp; The firm's Indian-American CEO Amit Jain, an IITian who was formerly with Apple, told ET that the domain the company operated &hellip; </P>

نجحت شركة لومة إيه آي الناشئة في مجال الفيديو الذكي في جمع 900 مليون دولار في جولة تمويل من الفئة C بقيادة شركة هومين، مما رفع قيمتها إلى حوالي 4 مليارات دولار. كما تتعاون الشركة، التي أسسها الرئيس التنفيذي الأمريكي الهندي أميت جاين، مع هومين لتطوير مجموعة ذكاء اصطناعي بقدرة 2 جيجاوات في المملكة العربية السعودية، مما يمثل استثمارًا كبيرًا في بنية الذكاء الاصطناعي التحتية في المنطقة.

La startup de video AI Luma AI ha recaudado con éxito 900 millones de dólares en una ronda de financiación Serie C liderada por Humain, alcanzando una valoración de aproximadamente 4 mil millones de dólares. La empresa, fundada por el CEO indio-estadounidense Amit Jain, también se asocia con Humain para desarrollar un clúster de IA de 2GW en Arabia Saudita, marcando una inversión significativa en la infraestructura de IA de la región.

La startup de vidéo AI Luma AI a réussi à lever 900 millions de dollars lors d'un tour de financement de série C dirigé par Humain, portant sa valorisation à environ 4 milliards de dollars. La société, fondée par le PDG américano-indien Amit Jain, s'associe également à Humain pour développer un cluster AI de 2 GW en Arabie Saoudite, marquant un investissement significatif dans l'infrastructure AI de la région.

AI video startup Luma AI has successfully raised $900 million in a Series C funding round led by Humain, bringing its valuation to approximately $4 billion. The company, founded by Indian-American CEO Amit Jain, is also partnering with Humain to develop a 2GW AI cluster in Saudi Arabia, marking a significant investment in the region's AI infrastructure.

AI video startup Luma AI raised a $900M Series C led by Humain, sources say at a ~$4B valuation, and partners with Humain on a 2GW AI cluster in Saudi Arabia (Vaibhavi Khanwalkar/The Economic Times)

<p><a href="https://www.microsoft.com/en-us/"><u>Microsoft</u></a> is fundamentally restructuring its Windows operating system to become what executives call the first &quot;agentic OS,&quot; embedding the infrastructure needed for autonomous AI agents to operate securely at enterprise scale — a watershed moment in the evolution of personal computing that positions the 40-year-old platform as the foundation for a new era of human-machine collaboration.</p><p>The company announced Tuesday at its <a href="https://ignite.microsoft.com/en-US/home"><u>Ignite conference</u></a> that it is introducing native agent infrastructure directly into <a href="http://microsoft.com/en-us/windows/windows-11?r=1"><u>Windows 11</u></a>, allowing AI agents — autonomous software programs that can perform complex, multi-step tasks on behalf of users — to discover tools, execute workflows, and interact with applications through standardized protocols while operating in secure, policy-controlled environments separate from user sessions.</p><p>The shift is Microsoft&#x27;s most significant architectural evolution of Windows since the introduction of the modern security model, transforming the operating system from a platform where users manually orchestrate applications into one where they can &quot;simply express your desired outcome, and agents handle the complexity,&quot; according to Pavan Davuluri, President of Windows &amp; Devices at Microsoft.</p><p>&quot;Windows 11 starts with this notion of secure by design, secure by default,&quot; Davuluri said in an exclusive interview with VentureBeat. &quot;And a lot of the work that we&#x27;re doing today, when we think about the engagement we have with our customers, the expectations they have with us is making sure we are building upon the fact that Windows is the most secure platform for them and is the most resilient platform as well.&quot;</p><p>The announcements arrive as enterprises are <a href="https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai"><u>experimenting with AI agents</u></a> but struggling with fragmented tooling, security concerns, and lack of centralized management — challenges that Microsoft believes only operating system-level integration can solve. The stakes are enormous: with Windows running on an estimated 1.4 billion devices globally, Microsoft&#x27;s architectural choices will likely shape how organizations deploy autonomous AI systems for years to come.</p><h3><b>New platform primitives create foundation for agent computing</b></h3><p>At the core of Microsoft&#x27;s vision are three new platform capabilities entering preview that fundamentally change how agents operate on Windows. <a href="https://learn.microsoft.com/en-us/connectors/overview"><u>Agent Connectors</u></a> provide native support for the <a href="https://learn.microsoft.com/en-us/dynamics365/release-plan/2025wave2/service/dynamics365-customer-service/connect-ai-agents-using-model-context-protocol-server"><u>Model Context Protocol (MCP)</u></a>, an open standard introduced by Anthropic that allows AI agents to connect with external tools and data sources. Microsoft has built what it calls an &quot;on-device registry&quot; — a secure, manageable repository where developers can register their applications&#x27; capabilities as agent connectors, making them discoverable to any compatible agent on the system.</p><p>&quot;These are platform capabilities that then become available to all of our customers,&quot; Davuluri explained, describing how the Windows file system, for example, becomes an agent connector that any MCP-compatible agent can access with user consent. &quot;We&#x27;re able to do this in a fashion that can scale for one but it also allows others to participate in the Windows registry for MCP.&quot;</p><p>The architecture introduces an <a href="https://blogs.windows.com/windowsexperience/2025/05/19/securing-the-model-context-protocol-building-a-safer-agentic-future-on-windows/"><u>MCP proxy layer</u></a> that handles authentication, authorization, and auditing for all communication between agents and connectors. Microsoft is launching with two built-in agent connectors for File Explorer and System Settings, allowing agents to manage files or adjust system configurations like switching between light and dark mode — all with explicit user permission.</p><p><a href="https://support.microsoft.com/en-us/windows/experimental-agentic-features-a25ede8a-e4c2-4841-85a8-44839191dfb3"><u>Agent Workspace</u></a>, entering private preview, represents perhaps the most significant security innovation. It creates what Microsoft describes as &quot;a contained, policy-controlled, and auditable environment where agents can interact with software&quot; — essentially a parallel desktop session where agents operate with their own distinct identity, completely separate from the user&#x27;s primary session.</p><p>&quot;We want to be able to have clarity in the identity of the agent that is operating in the local operating system,&quot; Davuluri said, addressing security concerns about agents accessing sensitive data. &quot;We want that session to be a session that is secure, that is policy control, that is manageable, that has transparency and auditability.&quot;</p><p>Each agent workspace runs with minimal privileges by default, accessing only explicitly granted resources. The system maintains detailed audit logs distinguishing agent actions from user actions — critical for enterprises that need to prove compliance and track all changes to systems and data.</p><p><a href="https://adoption.microsoft.com/en-us/ai-agents/agents-in-microsoft-365/"><u>Windows 365 for Agents</u></a> extends this infrastructure to the cloud, turning Microsoft&#x27;s Cloud PC offering into execution environments for agents. Instead of running on local devices, agents can operate in secure, policy-controlled virtual machines in Azure, enabling what Microsoft calls &quot;computer-using agents&quot; to interact with legacy applications and perform automation tasks at scale without consuming local compute resources.</p><h3><b>Taskbar becomes command center for monitoring AI agents at work</b></h3><p>The infrastructure enables significant user interface changes designed to make agents as commonplace as applications. Microsoft is introducing &quot;Ask Copilot on the taskbar,&quot; a unified entry point in preview that combines Microsoft 365 Copilot, agent invocation, and traditional search in a single interface.</p><p>Users will be able to invoke agents using &quot;@&quot; mentions directly from the taskbar, then monitor their progress through familiar UI patterns like hover cards, progress badges, and notifications — all while continuing other work. When an agent completes a task or needs input, it surfaces updates through the taskbar without disrupting the user&#x27;s primary workflow.</p><p>&quot;We&#x27;ve evolved and created new UX in the taskbar to reflect the unique needs of agents performing background tasks on your behalf,&quot; said Navjot Virk, Corporate Vice President of Windows Experiences, describing features like progress bars and status badges that indicate when agents are working, need approval, or have completed tasks.</p><p>The design philosophy, Virk emphasized, centers on user control. &quot;These experiences are designed to be opt in. We want to give customers full control over when and how they engage with copilots and agents.&quot;</p><p>For commercial <a href="https://www.microsoft.com/en-us/microsoft-365-copilot"><u>Microsoft 365 Copilot</u></a> users, the integration goes deeper. Microsoft is embedding Copilot directly into File Explorer, allowing users to ask questions, generate summaries, or draft emails based on document contents without leaving the file management interface. On Copilot+ PCs — devices with neural processing units capable of 40 trillion operations per second — new capabilities include converting any on-screen table into an Excel spreadsheet through the Click to Do feature.</p><h3><b>Microsoft bets on open standards against Apple and Google&#x27;s proprietary approaches</b></h3><p>Microsoft&#x27;s embrace of the open <a href="https://www.anthropic.com/news/model-context-protocol"><u>Model Context Protocol</u></a>, created by Anthropic, marks a strategic bet on openness as enterprises evaluate competing AI platforms from Apple and Google that use proprietary frameworks.</p><p>&quot;Windows is an open platform, and by virtue [of being] an open platform, we certainly have the ability to take existing technologies, evolve, harden, adapt those, but we also allow customers to bring their own capabilities to the platform as well,&quot; Davuluri said when asked about competing with <a href="https://www.apple.com/apple-intelligence/"><u>Apple Intelligence</u></a> and Google&#x27;s <a href="https://www.android.com/enterprise/ai-at-work/"><u>Android AI for Enterprise</u></a>.</p><p>The company demonstrated this openness with Claude, Anthropic&#x27;s AI assistant, accessing the Windows file system through agent connectors with user consent — one of numerous partnerships Microsoft has secured. Dynamics 365 is using the File Explorer connector to streamline expense reporting, reducing what was previously a 30-minute, dozen-step process to &quot;one sentence with high accuracy,&quot; according to Microsoft&#x27;s blog post. Other early partners include <a href="https://manus.im/"><u>Manus AI</u></a>, <a href="https://dash.dropbox.com/"><u>Dropbox Dash</u></a>, <a href="https://roboflow.com/"><u>Roboflow</u></a>, and <a href="https://www.infosys.com/"><u>Infosys</u></a>.</p><p>&quot;Windows is the platform in which they build upon,&quot; Davuluri said of enterprise customers. &quot;And so our ability to take those existing bodies of work they have, and extend them is the, I think, the least friction way for them to go, learn, adopt, experiment and find ways to [scale].&quot;</p><h3><b>Security model enforces strict containment and mandatory user consent</b></h3><p>Microsoft&#x27;s security model for agents adheres to what it calls &quot;<a href="https://learn.microsoft.com/en-us/purview/deploymentmodels/depmod-securebydefault-intro"><u>secure by default</u></a>&quot; policies aligned with the company&#x27;s broader <a href="https://www.microsoft.com/en-us/trust-center/security/secure-future-initiative"><u>Secure Future Initiative</u></a>. All agent connectors registered in the on-device registry must meet strict requirements around packaging and identity, with applications properly packaged and signed by trusted sources. Developers must explicitly declare the minimum capabilities their agent connectors require, and agents and connectors run in isolated environments with dedicated agent user accounts, separate from human user accounts. Windows requires explicit user approval when agents first access sensitive resources like files or system settings.</p><p>&quot;We give Windows the ability to go deliver on the security expectations, and then it is auditable at the end of the day,&quot; Davuluri said. &quot;You still want an auditability log that looks similar to perhaps what you use in the cloud. And so all three pieces are built into the design and architecture of Agent Workspace.&quot;</p><p>For IT administrators, <a href="https://www.microsoft.com/en-us/"><u>Microsoft</u></a> is introducing management policies through <a href="https://learn.microsoft.com/en-us/intune/intune-service/fundamentals/what-is-intune"><u>Intune</u></a> and <a href="https://learn.microsoft.com/en-us/windows-server/identity/ad-ds/manage/group-policy/group-policy-overview"><u>Group Policy</u></a> that allow organizations to enable or disable agent features at device and account levels, set minimum security policy levels, and access event logs enumerating all agent connector invocations and errors. The company emphasized that agents operate with restricted privileges, with minimal permissions by default and access granted only to explicitly approved resources that users can revoke at any time. </p><h3><b>Post-quantum cryptography and recovery tools address emerging and persistent threats</b></h3><p>Beyond agent infrastructure, Microsoft announced significant security and resilience updates addressing both emerging and persistent enterprise challenges. <a href="https://cloud.google.com/security/resources/post-quantum-cryptography?hl=en"><u>Post-Quantum Cryptography APIs</u></a> are now generally available in Windows, allowing organizations to begin migrating to encryption algorithms designed to withstand future quantum computing attacks that could break today&#x27;s cryptographic standards. Microsoft worked closely with the National Institute of Standards and Technology to implement these algorithms.</p><p>&quot;We are introducing post quantum cryptography APIs in Windows,&quot; Davuluri said. &quot;For customers who want to be able to do cryptographic encryption in their workloads, they can start taking advantage of these APIs in Windows for the first time. That is a huge step forward for us when we think about the future of windows.&quot;</p><p>Hardware-accelerated <a href="https://learn.microsoft.com/en-us/windows/security/operating-system-security/data-protection/bitlocker/"><u>BitLocker</u></a> will arrive on new devices starting spring 2026, offloading disk encryption to dedicated silicon for faster performance while providing hardware-level key protection. Sysmon functionality is becoming generally available as part of Windows in early 2026, bringing advanced forensics and threat detection capabilities previously available only as a separate download directly into the operating system&#x27;s event logging system.</p><p>The company also detailed progress on its Windows Resiliency Initiative, launched a year ago following the CrowdStrike incident that disrupted 8.5 million Windows devices globally. New recovery capabilities include Quick Machine Recovery with expanded networking support and Autopatch management, allowing IT to remotely fix devices stuck in Windows Recovery Environment. Point-in-time restore entering preview rolls back devices to earlier states to resolve update conflicts or configuration errors, while Cloud rebuild in preview allows IT to remotely rebuild malfunctioning devices by downloading fresh installation media and using Autopilot for zero-touch provisioning.</p><p>Microsoft is also raising security requirements for third-party drivers across the Windows ecosystem. Following updated requirements for antivirus drivers effective April 1, 2025, the company is expanding this approach to other driver classes including networking, cameras, USB, printers, and storage — requiring higher certification standards, adding compiler safeguards, and providing more Windows in-box drivers to reduce reliance on third-party kernel-mode code.</p><h3><b>Measured rollout reflects enterprise caution around autonomous software</b></h3><p>Microsoft is positioning these updates as essential infrastructure for what it calls &quot;<a href="https://www.microsoft.com/en-us/worklab/work-trend-index/2025-the-year-the-frontier-firm-is-born"><u>Frontier Firms</u></a>&quot; — organizations that &quot;blend human ingenuity with intelligent systems to deliver real outcomes.&quot; However, the company emphasized a cautious, opt-in approach that reflects enterprise concerns about autonomous software agents.</p><p>&quot;The principles we&#x27;re using in designing these new platform capabilities accounts for the reality that we have a very, very broad user base,&quot; Davuluri said. &quot;A lot of the features and capabilities we&#x27;re building are opt in capabilities. And so it is our goal to be able to have users find value in the workflow and meet them.&quot;</p><p>Virk emphasized the measured approach: &quot;This is more about meeting customers where they are and then taking them on this journey when they are ready. So there&#x27;s the optionality, but also having support for it. And really important thing is that they should feel comfortable. They should feel secure.&quot;</p><p>Microsoft&#x27;s bet is that only operating system-level integration can provide the security, governance, and user experience required for mainstream AI agent adoption. Whether that vision materializes will depend on developer adoption, enterprise comfort with autonomous software, and Microsoft&#x27;s ability to balance innovation with the stability that <a href="https://gizmodo.com/windows-40-year-anniversary-evolving-into-bloated-ai-slop-copilot-2000686932"><u>40 years of Windows</u></a> customers expect. After four decades of putting users in control of their computers, Windows is now asking them to share that control with machines.</p>

تقوم شركة مايكروسوفت بإعادة هيكلة نظام التشغيل ويندوز لإنشاء ما تسميه أول 'نظام تشغيل وكيل'. ستدمج هذه الإطار الجديد البنية التحتية اللازمة لعمل الوكلاء الذكاء الاصطناعي المستقل بشكل آمن على نطاق المؤسسات. تم الإعلان عن ذلك في مؤتمر Ignite، حيث ستسمح دمج البنية التحتية للوكلاء الأصليين في ويندوز 11 للوكلاء الذكاء الاصطناعي بأداء مهام معقدة، واكتشاف الأدوات، والتفاعل مع التطبيقات في بيئات آمنة، مما يمثل لحظة مهمة في تطور الحوسبة الشخصية.

Microsoft está reestructurando su sistema operativo Windows para crear lo que llama el primer 'sistema operativo agente'. Este nuevo marco integrará la infraestructura necesaria para que los agentes de IA autónomos operen de manera segura a escala empresarial. Anunciado en la conferencia Ignite, la integración de la infraestructura de agentes nativos en Windows 11 permitirá a los agentes de IA realizar tareas complejas, descubrir herramientas e interactuar con aplicaciones en entornos seguros, marcando un momento significativo en la evolución de la computación personal.

Microsoft restructure son système d'exploitation Windows pour créer ce qu'il appelle le premier 'système d'exploitation agentique'. Ce nouveau cadre intégrera l'infrastructure nécessaire pour que les agents IA autonomes fonctionnent en toute sécurité à l'échelle des entreprises. Annoncé lors de la conférence Ignite, l'intégration de l'infrastructure d'agent natif dans Windows 11 permettra aux agents IA d'effectuer des tâches complexes, de découvrir des outils et d'interagir avec des applications dans des environnements sécurisés, marquant une évolution significative de l'informatique personnel…

Microsoft is restructuring its Windows operating system to create what it calls the first 'agentic OS.' This new framework will embed the necessary infrastructure for autonomous AI agents to operate securely at enterprise scale. Announced at the Ignite conference, the integration of native agent infrastructure into Windows 11 will enable AI agents to perform complex tasks, discover tools, and interact with applications in secure environments, marking a significant evolution in personal computing.

Microsoft remakes Windows for an era of autonomous AI agents

Welcome to Tech In Depth, our daily newsletter about the business of tech from Bloomberg’s journalists around the world. Today, Pei Li and Vlad Savov delve into Tencent’s role in the new program Apple is offering mobile app developers.

تتعاون شركة آبل مع شركة تينسنت لتنظيم اقتصاد وي شات، وهو تحرك مهم لمطوري التطبيقات المحمولة. تأتي هذه المبادرة كجزء من برنامج جديد تقدمه آبل، والذي يهدف إلى تزويد المطورين بأدوات وأطر أفضل للتنقل في تعقيدات منصة وي شات. إن مشاركة تينسنت، وهي لاعب رئيسي في المشهد التكنولوجي الصيني، تؤكد على أهمية هذه الشراكة في تشكيل مستقبل تطوير التطبيقات واستراتيجيات تحقيق الدخل داخل نظام وي شات البيئي.

Apple y Tencent están colaborando para regular la economía de WeChat, un movimiento significativo para los desarrolladores de aplicaciones móviles. Esta iniciativa forma parte de un nuevo programa que Apple está ofreciendo, que tiene como objetivo proporcionar a los desarrolladores mejores herramientas y marcos para navegar las complejidades de la plataforma WeChat. La participación de Tencent, un actor importante en el panorama tecnológico chino, subraya la importancia de esta asociación en la configuración del futuro del desarrollo de aplicaciones y las estrategias de monetización dentro del…

Apple et Tencent collaborent pour réguler l'économie de WeChat, une initiative significative pour les développeurs d'applications mobiles. Ce programme fait partie d'une nouvelle offre d'Apple, visant à fournir aux développeurs de meilleurs outils et cadres pour naviguer dans les complexités de la plateforme WeChat. L'implication de Tencent, un acteur majeur du paysage technologique chinois, souligne l'importance de ce partenariat pour façonner l'avenir du développement d'applications et des stratégies de monétisation au sein de l'écosystème WeChat.

Apple and Tencent are collaborating to regulate the WeChat economy, a significant move for mobile app developers. This initiative is part of a new program that Apple is offering, which aims to provide developers with better tools and frameworks to navigate the complexities of the WeChat platform. The involvement of Tencent, a major player in the Chinese tech landscape, underscores the importance of this partnership in shaping the future of app development and monetization strategies within the WeChat ecosystem.

Apple and Tencent Try to Regulate the WeChat Economy

Integration of Copilot Actions into Windows is off by default, but for how long?

أصدرت شركة مايكروسوفت تحذيرًا بشأن ميزة الذكاء الاصطناعي الخاصة بها، Copilot Actions، التي تم تعطيلها حاليًا بشكل افتراضي في نظام ويندوز. وقد أعرب النقاد عن مخاوفهم بشأن المخاطر المحتملة لهذه الميزة، بما في ذلك إمكانية إصابة الأجهزة وسرقة البيانات الحساسة. ولم توضح الشركة بعد المدة التي ستظل فيها هذه الإعدادات الافتراضية سارية.

Microsoft ha emitido una advertencia sobre su función de IA, Copilot Actions, que actualmente está desactivada por defecto en Windows. Los críticos han expresado preocupaciones sobre los riesgos potenciales de esta función, incluida la posibilidad de infectar máquinas y robar datos sensibles. La empresa aún no ha aclarado cuánto tiempo permanecerá esta configuración predeterminada.

Microsoft a émis un avertissement concernant sa fonctionnalité d'IA, Copilot Actions, qui est actuellement désactivée par défaut dans Windows. Les critiques ont exprimé des préoccupations quant aux risques potentiels de cette fonctionnalité, notamment la possibilité d'infecter des machines et de voler des données sensibles. L'entreprise n'a pas encore précisé combien de temps ce paramètre par défaut restera en place.

Microsoft has issued a warning regarding its AI feature, Copilot Actions, which is currently disabled by default in Windows. Critics have raised concerns about the potential risks of this feature, including the possibility of infecting machines and stealing sensitive data. The company has yet to clarify how long this default setting will remain in place.

Testing shows Apple N1 Wi-Fi chip improves on older Broadcom chips in every way

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