In this first article of a planned tutorial series about the <a href="https://nuxt.com/" rel="noopener noreferrer">Nuxt</a> framework, I'll show you how easily you can get a working project up and running.

<h2>
 
 
 Nuxt Simple
</h2>

<a href="https://github.com/AloisSeckar/demos-nuxt/tree/main/nuxt-simple" rel="noopener noreferrer">nuxt-simple @ GitHub</a>

At the above link, you'll find the source code for a simple starter project for Nuxt (currently version 4). It's based on the <a href="https://stackblitz.com/github/nuxt/starter/tree/v4" rel="noopener noreferrer">Nuxt Starter on the StackBlitz cloud platform</a>, which you can run and try directly in your browser.

To run Nuxt locally, you'll need to:

<ul>
<li>Install the <a href="https://nodejs.org/" rel="noopener noreferrer">Node.js</a> runtime environment (current LTS version recommended, at least <code>18.*</code>)</li>
<li>Download the project using the <a href="https://git-scm.com/" rel="noopener noreferrer">Git</a> version control tool</li>
<li>Open it in the <a href="https://code.visualstudio.com/" rel="noopener noreferrer">VS Code</a> development environment</li>
<li>Switch to the <code>nuxt-simple</code> folder</li>
<li>Run <code>pnpm install</code> in the terminal</li>
<li>Start the application with <code>pnpm dev</code>
</li>
<li>Open <code>http://localhost:3000</code> in your browser</li>
</ul>

You'll see a simple page that doesn't do much on its own, but it proves Nuxt is running. We can start building from there. First, let's go through the individual components:

<ul>
<li>
<code>app/app.vue</code> - the main entry point with a simple template inside <code>&lt;template&gt;</code> (since Nuxt v4, it's located in the <code>/app</code> folder rather than directly in the project root). Unlike the official nuxt-starter, I replaced their <code>&lt;NuxtWelcome&gt;</code> component with custom HTML code, which you'll see rendered on the page when you launch the project.</li>
<li>
<code>public/favicon.ico</code> - an optional image file that adds an icon to the browser tab. The application works without it, but looks better havinh one. It also demonstrates how simple Nuxt can be - just copy your image to this path and you're done, no additional configuration needed.</li>
<li>
<code>README.md</code> - a description file that presents basic information about the project when viewing the <a href="https://github.com/" rel="noopener noreferrer">GitHub</a> repository. While not required, it's common practice to include this to inform visitors and potential users about your code.</li>
<li>
<code>eslint.config.mjs</code> - this configuration file for the <a href="https://eslint.nuxt.com/" rel="noopener noreferrer">@nuxt/eslint</a> module controls the behavior of <a href="https://eslint.org/" rel="noopener noreferrer">ESLint</a>, a tool I added to the project (though it's not in the official nuxt-starter) to monitor syntax rules and help keep your source code clean. It requires some environment setup, which will be covered in a future article. Until configured, it won't cause any issues, but you won't benefit from its code quality assistance either.</li>
<li>
<code>nuxt.config.ts</code> - the main configuration file for Nuxt. By default, it can be completely "empty" - all settings are placed inside the object parameter of the built-in <code>defineNuxtConfig</code> method (which is automatically imported). Currently, it contains the configuration for the <a href="https://eslint.nuxt.com/" rel="noopener noreferrer">@nuxt/eslint</a> module, which works together with <code>eslint.config.mjs</code> to implement ESLint rules.

<ul>
<li>
<code>pnpm-lock.yaml</code> - automatically generated by the <code>pnpm</code> package manager during <code>pnpm install</code>, this file maintains an exhaustive definition of all project dependencies. You may encounter opinions that lock files can be omitted from Git since they change frequently and developers generate them locally anyway. However, I follow the <a href="https://stackoverflow.com/a/44210813/3204544" rel="noopener noreferrer">recommendation</a> to include them in the repository, as they help ensure consistent builds across all environments. Without it, you might encounter weird errors from developers having slightly different package versions.</li>
</ul>


</li>

<li>

<code>package.json</code> - a key file that primarily defines dependencies on external packages. In our case, this includes <code>nuxt</code>, the ESLint module <code>@nuxt/eslint</code>, and <code>typescript</code>. It also contains project metadata and command definitions in the <code>script</code> section, which can be invoked as <code>pnpm *</code>. These script definitions aren't required, but they make operations more convenient.
</li>

<li>

<code>tsconfig.json</code> - allows customization of TypeScript compiler settings that would otherwise be auto-generated. More on this in a future article.</li>

</ul>

<h2>
 
 
 Nuxt Starter
</h2>

If you want to start your own small Nuxt project, you can find the above setup in a standalone GitHub repository for easier cloning:

<a href="https://github.com/AloisSeckar/nuxt-v4-starter" rel="noopener noreferrer">nuxt-v4-starter @ GitHub</a>

<h2>
 
 
 Nuxt Minimal
</h2>

<a href="https://github.com/AloisSeckar/demos-nuxt/tree/main/nuxt-minimal" rel="noopener noreferrer">nuxt-minimal @ GitHub</a>

This sub-project demonstrates that Nuxt can actually be launched with even less. While not recommended for real projects, the website will run with just these two files:

<ul>
<li>
<code>app.vue</code> - the basic entry point with a simple template inside <code>&lt;template&gt;</code>
</li>
<li>
<code>package.json</code> - in which the dependency on the <code>nuxt</code> package will be declared in <code>devDependencies</code>
</li>
</ul>

Instead of <code>pnpm dev</code> (which would require additional <code>package.json</code> configuration), the application is launched using <code>npx nuxi dev</code> command. Here, <code>npx</code> is an npm tool that runs packages without installing them locally into project, and <code>nuxi</code> is the CLI tool from the Nuxt framework creators.

The remaining two files in the repository - <code>README.md</code> and <code>pnpm-lock.yaml</code> - have already been described above and are technically not required in the repository.

WARNING: This is meant only as a demonstration of minimalism. The absence of standard system components would likely cause problems in real-world use. Stick with the <code>nuxt-simple</code> variant for actual projects.

In the second part of the tutorial, we'll explore the basic building blocks of Nuxt applications: <a href="https://dev.to/article/nuxt-pages">components and pages</a>.

يقدم المقال سلسلة من الدروس حول إطار عمل Nuxt، مع التركيز بشكل خاص على كيفية تشغيل مشروع بسيط. يوفر رابطًا لمستودع GitHub يحتوي على الشيفرة المصدرية لمشروع بدء التشغيل المستند إلى الإصدار 4 من Nuxt. لتشغيل المشروع محليًا، يتم توجيه المستخدمين لتثبيت Node.js (الإصدار 18 أو أعلى)، وتنزيل المشروع باستخدام Git، وفتحه في بيئة تطوير VS Code. هذه السلسلة من الدروس مهمة لأنها تهدف إلى تبسيط العملية للمطورين الذين يرغبون في استخدام إطار عمل Nuxt في مشاريعهم.

El artículo presenta una serie de tutoriales sobre el framework Nuxt, centrándose específicamente en cómo poner en marcha un proyecto simple. Proporciona un enlace a un repositorio de GitHub que contiene el código fuente de un proyecto inicial basado en la versión 4 de Nuxt. Para ejecutar el proyecto localmente, se instruye a los usuarios a instalar Node.js (versión 18 o superior), descargar el proyecto usando Git y abrirlo en el entorno de desarrollo de VS Code. Este tutorial es significativo ya que busca simplificar el proceso para los desarrolladores que desean utilizar el framework Nuxt en sus proyectos.

L'article présente une série de tutoriels sur le framework Nuxt, en se concentrant spécifiquement sur la mise en route d'un projet simple. Il fournit un lien vers un dépôt GitHub contenant le code source d'un projet de démarrage basé sur la version 4 de Nuxt. Pour exécuter le projet localement, les utilisateurs doivent installer Node.js (version 18 ou supérieure), télécharger le projet à l'aide de Git et l'ouvrir dans l'environnement de développement VS Code. Ce tutoriel est important car il vise à simplifier le processus pour les développeurs souhaitant utiliser le framework Nuxt pour leurs projets.

The article introduces a tutorial series on the Nuxt framework, specifically focusing on getting a simple project up and running. It provides a link to a GitHub repository containing the source code for a starter project based on Nuxt version 4. To run the project locally, users are instructed to install Node.js (version 18 or higher), download the project using Git, and open it in the VS Code development environment. This tutorial is significant as it aims to simplify the process for developers looking to utilize the Nuxt framework for their projects.

Nuxt Tutorial 1 - First Steps

Guardio is leveraging its experience building browser extensions and apps that scan for malicious and phishing sites to build a tool that looks for artifacts in code and websites made with vibe coding tools.

نجحت شركة Guardio الناشئة في مجال الأمن السيبراني في تأمين 80 مليون دولار من التمويل من ION Crossover Partners. تشتهر الشركة بخبرتها في تطوير إضافات المتصفح والتطبيقات التي تكشف عن المواقع الضارة والمحتالة. تخطط Guardio لاستخدام هذا التمويل لإنشاء أداة تبحث عن الآثار في الشيفرة والمواقع التي تم إنشاؤها باستخدام أدوات البرمجة vibe.

La startup de seguridad Guardio ha asegurado 80 millones de dólares en financiamiento de ION Crossover Partners. La empresa es conocida por su experiencia en el desarrollo de extensiones de navegador y aplicaciones que detectan sitios maliciosos y de phishing. Guardio planea utilizar estos fondos para crear una herramienta que identifique artefactos en el código y sitios web construidos con herramientas de codificación vibe.

La startup de sécurité Guardio a obtenu 80 millions de dollars de financement de la part d'ION Crossover Partners. Connue pour son expertise dans le développement d'extensions de navigateur et d'applications détectant les sites malveillants et de phishing, Guardio prévoit d'utiliser ce financement pour créer un outil identifiant les artefacts dans le code et les sites web construits avec des outils de codage vibe.

Security startup Guardio has secured $80 million in funding from ION Crossover Partners. The company is known for its expertise in developing browser extensions and applications that detect malicious and phishing websites. Guardio plans to utilize this funding to create a tool that identifies artifacts in code and websites built with vibe coding tools.

Security startup Guardio nabs $80M from ION Crossover Partners

A new artificial intelligence startup founded by the creators of <a href="https://opencv.org/">the world&#x27;s most widely used computer vision library</a> has emerged from stealth with technology that generates realistic human-centric videos up to five minutes long — a dramatic leap beyond the capabilities of rivals including OpenAI&#x27;s <a href="https://openai.com/sora/">Sora</a> and Google&#x27;s <a href="https://deepmind.google/models/veo/">Veo</a>.<a href="https://craftstory.com/">CraftStory</a>, which launched Tuesday with $2 million in funding, is introducing Model 2.0, a video generation system that addresses one of the most significant limitations plaguing the nascent AI video industry: duration. While OpenAI&#x27;s <a href="https://openai.com/index/sora-2/">Sora 2</a> tops out at 25 seconds and most competing models generate clips of 10 seconds or less, CraftStory&#x27;s system can produce continuous, coherent video performances that run as long as a typical YouTube tutorial or product demonstration.The breakthrough could unlock substantial commercial value for enterprises struggling to scale video production for training, marketing, and customer education — markets where brief AI-generated clips have proven inadequate despite their visual polish.&quot;If you really try to create a video with one of these video generation systems, you find that a lot of the times you want to implement a certain creative vision, and regardless of how detailed the instructions are, the systems basically ignore a part of your instructions,&quot; said Victor Erukhimov, CraftStory&#x27;s founder and CEO, in an exclusive interview with VentureBeat. &quot;We developed a system that can generate videos basically as long as you need them.&quot;<h3>How parallel processing solves the long-form video problem</h3>CraftStory&#x27;s advance rests on what the company describes as a parallelized diffusion architecture — a fundamentally different approach to how AI models generate video compared to the sequential methods employed by most competitors.Traditional video generation models work by running diffusion algorithms on increasingly large three-dimensional volumes where time represents the third axis. To generate a longer video, these models require proportionally larger networks, more training data, and significantly more computational resources.<a href="https://craftstory.com/">CraftStory</a> instead runs multiple smaller diffusion algorithms simultaneously across the entire duration of the video, with bidirectional constraints connecting them. &quot;The latter part of the video can influence the former part of the video too,&quot; Erukhimov explained. &quot;And this is pretty important, because if you do it one by one, then an artifact that appears in the first part propagates to the second one, and then it accumulates.&quot;Rather than generating eight seconds and then stitching on additional segments, CraftStory&#x27;s system processes all five minutes concurrently through interconnected diffusion processes.Crucially, CraftStory trained its model on proprietary footage rather than relying solely on internet-scraped videos. The company hired studios to shoot actors using high-frame-rate camera systems that capture crisp detail even in fast-moving elements like fingers — avoiding the motion blur inherent in standard 30-frames-per-second YouTube clips.&quot;What we showed is that you don&#x27;t need a lot of data and you don&#x27;t need a lot of training budget to create high quality videos,&quot; Erukhimov said. &quot;You just need high quality data.&quot;Model 2.0 currently operates as a video-to-video system: users upload a still image to animate and a &quot;driving video&quot; containing a person whose movements the AI will replicate. CraftStory provides preset driving videos shot with professional actors, who receive revenue shares when their motion data is used, or users can upload their own footage.The system generates 30-second clips at low resolution in approximately 15 minutes. An advanced lip-sync system synchronizes mouth movements to scripts or audio tracks, while gesture alignment algorithms ensure body language matches speech rhythm and emotional tone.<h3>Fighting a war chest battle with $2 million against billions</h3>CraftStory&#x27;s funding comes almost entirely from <a href="https://finance.yahoo.com/news/2-25-billion-exit-taught-130300997.html">Andrew Filev</a>, who sold his project management software company Wrike to Citrix for <a href="https://techcrunch.com/2021/01/19/citrix-is-acquiring-wrike-from-vista-for-2-25b/">$2.25 billion</a> in 2021 and now runs <a href="https://zencoder.ai/">Zencoder</a>, an AI coding company. The modest raise stands in stark contrast to the billions flowing into competing efforts — OpenAI has <a href="https://www.reuters.com/technology/artificial-intelligence/openai-closes-66-billion-funding-haul-valuation-157-billion-with-investment-2024-10-02/">raised over $6 billion</a> in its latest funding round alone.Erukhimov pushed back on the notion that massive capital is prerequisite for success. &quot;I don&#x27;t necessarily buy the thesis that compute is the path to success,&quot; he said. &quot;It definitely helps if you have compute. But if you raise a billion dollars on a PowerPoint, in the end, no one is happy, neither the founders nor the investors.&quot;Filev defended the David-versus-Goliath approach. &quot;When you invest in startups, you&#x27;re fundamentally betting on people,&quot; he said in an interview with VentureBeat. &quot;To paraphrase Margaret Mead: never underestimate what a small group of thoughtful, committed engineers and scientists can build.&quot;He argued that CraftStory benefits from a focused strategy. &quot;The big labs are in an arms race to build general-purpose video foundation models,&quot; Filev said. &quot;CraftStory is riding that wave and going very deep into a specific format: long-form, engaging, human-centric video.&quot;<h3>Why computer vision expertise matters in generative AI video</h3>Erukhimov&#x27;s credibility stems from his deep roots in computer vision rather than the transformer architectures that have dominated recent AI advances. He was an early contributor to <a href="https://opencv.org/">OpenCV</a> — the Open Source Computer Vision Library that has become the de facto standard for computer vision applications, with over <a href="https://github.com/opencv/opencv">84,000 stars on GitHub</a>.When Intel reduced its support for OpenCV in the mid-2000s, Erukhimov co-founded Itseez with the explicit goal of maintaining and advancing the library. The company expanded OpenCV significantly and pivoted toward automotive safety systems before Intel acquired it in 2016.Filev said this background is precisely what makes Erukhimov well-positioned for video generation. &quot;What people sometimes miss is that generative AI video isn&#x27;t just about the generative part. It&#x27;s about understanding motion, facial dynamics, temporal coherence, and how humans actually move,&quot; Filev said. &quot;Victor has spent his career mastering exactly those problems.&quot;<h3>Enterprise focus targets training videos and product demos</h3>While much of the public excitement around AI video generation has centered on creative tools for consumers, CraftStory is pursuing a decidedly enterprise-focused strategy.&quot;We are definitely thinking about B2B more than consumer,&quot; Erukhimov said. &quot;We&#x27;re thinking about companies, specifically software companies, being able to make cool training videos and product videos and launch videos.&quot;The logic is straightforward: corporate training, product tutorials, and customer education videos often run several minutes and require consistent quality throughout. A 10-second AI clip cannot effectively demonstrate how to use enterprise software or explain a complex product feature.&quot;If you need a longer-form video, then you should go with us,&quot; Erukhimov said. &quot;We can create up to five minutes, consistent video, high quality.&quot;Filev echoed this assessment. &quot;One huge gap in this market is the lack of models that can generate consistent videos over longer sequences — and that&#x27;s extremely important for real-world use,&quot; he said. &quot;If you&#x27;re creating a commercial for your company, a 10-second video, no matter how good it looks, just isn&#x27;t enough. You need 30 seconds, you need two minutes — you need more.&quot;The company anticipates cost savings for customers. Filev suggested that &quot;a small business owner could create content in minutes that previously would have cost $20,000 and taken two months to produce.&quot;CraftStory is also courting creative agencies that produce video content for corporate clients, with the value proposition centered on cost and speed: agencies can record an actor on camera and transform that footage into a finished AI video, rather than managing expensive multi-day shoots.The next major development on CraftStory&#x27;s roadmap is a text-to-video model that would allow users to generate long-form content directly from scripts. The team is also developing support for moving-camera scenarios, including the popular &quot;walk-and-talk&quot; format common in high-end advertising.<h3>Where CraftStory fits in a fragmented competitive landscape</h3>CraftStory enters a crowded and rapidly evolving market. OpenAI&#x27;s <a href="https://openai.com/index/sora-2/">Sora 2</a>, while not yet publicly available, has generated significant buzz. Google&#x27;s <a href="https://deepmind.google/models/veo/">Veo models</a> are advancing quickly. <a href="https://runwayml.com/">Runway</a>, <a href="https://pika.art/login">Pika</a>, and <a href="https://stability.ai/">Stability AI </a>all offer video generation tools with different capabilities.Erukhimov acknowledged the competitive pressure but emphasized that CraftStory serves a distinct niche focused on human-centric videos. He positioned rapid innovation and market capture as the company&#x27;s primary strategy rather than relying on technical moats.Filev sees the market fragmenting into distinct layers, with large tech companies serving as &quot;API providers of powerful, general-purpose generation models&quot; while specialized players like CraftStory focus on specific use cases. &quot;If the big players are building the engines, CraftStory is building the production studio and assembly line on top,&quot; he said.Model 2.0 is available now at app.craftstory.com/model-2.0, with the company offering early access to users and enterprises interested in testing the technology. Whether a lightly-funded startup can capture meaningful market share against deep-pocketed incumbents remains uncertain, but Erukhimov is characteristically confident about the opportunity ahead.&quot;AI-generated video will soon become the primary way companies communicate their stories,&quot; he said.

أطلقت CraftStory، وهي شركة ناشئة جديدة في مجال الذكاء الاصطناعي أسسها مبتكرو OpenCV، نظامًا لتوليد الفيديو قادرًا على إنتاج مقاطع فيديو واقعية تركز على الإنسان تصل مدتها إلى خمس دقائق. تتجاوز هذه التكنولوجيا بشكل كبير قدرات المنافسين مثل Sora من OpenAI وVeo من Google، الذين لديهم حدود زمنية أقصر. حصلت الشركة الناشئة على تمويل بقيمة مليوني دولار لدعم نهجها المبتكر في صناعة الفيديو بالذكاء الاصطناعي.

CraftStory, una nueva startup de IA fundada por los creadores de OpenCV, ha lanzado un sistema de generación de video capaz de producir videos realistas centrados en humanos de hasta cinco minutos de duración. Esta tecnología supera significativamente a competidores como Sora de OpenAI y Veo de Google, que tienen límites de duración más cortos. La startup ha asegurado 2 millones de dólares en financiamiento para apoyar su enfoque innovador en la industria de video de IA.

CraftStory, une nouvelle startup d'IA fondée par les créateurs d'OpenCV, a lancé un système de génération vidéo capable de produire des vidéos réalistes centrées sur l'humain d'une durée allant jusqu'à cinq minutes. Cette technologie surpasse considérablement les concurrents tels que Sora d'OpenAI et Veo de Google, qui ont des limites de durée plus courtes. La startup a sécurisé 2 millions de dollars de financement pour soutenir son approche innovante de l'industrie vidéo IA.

CraftStory, a new AI startup founded by the creators of OpenCV, has launched a video generation system capable of producing realistic human-centric videos up to five minutes long. This technology significantly outpaces competitors like OpenAI's Sora and Google's Veo, which have shorter duration limits. The startup has secured $2 million in funding to support its innovative approach to the AI video industry.

OpenCV founders launch AI video startup to take on OpenAI and Google

<a href="https://petapixel.com/2025/11/19/remote-cameras-may-have-captured-first-recorded-tool-use-by-a-wild-wolf/"><img width="1600" height="840" src="https://petapixel.com/assets/uploads/2025/11/wolve-tool-use.jpg" class="attachment-card-large size-card-large wp-post-image" alt="A wolf stands at the edge of a rocky shoreline, holding an orange and white fishing bobber in its mouth. A fishing net and additional gear are lying on the ground nearby. Rippling water is in the background." decoding="async" fetchpriority="high" /></a>Remote cameras have captured footage of wild wolves pulling crab traps out of the sea by their lines to eat the bait inside -- in the first evidence of possible tool use by the canines.
[<a href="https://petapixel.com/2025/11/19/remote-cameras-may-have-captured-first-recorded-tool-use-by-a-wild-wolf/">Read More</a>]

التقطت الكاميرات عن بُعد لقطات لذئاب برية تستخدم تقنية تتضمن سحب فخاخ السلطعون من البحر للوصول إلى الطعم داخلها. وهذا يمثل أول دليل موثق على استخدام محتمل للأدوات من قبل هذه الكلاب.

Cámaras remotas han grabado a lobos salvajes utilizando una técnica que consiste en sacar trampas de cangrejos del mar para acceder al cebo en su interior. Esto marca la primera evidencia documentada de un posible uso de herramientas por parte de estos caninos.

Des caméras à distance ont enregistré des loups sauvages utilisant une technique qui consiste à tirer des pièges à crabes de la mer pour accéder à l'appât à l'intérieur. Cela marque la première preuve documentée d'un potentiel usage d'outils par ces canidés.

Remote cameras have recorded wild wolves using a technique that involves pulling crab traps from the sea to access the bait inside. This marks the first documented evidence of potential tool use by these canines.

Remote Cameras May Have Captured First Recorded Tool Use by a Wild Wolf

The AI startup Firebird Inc. has received US government approval to export Nvidia Corp. chips to Armenia for a supercomputer project in the country, part of a global push to expand artificial intelligence infrastructure.

حصلت شركة Firebird Inc. الناشئة في مجال الذكاء الاصطناعي على موافقة الحكومة الأمريكية لتصدير شرائح Nvidia Corp. إلى أرمينيا، مما يسهل إنشاء مشروع حاسوب فائق في البلاد. تأتي هذه المبادرة كجزء من جهد عالمي أوسع لتعزيز بنية الذكاء الاصطناعي التحتية.

La startup de IA Firebird Inc. ha recibido la aprobación del gobierno de EE. UU. para exportar chips de Nvidia Corp. a Armenia, facilitando el establecimiento de un proyecto de supercomputadora en el país. Esta iniciativa forma parte de un esfuerzo global más amplio para mejorar la infraestructura de inteligencia artificial.

La startup d'IA Firebird Inc. a obtenu l'approbation du gouvernement américain pour exporter des puces de Nvidia Corp. en Arménie, facilitant ainsi l'établissement d'un projet de superordinateur dans le pays. Cette initiative s'inscrit dans un effort mondial plus large pour améliorer l'infrastructure de l'intelligence artificielle.

AI startup Firebird Inc. has obtained approval from the US government to export Nvidia Corp. chips to Armenia, facilitating the establishment of a supercomputer project in the country. This initiative is part of a broader global effort to enhance artificial intelligence infrastructure.

AI Startup Firebird Gets US Approval to Use Nvidia Chips in Armenian Data Center

Gesture of goodwill temporarily cools EU–China dispute that rattled global car supply chains.
The post <a href="https://www.techrepublic.com/article/news-netherlands-nexperia-chipmaker-control/">Netherlands Pauses Move to Seize Chinese-Owned Chipmaker Nexperia</a> appeared first on <a href="https://www.techrepublic.com">TechRepublic</a>.

أوقفت هولندا مؤقتًا جهودها للاستيلاء على شركة Nexperia لصناعة الرقائق المملوكة للصين، وهو إجراء يُعتبر بمثابة لفتة حسن نية تهدف إلى تهدئة التوترات في النزاع القائم بين الاتحاد الأوروبي والصين. تأتي هذه الخطوة وسط مخاوف بشأن تأثيرها على سلاسل الإمداد العالمية التي تأثرت بالفعل بنقص أشباه الموصلات.

Los Países Bajos han detenido temporalmente sus esfuerzos por apoderarse del fabricante de chips Nexperia, de propiedad china, un gesto que se considera como una buena voluntad para aliviar las tensiones en la disputa entre la UE y China. Esta decisión se produce en medio de preocupaciones sobre el impacto en las cadenas de suministro globales, que ya se han visto afectadas por la escasez de semiconductores.

Les Pays-Bas ont temporairement suspendu leurs efforts pour saisir le fabricant de puces Nexperia, détenu par des Chinois, un geste perçu comme une volonté d'apaiser les tensions dans le conflit en cours entre l'UE et la Chine. Cette décision intervient dans un contexte d'inquiétudes concernant l'impact sur les chaînes d'approvisionnement mondiales, déjà affectées par des pénuries de semi-conducteurs.

The Netherlands has temporarily halted its efforts to seize control of the Chinese-owned chipmaker Nexperia, a move seen as a gesture of goodwill aimed at easing tensions in the ongoing EU-China dispute. This decision comes amid concerns over the impact on global car supply chains, which have been affected by semiconductor shortages.

Netherlands Pauses Move to Seize Chinese-Owned Chipmaker Nexperia

استقال لاري سامرز من مجلس إدارة OpenAI، كما أفاد نيويورك تايمز. تأتي استقالته بعد التدقيق في اتصالاته السابقة مع المدان بجريمة الاعتداء الجنسي جيفري إبستين. تمثل هذه الخطوة انسحابًا كبيرًا لسامرز من الأدوار العامة وسط انتقادات متزايدة.

Larry Summers ha renunciado a la junta de OpenAI, según informa The New York Times. Su renuncia se produce tras el escrutinio sobre sus comunicaciones pasadas con el delincuente sexual condenado Jeffrey Epstein. Esta decisión marca un paso significativo en el retiro de Summers de los roles públicos en medio de una creciente crítica.

Larry Summers a démissionné du conseil d'administration d'OpenAI, selon le New York Times. Sa démission fait suite à un examen minutieux de ses communications passées avec le délinquant sexuel condamné Jeffrey Epstein. Cette décision marque un retrait significatif de Summers de ses rôles publics face à une critique croissante.

Larry Summers has resigned from the board of OpenAI, as reported by The New York Times. His resignation follows scrutiny over his past communications with convicted sex offender Jeffrey Epstein. This decision marks a significant step in Summers' withdrawal from public roles amid growing criticism.

Larry Summers Resigns From OpenAI’s Board

Vous utilisez les Conventional Commits et vous commencez à apprécier la clarté de l’historique Git (si ce n’est pas le cas, <a href="https://dev.to/jpiazzal/conventional-commits-un-premier-pas-vers-lautomatisation-3l5d">voici un article pour le découvrir</a>).

Mais vous continuez à gérer vos versions à la main, écrire le changelog, créer les tags, publier les nouvelles versions…

Et si tout ça se faisait tout seul ? C’est la promesse de <a href="https://github.com/semantic-release/semantic-release" rel="noopener noreferrer">semantic-release</a>.

<blockquote>
Par défaut, semantic-release est étroitement lié à l’écosystème npm, puisqu’il est développé en JavaScript et pensé à l’origine pour les projets Node.js mais certains plugins peuvent exister dans d’autres contextes.
</blockquote>

<h2>
 
 
 Pourquoi semantic-release ?
</h2>

Versionner à la main peut être une source d’erreurs multiple :

<ul>
<li>on oublie d’incrémenter le numéro de version</li>
<li>on modifie le changelog une fois sur deux</li>
<li>on ne sait plus si on doit passer en <code>1.4.2</code> ou <code>1.5.0</code>
</li>
<li>et parfois on publie sans tag</li>
</ul>

semantic-release résout tout ça automatiquement en s’appuyant sur les messages de commit (suivant la norme Conventional Commits).

En clair : vous écrivez votre code, vous faites vos commits correctement, vous mergez et le reste se fait tout seul.

<h2>
 
 
 Comment ça fonctionne ?
</h2>

Tout part de l’analyse des commits depuis la dernière version publiée (ex : <code>1.0.0</code>) :

<ul>
<li>un <code>fix:</code> déclenche une version patch (ex : <code>1.0.1</code>)</li>
<li>un <code>feat:</code> déclenche une version mineure (ex : <code>1.1.0</code>)</li>
<li>un <code>!</code> ou un <code>BREAKING CHANGE</code> déclenche une version majeure (ex : <code>2.0.0</code>)</li>
</ul>

Une fois la version déterminée, semantic-release :

<ol>
<li>met à jour le changelog automatiquement</li>
<li>crée un tag Git</li>
<li>et publie la nouvelle version (sur npm, GitHub, GitLab, etc.)</li>
</ol>

Chaque étapes peut être supprimée ou configurée.

En somme : un cycle de release entièrement piloté par votre historique Git.

<h2>
 
 
 L’écosystème autour de semantic-release
</h2>

semantic-release ne travaille pas seul. Il s’intègre parfaitement avec :

<ul>
<li>
commitlint et Husky, pour vérifier la structure des commits</li>
<li>
GitHub Actions, GitLab CI, ou n’importe quelle pipeline CI/CD</li>
</ul>

Une fois en place, il s’impose comme un maillon essentiel du processus d’intégration continue.

<h2>
 
 
 Mise en place
</h2>

Si vous souhaitez l'intégrer sur votre projet, <a href="https://semantic-release.gitbook.io/semantic-release/usage/getting-started" rel="noopener noreferrer">la documentation officielle</a> est relativement bien faite.

<h2>
 
 
 En résumé
</h2>

Avec semantic-release, vous éliminez toute la partie fastidieuse du versioning et vous rendez votre cycle de release fiable, prévisible et sans effort manuel.

En un mot : publie sans y penser.

يتناول المقال فوائد استخدام semantic-release، وهي أداة مصممة لأتمتة إدارة الإصدارات في تطوير البرمجيات. يبرز التحديات المرتبطة بالنسخ اليدوي، والتي يمكن أن تؤدي إلى أخطاء متعددة، ويؤكد على الوضوح الذي توفره Conventional Commits في تاريخ Git. تم تطوير semantic-release في الأصل لمشاريع Node.js بلغة JavaScript، وتعد بتبسيط العملية من خلال التعامل تلقائيًا مع زيادة الإصدارات، وتوليد سجلات التغييرات، ونشر الإصدارات الجديدة، مما يقلل من احتمال حدوث أخطاء بشرية.

El artículo discute los beneficios de usar semantic-release, una herramienta diseñada para automatizar la gestión de versiones en el desarrollo de software. Destaca los desafíos del versionado manual, que pueden llevar a múltiples errores, y enfatiza la claridad proporcionada por los Conventional Commits en el historial de Git. Semantic-release, desarrollado originalmente para proyectos de Node.js en JavaScript, promete simplificar el proceso al manejar automáticamente los incrementos de versión, la generación de changelogs y la publicación de nuevas versiones, reduciendo así el potencial de error humano.

L'article traite des avantages de l'utilisation de semantic-release, un outil conçu pour automatiser la gestion des versions dans le développement logiciel. Il met en lumière les défis du versionnement manuel, qui peuvent entraîner de multiples erreurs, et souligne la clarté apportée par les Conventional Commits dans l'historique Git. Semantic-release, développé à l'origine pour les projets Node.js en JavaScript, promet de rationaliser le processus en gérant automatiquement les incréments de version, la génération de changelogs et la publication de nouvelles versions, réduisant ainsi le potentiel d'erreur humaine.

The article discusses the benefits of using semantic-release, a tool designed to automate version management in software development. It highlights the challenges of manual versioning, which can lead to multiple errors, and emphasizes the clarity provided by Conventional Commits in Git history. Semantic-release, originally developed for Node.js projects in JavaScript, promises to streamline the process by automatically handling version increments, changelog generation, and publishing new releases, thus reducing the potential for human error.

semantic-release : quand la CI gère nos versions à notre place

Introduction 
The problem: Context management in AI workflows is broken. Personal frustration: I was spending 60 seconds explaining every single bug to Claude or Copilot. This is adharma—it breaks your flow and drains your cognitive load.

The Inspiration 
Vedantic philosophy: विस्मृति भी विद्या है ("Forgetting too is knowledge")

Fuzzy-Trace Theory: Human memory relies on "gists" (semantic meaning), not "verbatim" (perfect recall).

Neuroscience: Synaptic pruning (forgetting) is what makes learning possible.

The Architecture 
Three-Kosha memory system (inspired by Vedantic philosophy)

Salience classification (tagging what's important)

Gist extraction (compressing the "noise")

Python bridge + VS Code integration

The Results 
We ran a real-world Flask debugging test. The results are not a hypothesis; they are a fact.

90% time savings (A 60-second manual workflow → 5 seconds)

59% token reduction (A 210-token conversation → a one-shot 85-token prompt)

95.6/100 integration score

See the 5-second demo here: <a href="https://www.youtube.com/watch?v=gV4sPkl5qns" rel="noopener noreferrer">https://www.youtube.com/watch?v=gV4sPkl5qns</a>

The Journey 
From research paper to production.

Challenges: Cross-platform Python, auto-detecting secrets to maintain privacy.

Wins: A zero-config installation that is local-first and privacy-first by default.

The Future 
v0.2.0: Memory Ledger UI (to see what Vidurai remembers)

v1.0.0: ML-based salience classification

v2.0.0: Multi-IDE support

Try It 
Vidurai is free, open-source, and available now.

Install: ext install vidurai.vidurai

Marketplace: <a href="https://marketplace.visualstudio.com/items?itemName=vidurai.vidurai" rel="noopener noreferrer">https://marketplace.visualstudio.com/items?itemName=vidurai.vidurai</a>

GitHub (MIT): <a href="https://github.com/chandantochandan/vidurai" rel="noopener noreferrer">https://github.com/chandantochandan/vidurai</a>

Feedback is our Dharma 
v0.1.0 is alpha. Please join the sangha and help build the future of AI-human collaboration.

🐞 Bugs: <a href="https://github.com/chandantochandan/vidurai/issues" rel="noopener noreferrer">https://github.com/chandantochandan/vidurai/issues</a>

💡 Features: Introduction 
The problem: Context management in AI workflows is broken. Personal frustration: I was spending 60 seconds explaining every single bug to Claude or Copilot. This is adharma—it breaks your flow and drains your cognitive load.

The Inspiration 
Vedantic philosophy: विस्मृति भी विद्या है ("Forgetting too is knowledge")

Fuzzy-Trace Theory: Human memory relies on "gists" (semantic meaning), not "verbatim" (perfect recall).

Neuroscience: Synaptic pruning (forgetting) is what makes learning possible.

The Architecture 
Three-Kosha memory system (inspired by Vedantic philosophy)

Salience classification (tagging what's important)

Gist extraction (compressing the "noise")

Python bridge + VS Code integration

The Results 
We ran a real-world Flask debugging test. The results are not a hypothesis; they are a fact.

90% time savings (A 60-second manual workflow → 5 seconds)

59% token reduction (A 210-token conversation → a one-shot 85-token prompt)

95.6/100 integration score

See the 5-second demo here: <a href="https://www.youtube.com/watch?v=gV4sPkl5qns" rel="noopener noreferrer">https://www.youtube.com/watch?v=gV4sPkl5qns</a>

The Journey 
From research paper to production.

Challenges: Cross-platform Python, auto-detecting secrets to maintain privacy.

Wins: A zero-config installation that is local-first and privacy-first by default.

The Future 
v0.2.0: Memory Ledger UI (to see what Vidurai remembers)

v1.0.0: ML-based salience classification

v2.0.0: Multi-IDE support

Try It 
Vidurai is free, open-source, and available now.

Install: ext install vidurai.vidurai

Marketplace: <a href="https://marketplace.visualstudio.com/items?itemName=vidurai.vidurai" rel="noopener noreferrer">https://marketplace.visualstudio.com/items?itemName=vidurai.vidurai</a>

GitHub (MIT): <a href="https://github.com/chandantochandan/vidurai" rel="noopener noreferrer">https://github.com/chandantochandan/vidurai</a>

Feedback is our Dharma 
v0.1.0 is alpha. Please join the sangha and help build the future of AI-human collaboration.

🐞 Bugs: <a href="https://github.com/chandantochandan/vidurai-vscode-extension/issues" rel="noopener noreferrer">https://github.com/chandantochandan/vidurai-vscode-extension/issues</a>

💡 Features: <a href="https://github.com/chandantochandan/vidurai-vscode-extension/discussions" rel="noopener noreferrer">https://github.com/chandantochandan/vidurai-vscode-extension/discussions</a>

💬 Community: <a href="https://discord.gg/DHdgS8eA" rel="noopener noreferrer">https://discord.gg/DHdgS8eA</a>

Conclusion 
Ancient wisdom has modern applications. Privacy and AI can coexist. Philosophy matters in technology. जय विदुराई! 🕉️

💬 Community: <a href="https://discord.gg/DHdgS8eA" rel="noopener noreferrer">https://discord.gg/DHdgS8eA</a>

Conclusion 
Ancient wisdom has modern applications. Privacy and AI can coexist. Philosophy matters in technology. जय विदुराई! 🕉️

يتناول المقال إنشاء فيدوراي، أداة مصممة لتحسين إدارة السياق في سير العمل الخاص بالذكاء الاصطناعي. يشارك المؤلف إحباطاته الشخصية مع الأنظمة الحالية، مشيرًا إلى أن شرح الأخطاء للمساعدين الذكيين مثل كلود أو كوبايلت كان يستغرق وقتًا طويلاً. مستلهمًا من الفلسفة الفيدانتية ونظرية فوزي-ترايس، طور المؤلف نظام ذاكرة ثلاثي الكوشا يعزز الكفاءة. أظهرت نتائج الاختبارات في العالم الحقيقي توفيرًا بنسبة 90% في الوقت المستغرق في سير العمل وتقليصًا بنسبة 59% في استخدام الرموز، مما يثبت فعالية هذا النهج المبتكر.

El artículo trata sobre la creación de Vidurai, una herramienta diseñada para mejorar la gestión del contexto en los flujos de trabajo de IA. El autor comparte sus frustraciones personales con los sistemas existentes, señalando que explicar errores a asistentes de IA como Claude o Copilot era muy lento. Inspirándose en la filosofía vedántica y la teoría del fuzzy-trace, el autor desarrolló un sistema de memoria de tres kosha que mejora la eficiencia. Los resultados de las pruebas en el mundo real mostraron una reducción del 90% en el tiempo dedicado a los flujos de trabajo y una disminución de…

L'article traite de la création de Vidurai, un outil conçu pour améliorer la gestion du contexte dans les flux de travail de l'IA. L'auteur partage ses frustrations personnelles avec les systèmes existants, notant que l'explication des bogues aux assistants IA comme Claude ou Copilot était chronophage. S'inspirant de la philosophie védantique et de la théorie du fuzzy-trace, l'auteur a développé un système de mémoire à trois kosha qui améliore l'efficacité. Les résultats des tests en conditions réelles ont montré une réduction de 90 % du temps passé sur les flux de travail et une diminution de…

The article discusses the creation of Vidurai, a tool designed to improve context management in AI workflows. The author shares personal frustrations with existing systems, noting that explaining bugs to AI assistants like Claude or Copilot was time-consuming. Drawing inspiration from Vedantic philosophy and fuzzy-trace theory, the author developed a three-kosha memory system that enhances efficiency. The results of real-world testing showed a 90% reduction in time spent on workflows and a 59% decrease in token usage, demonstrating the effectiveness of this innovative approach.

How I Built Vidurai: When Ancient Philosophy Meets Modern AI

<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

Nuxt Tutorial 1 - First Steps

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