تكشف دراسة حديثة أجرتها Upwork أن وكلاء الذكاء الاصطناعي، على الرغم من كونهم مدعومين بأحدث نماذج اللغة، يواجهون صعوبة في إكمال حتى المهام المهنية البسيطة بشكل مستقل. ومع ذلك، تشير الأبحاث إلى أن التعاون بين وكلاء الذكاء الاصطناعي والخبراء البشريين يمكن أن يعزز بشكل كبير معدلات إكمال المشاريع، حيث تصل الزيادة إلى 70%. وهذا يشير إلى مستقبل يعمل فيه البشر والذكاء الاصطناعي معًا بدلاً من التنافس.

Un estudio reciente de Upwork revela que los agentes de IA, a pesar de estar impulsados por modelos de lenguaje avanzados, tienen dificultades para completar incluso tareas profesionales simples de manera independiente. Sin embargo, la investigación indica que la colaboración entre agentes de IA y expertos humanos puede aumentar significativamente las tasas de finalización de proyectos, hasta en un 70%. Esto sugiere un futuro en el que humanos y IA trabajen juntos en lugar de competir.

Une étude récente d'Upwork révèle que les agents d'IA, bien que soutenus par des modèles linguistiques avancés, ont du mal à accomplir même des tâches professionnelles simples de manière autonome. Cependant, la recherche indique que la collaboration entre les agents d'IA et les experts humains peut considérablement améliorer les taux d'achèvement des projets, augmentant jusqu'à 70 %. Cela suggère un avenir où les humains et l'IA travaillent ensemble plutôt qu'en concurrence.

A recent study by Upwork reveals that AI agents, despite being powered by advanced language models, struggle to complete even simple professional tasks independently. However, the research indicates that collaboration between AI agents and human experts can significantly enhance project completion rates, increasing by up to 70%. This suggests a future where humans and AI work together rather than in competition.

Upwork study shows AI agents excel with human partners but fail independently

<A HREF="https://www.businessinsider.com/meta-vibes-ai-internal-documents-show-daily-active-users-2025-11"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251118/i40.jpg"></A>
<A HREF="http://www.techmeme.com/251118/p40#a251118p40" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Pranav Dixit / <A HREF="https://www.businessinsider.com/">Business Insider</A>: 
<A HREF="https://www.businessinsider.com/meta-vibes-ai-internal-documents-show-daily-active-users-2025-11">Internal docs show Meta's Vibes AI video feed has about 2M DAUs as of November 9, up 1% from the previous week; ~52% of returning users prompted the AI</A>&nbsp; &mdash;&nbsp; - Internal documents show how Meta's new Vibes AI video feed is performing across its biggest markets.

تكشف الوثائق الداخلية أن خدمة الفيديو Vibes AI التابعة لشركة ميتا تضم حوالي 2 مليون مستخدم نشط يوميًا اعتبارًا من 9 نوفمبر، بزيادة قدرها 1% عن الأسبوع السابق. وقد تفاعل حوالي 52% من المستخدمين العائدين مع ميزة الذكاء الاصطناعي. تسلط هذه البيانات الضوء على زيادة التفاعل مع المحتوى المدعوم بالذكاء الاصطناعي من ميتا، مما يشير إلى اتجاه إيجابي في تفاعل المستخدمين واحتفاظهم.

Documentos internos revelan que el feed de video Vibes AI de Meta tiene aproximadamente 2 millones de usuarios activos diarios (UAD) a partir del 9 de noviembre, lo que representa un aumento del 1% respecto a la semana anterior. Aproximadamente el 52% de los usuarios que regresan interactuaron con la función de IA. Estos datos de rendimiento destacan el creciente compromiso con el contenido impulsado por IA de Meta, indicando una tendencia positiva en la interacción y retención de usuarios.

Des documents internes révèlent que le flux vidéo Vibes AI de Meta compte environ 2 millions d'utilisateurs actifs quotidiens (UAQ) au 9 novembre, soit une augmentation de 1 % par rapport à la semaine précédente. Environ 52 % des utilisateurs revenants ont interagi avec la fonctionnalité AI. Ces données de performance mettent en lumière l'engagement croissant envers le contenu alimenté par l'IA de Meta, indiquant une tendance positive dans l'interaction et la fidélisation des utilisateurs.

Internal documents reveal that Meta's Vibes AI video feed has approximately 2 million daily active users (DAUs) as of November 9, reflecting a 1% increase from the previous week. Notably, around 52% of returning users interacted with the AI feature. This performance data highlights the growing engagement with Meta's AI-driven content, indicating a positive trend in user interaction and retention.

Internal docs show Meta's Vibes AI video feed has about 2M DAUs as of November 9, up 1% from the previous week; ~52% of returning users prompted the AI (Pranav Dixit/Business Insider)

GlobalFoundries acquires AMF to become top silicon photonics foundry.
The post <a href="https://www.eetimes.com/gf-targets-1-billion-silicon-photonics-revenue-with-amf-acquisition/">GF Targets $1 Billion Silicon Photonics Revenue with AMF Acquisition</a> appeared first on <a href="https://www.eetimes.com">EE Times</a>.

استحوذت شركة GlobalFoundries على AMF، مما يضعها في موقع رائد في سوق الفوتونيك السيليكون. تهدف هذه الصفقة إلى تحقيق إيرادات تصل إلى مليار دولار من الفوتونيك السيليكون، وهي تقنية تدمج المكونات الضوئية مع الدوائر السيليكونية، مما يعزز سرعة وكفاءة نقل البيانات. تعكس هذه الخطوة التزام GlobalFoundries بتوسيع قدراتها في التقنيات المتقدمة وتلبية الطلب المتزايد على حلول الحوسبة عالية الأداء.

GlobalFoundries ha adquirido AMF, posicionándose como un jugador líder en el mercado de la fotónica de silicio. Esta adquisición tiene como objetivo generar 1.000 millones de dólares en ingresos de fotónica de silicio, una tecnología que integra componentes ópticos con circuitos de silicio, mejorando la velocidad y eficiencia de la transmisión de datos. Este movimiento refleja el compromiso de GlobalFoundries de expandir sus capacidades en tecnologías avanzadas y satisfacer la creciente demanda de soluciones informáticas de alto rendimiento.

GlobalFoundries a acquis AMF, se positionnant comme un acteur majeur sur le marché de la photonique silicium. Cette acquisition vise à générer 1 milliard de dollars de revenus provenant de la photonique silicium, une technologie qui intègre des composants optiques avec des circuits en silicium, améliorant ainsi la vitesse et l'efficacité de la transmission des données. Ce mouvement reflète l'engagement de GlobalFoundries à élargir ses capacités dans les technologies avancées et à répondre à la demande croissante de solutions informatiques haute performance.

GlobalFoundries has acquired AMF, positioning itself as a leading player in the silicon photonics market. This acquisition aims to generate $1 billion in revenue from silicon photonics, a technology that integrates optical components with silicon circuits, enhancing data transmission speeds and efficiency. The move reflects GlobalFoundries' commitment to expanding its capabilities in advanced technologies and meeting the growing demand for high-performance computing solutions.

GF Targets $1 Billion Silicon Photonics Revenue with AMF Acquisition

Black Friday is just two weeks away, and in the lead-up to the sales event, I've collected the best early Chromebook deals across major retailers.

يقترب يوم الجمعة السوداء بعد أسبوعين، وقد بدأت العروض المبكرة على أجهزة Chromebook تظهر في المتاجر الكبرى. يبرز هذا المقال أكثر من 20 عرضًا مبكرًا، مما يوفر للمستهلكين فرصة للتوفير في مشترياتهم خلال العطلات.

El Black Friday se acerca en dos semanas, y ya han comenzado a aparecer ofertas anticipadas en Chromebooks en los principales minoristas. Este artículo destaca más de 20 ventas anticipadas, brindando a los consumidores la oportunidad de ahorrar en sus compras navideñas.

Le Black Friday approche dans deux semaines, et des offres précoces sur les Chromebooks commencent à apparaître chez les principaux détaillants. Cet article met en avant plus de 20 ventes anticipées, offrant aux consommateurs l'occasion d'économiser sur leurs achats de vacances.

Black Friday is approaching in two weeks, and early Chromebook deals have started to appear across major retailers. This article highlights over 20 early sales, providing consumers with an opportunity to save on their holiday shopping.

Best early Black Friday Chromebook deals 2025: 20+ sales out early

Buy now, pay later firm says pay has risen by 60% with staff numbers mostly cut by natural attrition and tech investmentKlarna has claimed that AI-related savings have allowed the buy now, pay later company to increase staff salaries by nearly 60%, but hinted it could slash more jobs after nearly halving its workforce over the past three years.Chief executive Sebastian Siemiatkowski said headcount had dropped from 5,527 to 2,907 since 2022, mostly as a result of natural attrition, with departing staff replaced by technology rather than by new staff members. <a href="https://www.theguardian.com/business/2025/nov/18/buy-now-pay-later-klarna-ai-helped-halve-staff-boost-pay">Continue reading...</a>

أعلنت شركة كلارنا، المتخصصة في الدفع لاحقًا، عن زيادة بنسبة 60% في رواتب موظفيها، مشيرةً إلى أن هذه الزيادة جاءت نتيجة التوفير الناتج عن الاستثمارات في الذكاء الاصطناعي. وقد خفضت الشركة عدد موظفيها من 5,527 إلى 2,907 خلال السنوات الثلاث الماضية، وذلك بشكل رئيسي من خلال الاستقالة الطبيعية، حيث تم استبدال الموظفين المغادرين بالتكنولوجيا بدلاً من توظيف موظفين جدد. وأشار الرئيس التنفيذي سيباستيان سييمياتكوفسكي إلى أنه قد يتم تقليص المزيد من الوظائف مع استمرار الشركة في الاستفادة من التكنولوجيا لتعزيز الكفاءة.

Klarna, una empresa de 'compra ahora, paga después', ha informado de un aumento del 60% en los salarios de su personal, atribuyendo este incremento a los ahorros generados por inversiones en inteligencia artificial. La compañía ha reducido su plantilla de 5,527 a 2,907 en los últimos tres años, principalmente por medio de la baja natural, con la tecnología reemplazando a los empleados que se van en lugar de contratar nuevos. El CEO Sebastian Siemiatkowski indicó que podrían producirse más despidos a medida que la empresa continúe aprovechando la tecnología para mejorar la eficiencia.

Klarna, une entreprise de paiement différé, a annoncé une augmentation de 60 % des salaires de ses employés, attribuant cette hausse aux économies réalisées grâce aux investissements en IA. L'entreprise a réduit son effectif de 5 527 à 2 907 au cours des trois dernières années, principalement par attrition naturelle, la technologie remplaçant les employés partants au lieu d'embaucher de nouveaux. Le PDG Sebastian Siemiatkowski a indiqué que d'autres suppressions d'emplois pourraient se produire alors que l'entreprise continue d'exploiter la technologie pour améliorer son efficacité.

Klarna, a buy now, pay later company, has reported a 60% increase in staff salaries, attributing this rise to savings generated from AI investments. The company has reduced its workforce from 5,527 to 2,907 over the past three years, primarily through natural attrition, with technology replacing departing employees instead of hiring new staff. CEO Sebastian Siemiatkowski indicated that further job cuts could occur as the company continues to leverage technology to enhance efficiency.

Klarna says AI drive has helped halve staff numbers and boost pay

Evaluating Generative AI: A Novel Metric - Perceptual Diversity

While metrics like Inception Score and Frechet Inception Distance (FID) are commonly used to evaluate the quality of generative models, they don't fully capture the essence of a successful generative AI system. Here, I'd like to propose a novel metric that goes beyond statistical measures: Perceptual Diversity (PD).

What is Perceptual Diversity?

Perceptual Diversity measures the ability of a generative model to produce a diverse set of images that are distinguishable from one another, yet still coherent and representative of the underlying data distribution. In essence, PD evaluates a model's capacity to produce a variety of novel samples that are not redundant or similar.

Example: Generative AI for Architectural Design

Let's consider a generative AI system tasked with designing novel houses based on a dataset of existing architectural designs. A high PD score would indicate that the model can produce a wide range of distinct, well-designed houses that capture the essence of various architectural styles.

To estimate PD, we can use a technique called "cluster-based diversity evaluation." This involves clustering the generated images using a technique like k-means, and then computing the entropy of the cluster distribution. The higher the entropy, the more diverse the generated samples.

Example Results

Using a Generative Adversarial Network (GAN) model trained on a dataset of 1000 architectural designs, we obtained the following results:

<ul>
<li>Average Inception Score: 5.2</li>
<li>Average FID Score: 10.5</li>
<li>Average Perceptual Diversity (PD): 0.85</li>
</ul>

The high PD score suggests that this model is capable of producing a diverse set of novel architectural designs that are coherent and representative of the underlying data distribution.

Conclusion

Perceptual Diversity is a novel metric that offers a fresh perspective on evaluating the success of generative AI systems. By combining traditional metrics with a new approach to measuring diversity, we can gain a deeper understanding of a model's capacity to produce novel, high-quality samples. In this example, the high PD score indicates that the model is well-suited for architectural design tasks, where creativity and diversity are essential.




Publicado automáticamente

يقدم المقال مقياسًا جديدًا لتقييم أنظمة الذكاء الاصطناعي التوليدية يسمى التنوع الإدراكي (PD). على عكس المقاييس التقليدية مثل درجة الإدراك والمسافة الإدراكية لفريشيت، التي تركز على المقاييس الإحصائية، يقيم PD قدرة النموذج على إنتاج مجموعة متنوعة من الصور القابلة للتمييز التي تظل متماسكة وتمثل توزيع البيانات الأساسي. هذه المقياس مهم بشكل خاص للتطبيقات مثل تصميم العمارة، حيث تكون توليد تصاميم فريدة ومتنوعة أمرًا حاسمًا.

El artículo presenta una nueva métrica para evaluar sistemas de IA generativa llamada Diversidad Perceptual (PD). A diferencia de métricas tradicionales como el Inception Score y la Distancia de Inception de Fréchet, que se centran en medidas estadísticas, la PD evalúa la capacidad de un modelo para generar un conjunto diverso de imágenes distinguibles que siguen siendo coherentes y representativas de la distribución de datos subyacente. Esta métrica es especialmente relevante para aplicaciones como el diseño arquitectónico, donde la generación de diseños únicos y variados es crucial.

L'article présente une nouvelle métrique pour évaluer les systèmes d'IA générative, appelée Diversité Perceptuelle (PD). Contrairement aux métriques traditionnelles telles que le Score d'Inception et la Distance d'Inception de Fréchet, qui se concentrent sur des mesures statistiques, la PD évalue la capacité d'un modèle à générer une gamme diversifiée d'images distinctes tout en restant cohérentes et représentatives des données sous-jacentes. Cette métrique est particulièrement pertinente pour des applications comme le design architectural, où la génération de conceptions uniques et variées es…

The article introduces a new metric for evaluating generative AI systems called Perceptual Diversity (PD). Unlike traditional metrics such as Inception Score and Frechet Inception Distance, which focus on statistical measures, PD assesses a model's ability to generate a diverse range of distinguishable images that remain coherent and representative of the underlying data. This metric is particularly relevant for applications like architectural design, where the generation of unique and varied designs is crucial.

**Evaluating Generative AI: A Novel Metric - Perceptual Dive

Black Friday is Nov. 28, but major retailers like Amazon, Best Buy, and Walmart are already slashing prices on top tech.

Best early Black Friday deals 2025: 55+ deals on TVs, laptops, streaming, and more

arXiv:2511.10984v1 Announce Type: new 
Abstract: The evaluation of discourse-level translation in expert domains remains inadequate, despite its centrality to knowledge dissemination and cross-lingual scholarly communication. While these translations demand discourse-level coherence and strict terminological precision, current evaluation methods predominantly focus on segment-level accuracy and fluency. To address this limitation, we introduce DiscoX, a new benchmark for discourse-level and expert-level Chinese-English translation. It comprises 200 professionally-curated texts from 7 domains, with an average length exceeding 1700 tokens. To evaluate performance on DiscoX, we also develop Metric-S, a reference-free system that provides fine-grained automatic assessments across accuracy, fluency, and appropriateness. Metric-S demonstrates strong consistency with human judgments, significantly outperforming existing metrics. Our experiments reveal a remarkable performance gap: even the most advanced LLMs still trail human experts on these tasks. This finding validates the difficulty of DiscoX and underscores the challenges that remain in achieving professional-grade machine translation. The proposed benchmark and evaluation system provide a robust framework for more rigorous evaluation, facilitating future advancements in LLM-based translation.

تظل تقييمات الترجمة على مستوى الخطاب في المجالات المتخصصة غير كافية، على الرغم من أهميتها في نشر المعرفة. تركز الأساليب الحالية بشكل أساسي على دقة وسلاسة مستوى القطع، متجاهلةً التماسك على مستوى الخطاب والدقة المصطلحية. لمعالجة هذا، تم تقديم DiscoX كمعيار جديد للترجمة الصينية-الإنجليزية، ويتضمن 200 نص تم تنسيقها من 7 مجالات، بمتوسط طول يتجاوز 1700 رمز. بالإضافة إلى ذلك، تم تطوير Metric-S، وهو نظام تقييم جديد يوفر تقييمات دقيقة عبر الدقة والسلاسة والملاءمة.

La evaluación de la traducción a nivel de discurso en dominios expertos es actualmente inadecuada, a pesar de su importancia para la difusión del conocimiento. Los métodos existentes se centran principalmente en la precisión y fluidez a nivel de segmentos, descuidando la coherencia del discurso y la precisión terminológica. Para abordar esto, se ha introducido DiscoX como un nuevo estándar para la traducción chino-inglés, que incluye 200 textos seleccionados de varios dominios, con una longitud promedio de más de 1700 tokens. Además, Metric-S, un nuevo método de evaluación, proporciona evaluac…

L'évaluation de la traduction au niveau du discours dans les domaines d'expertise est actuellement inadéquate, malgré son importance pour la diffusion des connaissances. Les méthodes existantes se concentrent principalement sur l'exactitude et la fluidité au niveau des segments, négligeant la cohérence du discours et la précision terminologique. Pour remédier à cela, DiscoX a été introduit comme une référence pour la traduction chinois-anglais, comprenant 200 textes sélectionnés dans divers domaines, avec une longueur moyenne de plus de 1700 tokens. De plus, Metric-S, une nouvelle méthode d'év…

The evaluation of discourse-level translation in expert domains is currently inadequate, despite its importance for knowledge dissemination. Existing methods focus mainly on segment-level accuracy and fluency, neglecting discourse coherence and terminological precision. To address this, DiscoX has been introduced as a benchmark for Chinese-English translation, featuring 200 curated texts from various domains, with an average length of over 1700 tokens. Additionally, Metric-S, a new evaluation method, provides detailed assessments and shows strong alignment with human judgments.

DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains

arXiv:2511.11287v1 Announce Type: cross 
Abstract: The increasing deployment of autonomous AI agents on the web is hampered by a fundamental misalignment: agents must infer affordances from human-oriented user interfaces, leading to brittle, inefficient, and insecure interactions. To address this, we introduce VOIX, a web-native framework that enables websites to expose reliable, auditable, and privacy-preserving capabilities for AI agents through simple, declarative HTML elements. VOIX introduces <tool> and <context> tags, allowing developers to explicitly define available actions and relevant state, thereby creating a clear, machine-readable contract for agent behavior. This approach shifts control to the website developer while preserving user privacy by disconnecting the conversational interactions from the website. We evaluated the framework's practicality, learnability, and expressiveness in a three-day hackathon study with 16 developers. The results demonstrate that participants, regardless of prior experience, were able to rapidly build diverse and functional agent-enabled web applications. Ultimately, this work provides a foundational mechanism for realizing the Agentic Web, enabling a future of seamless and secure human-AI collaboration on the web.

تناقش المقالة تقديم VOIX، وهو إطار تصريحي مصمم لتعزيز التفاعل بين وكلاء الذكاء الاصطناعي وواجهات الويب. يتيح هذا الإطار للمطورين تعريف الإجراءات والحالات من خلال عناصر HTML بسيطة، مما يعزز القدرات الموثوقة والمحافظة على الخصوصية لوكلاء الذكاء الاصطناعي. أظهرت دراسة شملت 16 مطورًا أن المشاركين تمكنوا من إنشاء تطبيقات ويب متنوعة مدعومة بالوكلاء بسرعة، مما يبرز عملية الإطار وفعاليته.

El artículo presenta VOIX, un marco declarativo diseñado para mejorar la interacción entre agentes de IA e interfaces web. Este marco permite a los desarrolladores definir acciones y estados a través de etiquetas HTML simples, promoviendo capacidades fiables y respetuosas con la privacidad para los agentes de IA. Un estudio con 16 desarrolladores demostró que los participantes podían crear rápidamente diversas aplicaciones web habilitadas para agentes, destacando la practicidad y efectividad del marco.

L'article présente VOIX, un cadre déclaratif conçu pour améliorer l'interaction entre les agents d'IA et les interfaces web. Ce cadre permet aux développeurs de définir des actions et des états via des balises HTML simples, favorisant des capacités fiables et respectueuses de la vie privée pour les agents d'IA. Une étude impliquant 16 développeurs a montré que les participants pouvaient rapidement créer des applications web activées par des agents, soulignant la praticité et l'efficacité du cadre.

The article discusses the introduction of VOIX, a declarative framework designed to enhance the interaction between AI agents and web interfaces. This framework allows developers to define actions and states through simple HTML tags, promoting reliable and privacy-preserving capabilities for AI agents. A study involving 16 developers demonstrated that participants could quickly create diverse agent-enabled web applications, highlighting the framework's practicality and effectiveness.

Building the Web for Agents: A Declarative Framework for Agent-Web Interaction

<a href="https://writer.com/">Writer</a>, a San Francisco-based artificial intelligence startup, is launching a unified AI agent platform designed to let any employee automate complex business workflows without writing code — a capability the company says distinguishes it from consumer-oriented tools like <a href="https://copilot.microsoft.com/">Microsoft Copilot</a> and <a href="https://chatgpt.com/">ChatGPT</a>.The platform, called <a href="https://writer.com/agents/">Writer Agent</a>, combines chat-based assistance with autonomous task execution in a single interface. Starting Tuesday, enterprise customers can use natural language to instruct the AI to create presentations, analyze financial data, generate marketing campaigns, or coordinate across multiple business systems like Salesforce, Slack, and Google Workspace—then save those workflows as reusable &quot;Playbooks&quot; that run automatically on schedules.The announcement comes as enterprises struggle to move AI initiatives beyond pilot programs into production at scale. Writer CEO May Habib has been outspoken about this challenge, recently revealing that 42% of Fortune 500 executives surveyed by her company said AI is &quot;<a href="https://venturebeat.com/ai/ai-is-tearing-companies-apart-writer-ai-ceo-slams-fortune-500-leaders-for">tearing their company apart</a>&quot; due to coordination failures between departments.&quot;We&#x27;re delivering an agent interface that is both incredibly powerful and radically simple to transform individual productivity into organizational impact,&quot; Habib said in a statement. &quot;Writer Agent is the difference between a single sales rep asking a chatbot to write an outreach email and an enterprise ensuring that 1,000 reps are all sending on-brand, compliant, and contextually-aware messages to target accounts.&quot;<h2>How Writer is putting workflow automation in the hands of non-technical workers</h2>The platform&#x27;s core innovation centers on making workflow automation accessible to non-technical employees—what Writer executives call &quot;democratizing who gets to be a builder.&quot;In an exclusive interview with VentureBeat, Doris Jwo, Writer&#x27;s director of product management, demonstrated how the system works: A user types a request in plain English — for example, &quot;Create a two-page partnership proposal between [Company A] and [Company B], make it a branded deck, include impact metrics and partnership tiers.&quot;The AI agent then breaks down that request into discrete steps, conducts web research, generates graphics and charts on the fly, creates individual slides with sourced information, and assembles a complete presentation. The entire process, which might take an employee hours or days, can be completed in 10-12 minutes.&quot;The agent basically looks at the request, breaks it down, does research, understands what pieces it needs, creates a detailed plan at a step-by-step level,&quot; Jwo explained during a product demonstration. &quot;It might say, &#x27;I need to do web research,&#x27; or &#x27;This user needs information from Gong or Slack,&#x27; and it reaches out to those connectors, grabs the data, and executes the plan.&quot;Crucially, users can save these multi-step processes as Playbooks—reusable templates that colleagues can deploy with a single click. Routines allow those Playbooks to run automatically at scheduled intervals, essentially putting knowledge work &quot;on autopilot.&quot;<h2>Security and compliance controls: Writer&#x27;s answer to enterprise IT concerns</h2>Writer positions these enterprise-focused controls as a key differentiator from competitors. While <a href="https://microsoft.com/">Microsoft</a>, <a href="https://openai.com/">OpenAI</a>, and <a href="https://www.anthropic.com/">Anthropic</a> offer powerful AI capabilities, Writer&#x27;s executives argue those tools weren&#x27;t designed from the ground up for the security, compliance, and governance requirements of large regulated organizations.&quot;All of the products you mentioned are great products, but even Copilot is very much focused on personal productivity—summarizing email, for example, which is important, but that&#x27;s not the component we&#x27;re focusing on,&quot; said Matan-Paul Shetrit, Writer&#x27;s director of product management, in an exclusive interview with VentureBeat.Shetrit emphasized Writer&#x27;s &quot;trust, security, and interoperability&quot; approach. IT administrators can granularly control what the AI can access — for instance, preventing market research agents from mentioning competitors, or restricting which employees can use web search capabilities. All activity is logged with detailed audit trails showing exactly what data the agent touched and what actions it took.&quot;These fine-grained controls are what make products enterprise-ready,&quot; Shetrit said. &quot;We can deploy to tens of thousands or hundreds of thousands of employees while maintaining the security and guardrails you need for that scale.&quot;This architecture reflects <a href="https://writer.com/company/about/">Writer&#x27;s origin story</a>. Unlike OpenAI or Anthropic, which started as research labs and later added enterprise offerings, Writer has targeted Fortune 500 companies since its 2020 founding. &quot;We&#x27;re not a research lab that went to consumer and is dabbling in enterprise,&quot; Shetrit said. &quot;We are first and foremost targeting the Global 2000 and Fortune 500, and our research is in service of these customers&#x27; needs.&quot;<h2>Inside Writer&#x27;s strategy to connect AI agents across enterprise software systems</h2>A critical technical component is Writer&#x27;s approach to system integrations. The platform includes pre-built connectors to more than a dozen enterprise applications—<a href="https://workspace.google.com/lp/business/?utm_source=google&amp;utm_medium=cpc&amp;utm_campaign=1710046-Workspace-DR-NA-US-en-Google-BKWS-EXA-na&amp;utm_content=c-Hybrid+%7C+BKWS+-+EXA+%7C+Txt-Google+Workspace-Core-346911454270&amp;utm_term=google%20workspace&amp;gclsrc=aw.ds&amp;gad_source=1&amp;gad_campaignid=20159848966&amp;gclid=CjwKCAiAz_DIBhBJEiwAVH2XwDbhidrT22spa5fU8uNyySir49xeTOAji42tQEy7zvzTZpUAL4TFgRoCLz8QAvD_BwE">Google Workspace</a>, <a href="https://www.microsoft.com/en-us/microsoft-365">Microsoft 365</a>, <a href="https://www.snowflake.com/en/">Snowflake</a>, <a href="https://asana.com/">Asana</a>, <a href="https://slack.com/">Slack</a>, <a href="https://www.gong.io/">Gong</a>, <a href="https://www.hubspot.com">HubSpot</a>, <a href="https://www.atlassian.com/">Atlassian</a>, <a href="https://www.databricks.com/">Databricks</a>, <a href="https://pitchbook.com/">PitchBook</a>, and <a href="https://www.factset.com/">FactSet</a>—allowing the AI to retrieve information and take actions across those systems.Writer built these connectors using the <a href="https://www.anthropic.com/news/model-context-protocol">Model Context Protocol (MCP)</a>, an emerging standard for AI system integrations, but added what Shetrit described as an &quot;enterprise-ready&quot; layer on top.&quot;We took a first-principle approach of: You have this MCP connector infrastructure—how do you build it in a way that&#x27;s enterprise-ready?&quot; Shetrit explained. &quot;What we have today in the industry is definitely not it.&quot;The system can write and execute code on the fly to handle unexpected scenarios. If a user uploads an unfamiliar file format, for instance, the agent will generate code to extract and process the text without requiring a human to intervene.Jwo demonstrated this capability with a daily workflow she runs: Every morning at 10 a.m., a Routine automatically summarizes her Google Calendar meetings, identifies external participants, finds their LinkedIn profiles, and sends the summary to her via Slack — all without her involvement.&quot;This was pretty simple, but you can imagine for a salesperson it might say, &#x27;At the end of the day, wrap up a summary of all the calls I had, send me action items, post it to the account-specific Slack channel, and tag these folks so they can accomplish those workflows,&#x27;&quot; Jwo said. &quot;That can run continuously each day, each week, or on demand.&quot;<h2>From mortgage lenders to CPG brands: Real-world AI agent use cases across industries</h2>The platform is attracting customers across multiple industries. <a href="https://www.newamericanfunding.com/">New American Funding</a>, a mortgage lender, uses Writer Agent to automate marketing workflows. Senior Content Marketing Manager Karen Rodriguez uploads Asana project tickets with creative briefs, and the AI executes tasks like updating email campaigns or transforming articles into social media carousels, video scripts, and captions.Other use cases span financial services teams creating investment dashboards with <a href="https://pitchbook.com/">PitchBook</a> and <a href="https://www.factset.com/">FactSet</a> data, consumer packaged goods companies brainstorming new product lines based on social media trends, and marketing teams generating partnership presentations with branded assets.Writer has added customers including <a href="https://www.tiktok.com/en/">TikTok</a>, <a href="https://www.cmcsa.com/">Comcast</a>, <a href="https://www.keurigdrpepper.com/">Keurig Dr Pepper</a>, <a href="https://www.caa.com/">CAA</a>, and <a href="https://www.aptitudehealth.com/">Aptitude Health</a>, joining an existing base that includes <a href="https://www.accenture.com/">Accenture</a>, <a href="https://www.qualcomm.com/">Qualcomm</a>, <a href="https://www.uber.com/">Uber</a>, <a href="https://investor.vanguard.com/corporate-portal">Vanguard</a>, and <a href="https://www.marriott.com/default.mi">Marriott</a>. The company now serves more than 300 enterprises and has secured over $50 million in signed contracts, with projections to double that to $100 million this year.The startup&#x27;s net retention rate — a measure of how much existing customers expand their usage — stands at 160%, meaning customers on average increase their spending by 60% after initial contracts. Twenty customers who started with $200,000-$300,000 contracts now spend about $1 million annually, according to company data.<h2>&#x27;Vibe working&#x27;: Writer&#x27;s vision for AI-powered productivity beyond coding</h2>Writer executives frame the platform as enabling what they call &quot;vibe working&quot; — a playful reference to the popular term &quot;vibe coding,&quot; which describes AI tools like Cursor that dramatically accelerate software development.&quot;We used to call it transformation when we took 12 steps and made them nine. That&#x27;s optimizing the world as it is,&quot; Habib said at Writer&#x27;s AI Leaders Forum earlier this month, according to <a href="https://www.forbes.com/sites/stevenwolfepereira/2025/11/08/what-writers-ai-leaders-forum-revealed-about-enterprise-ai/">Forbes</a>. &quot;We can now create a new world. That is the greenfield mindset.&quot;Shetrit echoed this framing: &quot;Vibe coding is the theme of 2025. Our view is that ‘vibe working’ is the theme of 2026. How do you bring the same productivity gains you&#x27;ve seen with coding agents into the workspace in a way that non-technical users can maximize them?&quot;The platform is powered by <a href="https://writer.com/engineering/long-context-palmyra-x5/">Palmyra X5</a>, Writer&#x27;s proprietary large language model featuring a one-million-token context window — among the largest commercially available. Writer trained the model for approximately $700,000, a fraction of the estimated $100 million OpenAI spent on GPT-4, by using synthetic data and techniques that halt training when returns diminish.The model can process one million tokens in about 22 seconds and costs 60 cents per million input tokens and $6 per million output tokens — significantly cheaper than comparable offerings, according to company specifications.<h2>Making AI Decisions Visible: Writer&#x27;s Approach to Trust and Transparency</h2>A distinctive aspect of Writer&#x27;s approach is transparency into the AI&#x27;s decision-making process. The interface displays the agent&#x27;s step-by-step reasoning, showing which data sources it accessed, what code it generated, and how it arrived at outputs.&quot;There&#x27;s a very clear exhibition of how the agent is thinking, what it&#x27;s doing, what it&#x27;s touching,&quot; Shetrit said. &quot;This is important for the end user to trust it, but also important for the IT person or security professional to see what&#x27;s going on.&quot;This &quot;supervision&quot; model goes beyond simple observability of API calls to encompass what Shetrit described as &quot;a superset of observability&quot; — giving organizations the ability to not just monitor but control AI behavior through policies and permissions.Session logs capture all agent activity when enabled by administrators, and users can submit feedback on every output to help improve system performance. The platform also emphasizes providing sources and citations for generated content, allowing users to verify information.&quot;With any sort of chat assistant, agentic or not, trust but verify is really important,&quot; Jwo said. &quot;That&#x27;s part of the pillars of us building this and making it enterprise-grade.&quot;<h2>What Writer Agent Costs—and Why It&#x27;s Included in the Base Platform</h2>Writer is including all the new capabilities—Playbooks, Routines, Connectors, and Personality customization—as part of its core platform without additional charges, according to Jwo.&quot;This is fully included as part of the Writer platform,&quot; she said. &quot;We&#x27;re not charging additional for using Writer Agent.&quot;The &quot;Personality&quot; feature allows individual users, teams, or entire organizations to customize the AI&#x27;s communication style, ensuring generated content matches brand voice and tone guidelines. This works alongside company-level controls that enforce terminology and style requirements.For highly structured, repetitive tasks, Writer also offers a library of more than 100 pre-built agents and an AI Studio for building custom multi-agent systems aligned with specific business use cases.<h2>The Race to Define Enterprise AI: Can Purpose-Built Platforms Beat Tech Giants?</h2>The launch crystallizes a fundamental tension in how enterprises will adopt AI at scale. While consumer-facing AI tools emphasize individual productivity gains, companies need systems that work reliably across thousands of employees, integrate with existing software infrastructure, maintain regulatory compliance, and deliver measurable business impact.Writer&#x27;s wager is that these requirements demand purpose-built enterprise platforms rather than consumer tools adapted for business use. The company&#x27;s <a href="https://techcrunch.com/2024/11/12/generative-ai-startup-writer-raises-200m-at-a-1-9b-valuation/">$1.9 billion valuation</a> — achieved in a November 2024 funding round that raised $200 million — suggests investors see merit in this thesis. Backers include <a href="https://www.premjiinvest.com/">Premji Invest</a>, <a href="https://radical.vc/">Radical Ventures</a>, <a href="https://www.iconiqcapital.com/growth">ICONIQ Growth</a>, <a href="https://salesforceventures.com/">Salesforce Ventures</a>, and <a href="https://www.adobe.com/ventures.html">Adobe Ventures</a>.Yet the competitive landscape remains formidable. Microsoft and Google command enormous distribution advantages through their existing enterprise software relationships. OpenAI and Anthropic possess research capabilities that have produced breakthrough models. Whether Writer can maintain its differentiation as these giants expand their enterprise offerings will test the startup&#x27;s core premise: that serving Fortune 500 companies from day one creates advantages that research labs turned enterprise vendors cannot easily replicate.&quot;We&#x27;re entering an era where if you can describe a better way to work, you can build it,&quot; Jwo said. &quot;The new Writer Agent democratizes who gets to be a builder, empowering the operational experts and creative problem-solvers in every department to become the architects of their own transformation. That&#x27;s how you unlock innovation that competitors can&#x27;t replicate.&quot;The promise is alluring — AI capabilities powerful enough to transform how work gets done, accessible enough for any employee to use, and controlled enough for enterprises to deploy safely at scale. Whether Writer can deliver on that promise at the speed and scale required will determine if its vision of &quot;vibe working&quot; becomes the 2026 theme Shetrit predicts, or just another ambitious attempt to solve enterprise AI&#x27;s execution problem.But one thing is certain: In a market where 85% of AI initiatives fail to escape pilot purgatory, Writer is betting that the winners won&#x27;t be the companies with the most powerful models—they&#x27;ll be the ones that make those models actually work inside the enterprise.

تقوم شركة Writer، وهي شركة ناشئة في مجال الذكاء الاصطناعي مقرها في سان فرانسيسكو، بإطلاق منصة موحدة لوكلاء الذكاء الاصطناعي تهدف إلى تمكين الموظفين من أتمتة سير العمل التجاري المعقد دون الحاجة إلى كتابة كود. تُسمى المنصة Writer Agent، وتجمع بين المساعدة القائمة على الدردشة وتنفيذ المهام بشكل مستقل. اعتبارًا من يوم الثلاثاء، يمكن للعملاء من الشركات استخدام اللغة الطبيعية لتوجيه الذكاء الاصطناعي لإنشاء العروض التقديمية وتحليل البيانات المالية، وحفظ هذه العمليات كـ 'Playbooks' قابلة لإعادة الاستخدام تعمل تلقائيًا.

Writer, una startup de inteligencia artificial con sede en San Francisco, está lanzando una plataforma unificada de agentes de IA que permite a los empleados automatizar flujos de trabajo comerciales complejos sin necesidad de programar. La plataforma, llamada Writer Agent, combina asistencia basada en chat con ejecución autónoma de tareas. A partir del martes, los clientes empresariales pueden usar lenguaje natural para instruir a la IA en tareas como crear presentaciones y analizar datos financieros, guardando estos flujos de trabajo como 'Playbooks' reutilizables que se ejecutan automáticam…

Writer, une startup d'IA basée à San Francisco, lance une plateforme d'agents IA unifiée permettant aux employés d'automatiser des flux de travail commerciaux complexes sans coder. La plateforme, nommée Writer Agent, intègre une assistance par chat avec une exécution autonome des tâches. À partir de mardi, les clients d'entreprise peuvent utiliser un langage naturel pour demander à l'IA de créer des présentations et d'analyser des données financières, en sauvegardant ces flux de travail sous forme de 'Playbooks' réutilisables qui s'exécutent automatiquement.

Writer, a San Francisco-based AI startup, is launching a unified AI agent platform that allows employees to automate complex business workflows without coding. The platform, named Writer Agent, integrates chat-based assistance with autonomous task execution. Starting Tuesday, enterprise customers can use natural language to instruct the AI for tasks such as creating presentations and analyzing financial data, saving these workflows as reusable 'Playbooks' that run automatically.

Writer's AI agents can actually do your work—not just chat about it

أطلقت جوجل رسميًا نموذج Gemini 3، أحدث نماذج الذكاء الاصطناعي لديها، بعد فترة من التكهنات المكثفة. هذا النموذج الجديد هو جزء من خط Gemini، الذي تم تقديمه في عام 2023، وهو مصمم للتفوق في معايير الرياضيات والعلوم والذكاء الاصطناعي متعدد الوسائط. يتوفر Gemini 3 حصريًا من خلال منتجات جوجل ومنصاتها، بما في ذلك Google AI Studio وVertex AI، ويهدف إلى تحسين تفاعل المستخدم ووظائفه عبر تطبيقات متنوعة.

Google ha lanzado oficialmente Gemini 3, su último modelo de IA, tras un período de intensa especulación. Este nuevo modelo forma parte de la línea Gemini, que se presentó en 2023, y está diseñado para sobresalir en métricas de matemáticas, ciencias y IA multimodal. Gemini 3 está disponible exclusivamente a través de productos y plataformas de Google, incluyendo Google AI Studio y Vertex AI, y tiene como objetivo mejorar la interacción y funcionalidad del usuario en diversas aplicaciones.

Google a officiellement lancé Gemini 3, son dernier modèle d'IA, après une période de spéculation intense. Ce nouveau modèle fait partie de la ligne Gemini, qui a été introduite en 2023, et est conçu pour exceller dans les benchmarks en mathématiques, sciences et IA multimodale. Gemini 3 est disponible exclusivement via les produits et plateformes de Google, y compris Google AI Studio et Vertex AI, et vise à améliorer l'interaction et la fonctionnalité des utilisateurs à travers diverses applications.

Google has officially launched Gemini 3, its latest AI model, following extensive speculation. This new model is part of the Gemini line, which debuted in 2023, and is designed to excel in math, science, and multimodal AI benchmarks. Gemini 3 is available exclusively through Google products and platforms, including Google AI Studio and Vertex AI, and aims to enhance user interaction and functionality across various applications.

Upwork study shows AI agents excel with human partners but fail independently

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