Another day in late 2025, another impressive result from a Chinese company in open source artificial intelligence.Chinese social networking company <a href="https://huggingface.co/WeiboAI/VibeThinker-1.5B">Weibo&#x27;s AI division recently released its open source VibeThinker-1.5B</a>—a 1.5 billion parameter large language model (LLM) that is a fine-tuned variant of rival Chinese tech firm <a href="https://huggingface.co/Qwen/Qwen2.5-Math-1.5B">Alibaba&#x27;s Qwen2.5-Math-1.5B. </a>It&#x27;s available now for free download and usage by researchers and enterprise developers—even for commercial purposes—under a permissive MIT License on <a href="https://huggingface.co/WeiboAI/VibeThinker-1.5B">Hugging Face</a>, <a href="https://github.com/WeiboAI/VibeThinker">GitHub</a> and <a href="https://modelscope.cn/models/WeiboAI/VibeThinker-1.5B">ModelScope</a>, with a <a href="https://arxiv.org/pdf/2511.06221">technical report</a> on open access science publishing site arxiv.org.And yet, despite its compact size, VibeThinker-1.5B achieves benchmark-topping reasoning performance on math and code tasks, rivaling or surpassing models hundreds of times its size, even outperforming Chinese rival DeepSeek&#x27;s famed R1 that went viral at the start of this year—a 671-billion parameter model—on formal reasoning benchmark.It further eclipses Mistral AI&#x27;s Magistral Medium and holds its own against Anthropic&#x27;s Claude Opus 4 and OpenAI&#x27;s gpt-oss-20B Medium, all while requiring a fraction of the infrastructure and investment.It also does so having been post-trained on a budget of merely $7800 USD for compute resources (3900 GPU hours on Nvidia H800s) — far less than the tens, or even hundreds, of thousands of dollars typically required to fine-tune models of similar or larger scale.Recall this is not the total cost of the model&#x27;s development, however: LLMs are trained in stages. First comes pre-training, when the model learns basic language structure and general knowledge by predicting the next word across enormous amounts of text from the internet, books, and articles. This gives it fluency but not much sense of how to follow instructions or hold a conversationPost-training comes next, using much smaller, higher-quality datasets—typically collections of example questions, prompts, and expert-written answers—to teach the model how to respond helpfully, reason through problems, and align with human expectations. Still, Weibo&#x27;s post-training cost effectiveness on VibeThinker-1.5B is noteworthy and should be commended.The open-source release upends assumptions about parameter scale, compute intensity, and the minimum viable size for high-performance LLMs.<h3>A Different Training Approach: Spectrum-to-Signal</h3>VibeThinker-1.5B owes its performance not to scale, but to the training framework behind it: the Spectrum-to-Signal Principle (SSP).Instead of optimizing a model purely for single-answer correctness (Pass@1), the SSP framework decouples supervised fine-tuning (SFT) and reinforcement learning (RL) into two distinct phases with different goals:<ul><li>SFT (“Spectrum Phase”): The model is trained to maximize diversity across potential correct answers, improving its Pass@K score. This builds a wide range of plausible solution paths.</li><li>RL (“Signal Phase”): A second-stage reinforcement learning system (called MaxEnt-Guided Policy Optimization, or MGPO) is used to identify and amplify the most correct paths from this diverse solution pool. MGPO prioritizes problems where the model is most uncertain, using entropy-based weighting to focus learning.</li></ul>The authors argue this separation allows small models to explore reasoning space more effectively—achieving signal amplification without relying on massive parameter counts.VibeThinker-1.5B makes a compelling case that the industry’s reliance on parameter scaling as the only route to better reasoning performance may be outdated. By adopting a diversity-first training pipeline, WeiboAI has shown that smaller, more accessible models can match and even outperform billion-dollar systems in logic-heavy tasks.The low resource footprint is among the most significant aspects of VibeThinker-1.5B. At under $8,000, the post-training cost is 30–60x lower than models like DeepSeek R1 and MiniMax-M1, which cost between $294K and $535K to train.<h3>Performance Across Domains</h3>Despite its small size, VibeThinker-1.5B delivers cross-domain reasoning that outpaces many larger open-source and commercial models:<table><tbody><tr><td>Model</td><td>AIME25</td><td>LiveCodeBench v6</td><td>GPQA-Diamond</td></tr><tr><td>VibeThinker-1.5B</td><td>74.4</td><td>51.1</td><td>46.7</td></tr><tr><td>GPT-OSS-20B-Medium</td><td>72.1</td><td>54.9</td><td>66.0</td></tr><tr><td>Claude Opus 4</td><td>69.2</td><td>56.6</td><td>79.6</td></tr><tr><td>MiniMax M1 (456B)</td><td>74.6</td><td>62.3</td><td>69.2</td></tr><tr><td>DeepSeek R1 (671B)</td><td>70.0</td><td>65.9</td><td>71.5</td></tr><tr><td>Kimi K2 (1.09T)</td><td>49.5</td><td>53.7</td><td>75.1</td></tr></tbody></table>VibeThinker was benchmarked against both reasoning-centric models (Magistral, Claude, OpenAI o3-mini) and non-reasoning LLMs (GPT-4.1, Kimi K2, DeepSeek V3). Across structured reasoning benchmarks, the model consistently outperformed non-reasoning models, regardless of size:<ul><li>On AIME24 (math), it beat Kimi K2 (1.09T) by over 10 points (80.3 vs. 69.6).</li><li>On LiveCodeBench v6, it surpassed Claude Opus 4 (51.1 vs. 47.4).</li><li>On GPQA, it scored below GPT-4.1 and Claude, but still doubled its base model (from 16.4 to 46.7).</li></ul>This supports the authors’ claim that size is not the only path to reasoning capability—with proper training design, smaller models can reach or even exceed the performance of far larger systems in targeted tasks.Notably, it achieves parity with models hundreds of times larger on math and code, though it lags behind in general knowledge reasoning (GPQA), where larger models maintain an edge.This suggests a potential specialization trade-off: while VibeThinker excels at structured logical tasks, it has less capacity for wide-ranging encyclopedic recall, a known limitation of smaller architectures.<h3>Guidance for Enterprise Adoption</h3>The release includes recommended inference settings (temperature = 0.6, top_p = 0.95, max tokens = 40960).The model is small enough to be deployed on edge devices, including mobile phones and vehicle-embedded systems, while inference costs are estimated to be 20–70x cheaper than with large models.This positions VibeThinker-1.5B not just as a research achievement, but as a potential foundation for cost-efficient, locally deployable reasoning systems.<h3>Weibo’s Strategy and Market Position</h3>Weibo, launched by Sina Corporation in 2009, remains a cornerstone of China’s social media ecosystem. Often described as China’s version of X (formerly Twitter), the platform blends microblogging, multimedia content, and trending-topic features with a regulatory environment shaped by tight government oversight. Despite counting 600 million monthly active users (more than twice that of X), <a href="https://m.aastocks.com/en/stocks/analysis/stock-aafn-con/9898/HK6/NOW.1483101/hk-stock-news">investors are not optimistic about its advertising revenue growth potential</a> in the near term, and Weibo is navigating intensifying competition from video-first platforms like Douyin, which are drawing younger users and increasing time-spent elsewhere. In response, Weibo has leaned into creator-economy monetization, live-streaming, and vertical video—adding tools for influencer engagement, e-commerce integration, and richer analytics for brands.The platform’s role as a digital public square also makes it a focus of regulatory scrutiny. Chinese authorities continue to apply pressure on issues ranging from content governance to data security. In September 2025, <a href="https://www.reuters.com/technology/chinas-internet-regulator-issues-warnings-kuaishou-weibo-over-content-violations-2025-09-20/">Weibo was among the platforms cited in official warnings</a>, highlighting its ongoing exposure to policy risks.Weibo’s push into AI R&amp;D—exemplified by the release of VibeThinker-1.5B—signals a shift in ambition. Beyond being a media platform, Weibo is positioning itself as a player in the next phase of Chinese AI development, using its capital reserves, user behavior data, and in-house research capacity to pursue adjacent technical domains.<h3>What It Means for Enterprise Technical Decision Makers</h3>For engineering leaders and enterprise AI teams, VibeThinker’s release has practical implications for everything from orchestration pipelines to cost modeling. A 1.5B-parameter model that outperforms 100x larger models on math and programming tasks doesn’t just save compute—it shifts the architectural balance. It enables LLM inference on constrained infrastructure, reduces latency at the edge, and lowers the barrier to entry for applications that otherwise would have required API access to closed, frontier-scale models.That matters for enterprise ML leads trying to deploy reasoning-capable agents within existing systems, or for platform owners tasked with integrating LLMs into automated workflows. It also speaks to those running reinforcement learning from human feedback (RLHF) pipelines or managing inference optimization across hybrid cloud environments. The model’s post-training methodology—particularly its entropy-targeted reinforcement learning approach—offers a roadmap for teams looking to refine smaller checkpoints instead of relying on large-scale pretraining.VibeThinker’s benchmark transparency and data decontamination steps also address another emerging priority in enterprise AI: auditability. While its performance on general-knowledge tests still trails large frontier models, its task-specific reliability makes it an attractive candidate for controlled environments where correctness matters more than coverage.In short, VibeThinker-1.5B isn’t just a research milestone—it’s a strong candidate for practical enterprise use, deployment and learnings. It suggests that a new class of compact, reasoning-optimized models is viable for enterprise use cases that were previously the domain of far larger systems. For organizations trying to balance cost, latency, interpretability, and control, it’s a good new option to the long, growing list of Chinese open source offerings.

أطلقت ويبو نموذجها المفتوح المصدر للذكاء الاصطناعي، VibeThinker-1.5B، الذي يتفوق على DeepSeek-R1 من علي بابا على الرغم من كونه أصغر بكثير. هذا النموذج متاح الآن للتنزيل المجاني بموجب ترخيص MIT، ويظهر قدرات استدلال استثنائية في المهام الرياضية والترميز، مما يمثل تقدمًا كبيرًا في تكنولوجيا الذكاء الاصطناعي وإمكانية الوصول إليها للباحثين والمطورين.

Weibo ha lanzado su modelo de IA de código abierto, VibeThinker-1.5B, que supera al DeepSeek-R1 de Alibaba a pesar de ser significativamente más pequeño. Este modelo, disponible de forma gratuita bajo una licencia MIT, muestra habilidades excepcionales de razonamiento en tareas matemáticas y de codificación, marcando un avance significativo en la tecnología de IA y su accesibilidad para investigadores y desarrolladores.

Weibo a lancé son modèle d'IA open-source, VibeThinker-1.5B, qui surpasse le DeepSeek-R1 d'Alibaba malgré sa taille beaucoup plus petite. Ce modèle, disponible gratuitement sous une licence MIT, démontre des capacités de raisonnement exceptionnelles dans les tâches mathématiques et de codage, marquant une avancée significative dans la technologie et l'accessibilité de l'IA pour les chercheurs et les développeurs.

Weibo has launched its open-source AI model, VibeThinker-1.5B, which outperforms Alibaba's DeepSeek-R1 despite being significantly smaller. This model, available for free under an MIT License, showcases exceptional reasoning abilities in math and coding tasks, marking a significant advancement in AI technology and accessibility for researchers and developers.

Weibo's new open source AI model VibeThinker-1.5B outperforms DeepSeek-R1 on $7,800 post-training budget

Deutsche Bank AG has refinanced part of a $350 million loan originally extended by Mizuho Bank Ltd. to fund Oyo Hotels founder Ritesh Agarwal’s purchase of shares in the company, according to people familiar with the matter.

قامت دويتشه بنك AG بإعادة تمويل جزء من قرض بقيمة 350 مليون دولار تم منحه في الأصل من قبل بنك ميزوهو المحدود. كان هذا القرض يهدف إلى دعم ريتش أغاروال، مؤسس فنادق أوي، في شراء أسهم في الشركة. تشير إعادة التمويل هذه إلى تغيير في الدعم المالي لاستثمار أغاروال في أوي، مما يعكس المناورات المالية الجارية في قطاع الضيافة.

Deutsche Bank AG ha refinanciado parte de un préstamo de 350 millones de dólares que fue originalmente otorgado por Mizuho Bank Ltd. Este préstamo tenía como objetivo apoyar a Ritesh Agarwal, el fundador de Oyo Hotels, en la adquisición de acciones de la empresa. La refinanciación indica un cambio en el apoyo financiero para la inversión de Agarwal en Oyo, reflejando las maniobras financieras en curso dentro del sector hotelero.

Deutsche Bank AG a refinancé une partie d'un prêt de 350 millions de dollars initialement accordé par Mizuho Bank Ltd. Ce prêt visait à soutenir Ritesh Agarwal, le fondateur d'Oyo Hotels, dans l'acquisition d'actions de l'entreprise. Ce refinancement indique un changement dans le soutien financier pour l'investissement d'Agarwal dans Oyo, reflétant les manœuvres financières en cours dans le secteur de l'hôtellerie.

Deutsche Bank AG has refinanced a portion of a $350 million loan that was initially provided by Mizuho Bank Ltd. This loan was intended to support Ritesh Agarwal, the founder of Oyo Hotels, in acquiring shares in the company. The refinancing indicates a shift in financial support for Agarwal's investment in Oyo, reflecting the ongoing financial maneuvers within the hospitality sector.

Deutsche Bank Said to Refinance Part of $350 Million Oyo Loan

شهدت شركة كلاودفلير انقطاعًا كبيرًا في الخدمة يوم 18 نوفمبر 2025، والذي تأكد أنه لم يكن ناتجًا عن هجوم إلكتروني. تسبب هذا الحادث في تعطيل الوصول إلى العديد من المواقع والخدمات عبر الإنترنت، مما أثار مخاوف بشأن موثوقية خدمات السحابة. أفاد المستخدمون بأنهم واجهوا رسائل خطأ أثناء محاولة الوصول إلى منصات مختلفة، بما في ذلك منصات رئيسية مثل X وChatGPT. سلط الانقطاع الضوء على الثغرات التي يمكن أن تؤثر حتى على مزودي البنية التحتية للإنترنت الراسخين.

Cloudflare experimentó una interrupción significativa el 18 de noviembre de 2025, que se confirmó no fue causada por un ciberataque. El incidente interrumpió el acceso a numerosos sitios web y servicios en línea, lo que generó preocupaciones sobre la fiabilidad de los servicios en la nube. Los usuarios informaron haber encontrado mensajes de error al intentar acceder a varias plataformas, incluidas las principales como X y ChatGPT. La interrupción destacó las vulnerabilidades que pueden afectar incluso a los proveedores de infraestructura de Internet bien establecidos.

Cloudflare a connu une panne significative le 18 novembre 2025, qui n'a pas été causée par une cyberattaque. Cet incident a perturbé l'accès à de nombreux sites Web et services en ligne, soulevant des inquiétudes quant à la fiabilité des services cloud. Les utilisateurs ont signalé des messages d'erreur en essayant d'accéder à diverses plateformes, y compris des grandes comme X et ChatGPT. Cette panne a mis en lumière les vulnérabilités qui peuvent affecter même les fournisseurs d'infrastructure Internet bien établis.

Cloudflare experienced a significant outage on November 18, 2025, which was confirmed not to be caused by a cyber attack. The incident disrupted access to numerous websites and online services, raising concerns about the reliability of cloud services. Users reported encountering error messages while trying to access various platforms, including major ones like X and ChatGPT. The outage has highlighted the vulnerabilities that can affect even well-established internet infrastructure providers.

Cloudflare outage was not caused by a cyber attack

Wall Street will get a sense of where the billions of dollars being spent on artificial intelligence are going when Nvidia Corp. reports its earnings after the bell on Wednesday. How the sinking stock market will react is another question.

من المقرر أن تعلن شركة إنفيديا عن أرباحها يوم الأربعاء، مما يوفر رؤى حول الاستثمارات الكبيرة في الذكاء الاصطناعي. تبقى ردود فعل السوق، خاصةً في ظل الانخفاض الأخير، غير مؤكدة. تراقب وول ستريت عن كثب كيف ستعكس هذه الأرباح القطاع التكنولوجي الأوسع في ظل المخاوف المتزايدة بشأن الإنفاق على الذكاء الاصطناعي.

Nvidia Corp. está programada para informar sus ganancias el miércoles, proporcionando información sobre las significativas inversiones en inteligencia artificial. La reacción del mercado, especialmente dado el reciente descenso, sigue siendo incierta. Wall Street está observando de cerca cómo estos resultados reflejarán el sector tecnológico más amplio en medio de crecientes preocupaciones sobre el gasto en IA.

Nvidia Corp. doit publier ses résultats mercredi, offrant un aperçu des investissements considérables dans l'intelligence artificielle. La réaction du marché, en particulier compte tenu de la récente baisse, reste incertaine. Wall Street surveille de près comment ces résultats refléteront le secteur technologique plus large face aux préoccupations croissantes concernant les dépenses en IA.

Nvidia Corp. is set to report its earnings on Wednesday, providing insights into the significant investments being made in artificial intelligence. The market's reaction, particularly given the recent downturn, remains uncertain. Wall Street is closely monitoring how these earnings will reflect on the broader tech sector amidst growing concerns about AI spending.

Nvidia Earnings Run Into a Market Suddenly Afraid Of AI Spending

Investors include Nvidia and the Kuwait Investment Authority.

تقوم شركة بروكفيلد بجمع 10 مليارات دولار لإنشاء صندوق جديد للبنية التحتية للذكاء الاصطناعي. وقد جذبت هذه المبادرة اهتمامًا كبيرًا من مستثمرين رئيسيين، بما في ذلك شركة إنفيديا والهيئة العامة للاستثمار الكويتية. يسلط هذا التحرك الضوء على الطلب المتزايد على الاستثمار في تقنيات الذكاء الاصطناعي والبنية التحتية، مما يعكس اتجاهًا أوسع في قطاع التكنولوجيا.

Brookfield está en proceso de recaudar 10 mil millones de dólares para establecer un nuevo fondo de infraestructura de inteligencia artificial. La iniciativa ha atraído un interés significativo de importantes inversores, incluidos Nvidia y la Autoridad de Inversión de Kuwait. Este movimiento destaca la creciente demanda de inversión en tecnologías e infraestructuras de inteligencia artificial, reflejando una tendencia más amplia en el sector tecnológico.

Brookfield est en train de lever 10 milliards de dollars pour établir un nouveau fonds d'infrastructure en intelligence artificielle. Cette initiative a suscité un intérêt considérable de la part d'investisseurs majeurs, notamment Nvidia et l'Autorité d'investissement du Koweït. Ce mouvement met en lumière la demande croissante d'investissements dans les technologies et infrastructures d'intelligence artificielle, reflétant une tendance plus large dans le secteur technologique.

Brookfield is in the process of raising $10 billion to establish a new AI infrastructure fund. The initiative has attracted significant interest from major investors, including Nvidia and the Kuwait Investment Authority. This move highlights the growing demand for investment in artificial intelligence technologies and infrastructure, reflecting a broader trend in the technology sector.

Brookfield Is Raising $10 Billion for New AI Infrastructure Fund

أعلنت تيك توك عن ميزة جديدة تتيح للمستخدمين تقليل كمية المحتوى الذي يتم إنشاؤه بواسطة الذكاء الاصطناعي في خلاصة أخبارهم. تهدف هذه التحديثات إلى تحسين تجربة المستخدم من خلال منح الأفراد مزيدًا من التحكم في نوع المحتوى الذي يتفاعلون معه. تعكس هذه الخطوة التزام تيك توك بمعالجة تفضيلات واهتمامات المستخدمين بشأن الوجود المتزايد للذكاء الاصطناعي في وسائل التواصل الاجتماعي.

TikTok ha anunciado una nueva función que permite a los usuarios reducir la cantidad de contenido generado por IA que aparece en sus feeds. Esta actualización tiene como objetivo mejorar la experiencia del usuario al dar a las personas más control sobre el tipo de contenido con el que interactúan. La decisión refleja el compromiso de TikTok de abordar las preferencias y preocupaciones de los usuarios sobre la creciente presencia de la IA en las redes sociales.

TikTok a annoncé une nouvelle fonctionnalité permettant aux utilisateurs de réduire la quantité de contenu généré par l'IA apparaissant dans leurs fils d'actualité. Cette mise à jour vise à améliorer l'expérience utilisateur en offrant aux individus un meilleur contrôle sur le type de contenu avec lequel ils interagissent. Cette décision reflète l'engagement de TikTok à répondre aux préférences et préoccupations des utilisateurs concernant la présence croissante de l'IA sur les réseaux sociaux.

TikTok has announced a new feature that allows users to reduce the amount of AI-generated content appearing in their feeds. This update aims to enhance user experience by giving individuals more control over the type of content they engage with. The decision reflects TikTok's commitment to addressing user preferences and concerns regarding the increasing presence of AI in social media.

TikTok will let users tone down the amount of AI content in their feed

Algebris Investments’ founder and chief executive officer warned investors to reduce allocation to the world’s top technology companies as he made a bearish case for AI.

حذر الرئيس التنفيذي لشركة ألجبريس للاستثمار المستثمرين من تقليل استثماراتهم في الشركات التكنولوجية الكبرى، مشيرًا إلى مخاوف بشأن تصحيح كبير محتمل في قطاع الذكاء الاصطناعي. تعكس هذه النظرة السلبية شكوكًا متزايدة بشأن استدامة تقييمات الذكاء الاصطناعي في ظل زيادة تقلبات السوق.

El CEO de Algebris Investments ha advertido a los inversores que reduzcan su asignación a las principales empresas tecnológicas del mundo, argumentando un caso pesimista para la IA. Esta perspectiva negativa refleja un creciente escepticismo sobre la sostenibilidad de las valoraciones de la IA en medio de una mayor volatilidad del mercado.

Le PDG d'Algebris Investments a conseillé aux investisseurs de réduire leurs investissements dans les grandes entreprises technologiques, citant des préoccupations concernant une correction significative potentielle dans le secteur de l'IA. Ce point de vue pessimiste reflète un scepticisme croissant quant à la durabilité des évaluations de l'IA face à une volatilité accrue du marché.

The CEO of Algebris Investments has advised investors to decrease their investments in leading technology firms, citing concerns about a potential significant correction in the AI sector. This bearish outlook reflects a growing skepticism regarding the sustainability of AI valuations amidst increasing market volatility.

Algebris CEO Warns of ‘Significant’ Correction for Big AI Stocks

<a href="https://news.ycombinator.com/item?id=45944296">Comments</a>

مقالة حديثة على Hacker News تنتقد ورقة نشرتها شركة أنثروبيك، مشيرة إلى أنها تفتقر إلى المصداقية. عنوان المقال يعبر عن الشك في جودة التقرير، مما يدل على شعور سلبي تجاه المحتوى الذي تنتجه الشركة. هذا التعليق يعكس مخاوف أوسع بشأن موثوقية نتائج البحث في قطاع التكنولوجيا، وخاصة تلك المتعلقة بالذكاء الاصطناعي.

Un artículo reciente en Hacker News critica un documento publicado por Anthropic, sugiriendo que carece de credibilidad. El título del artículo expresa escepticismo sobre la calidad del informe, indicando un sentimiento negativo hacia el contenido producido por la empresa. Este comentario refleja preocupaciones más amplias sobre la fiabilidad de los resultados de investigación en el sector tecnológico, especialmente aquellos relacionados con la inteligencia artificial.

Un article récent sur Hacker News critique un document publié par Anthropic, suggérant qu'il manque de crédibilité. Le titre de l'article exprime un scepticisme quant à la qualité du rapport, indiquant un sentiment négatif envers le contenu produit par l'entreprise. Ce commentaire reflète des préoccupations plus larges concernant la fiabilité des résultats de recherche dans le secteur technologique, en particulier ceux liés à l'intelligence artificielle.

A recent article on Hacker News critiques a paper published by Anthropic, suggesting that it lacks credibility. The title of the article expresses skepticism about the quality of the report, indicating a negative sentiment towards the content produced by the company. This commentary reflects broader concerns about the reliability of research outputs in the technology sector, particularly those related to artificial intelligence.

Anthropic’s paper smells like bullshit

In what appeared to be a bid to soak up some of Google&#x27;s limelight prior to the <a href="https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and">launch of its new Gemini 3 flagship AI model </a>— now recorded as the most powerful LLM in the world by multiple independent evaluators — Elon Musk&#x27;s rival AI startup xAI last night unveiled its newest large language model, <a href="https://x.ai/news/grok-4-1">Grok 4.1.</a>The model is now live for consumer use on Grok.com, social network X (formerly Twitter), and the company’s iOS and Android mobile apps, and it arrives with major architectural and usability enhancements, among them: faster reasoning, improved emotional intelligence, and significantly reduced hallucination rates. xAI also commendably published a white paper on its evaluations and including a small bit on training process <a href="https://data.x.ai/2025-11-17-grok-4-1-model-card.pdf">here</a>. Across public benchmarks, Grok 4.1 has vaulted to the top of the leaderboard, outperforming rival models from Anthropic, OpenAI, and Google — at least, Google&#x27;s pre-Gemini 3 model (Gemini 2.5 Pro). It builds upon the success of xAI&#x27;s Grok-4 Fast, which <a href="https://venturebeat.com/ai/what-to-know-about-grok-4-fast-for-enterprise-use-cases">VentureBeat covered favorably</a> shortly following its release back in September 2025.However, enterprise developers looking to integrate the new and improved model Grok 4.1 into production environments will find one major constraint: it&#x27;s not yet available through <a href="https://docs.x.ai/docs/models?cluster=us-east-1#detailed-pricing-for-all-grok-models">xAI’s public API</a>. Despite its high benchmarks, Grok 4.1 remains confined to xAI’s consumer-facing interfaces, with no announced timeline for API exposure. At present, only older models—including Grok 4 Fast (reasoning and non-reasoning variants), Grok 4 0709, and legacy models such as Grok 3, Grok 3 Mini, and Grok 2 Vision—are available for programmatic use via the xAI developer API. These support up to 2 million tokens of context, with token pricing ranging from $0.20 to $3.00 per million depending on the configuration.For now, this limits Grok 4.1’s utility in enterprise workflows that rely on backend integration, fine-tuned agentic pipelines, or scalable internal tooling. While the consumer rollout positions Grok 4.1 as the most capable LLM in xAI’s portfolio, production deployments in enterprise environments remain on hold.<h3>Model Design and Deployment Strategy</h3>Grok 4.1 arrives in two configurations: a fast-response, low-latency mode for immediate replies, and a “thinking” mode that engages in multi-step reasoning before producing output. Both versions are live for end users and are selectable via the model picker in xAI’s apps.The two configurations differ not just in latency but also in how deeply the model processes prompts. Grok 4.1 Thinking leverages internal planning and deliberation mechanisms, while the standard version prioritizes speed. Despite the difference in architecture, both scored higher than any competing models in blind preference and benchmark testing.<h3>Leading the Field in Human and Expert Evaluation</h3>On the <a href="https://lmarena.ai/leaderboard/text">LMArena Text Arena leaderboard</a>, Grok 4.1 Thinking briefly held the top position with a normalized Elo score of 1483 — then was dethroned a few hours later with <a href="https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and">Google&#x27;s release of Gemini 3 </a>and its incredible 1501 Elo score. The non-thinking version of Grok 4.1 also fares well on the index, however, at 1465. These scores place Grok 4.1 above Google’s Gemini 2.5 Pro, Anthropic’s Claude 4.5 series, and OpenAI’s GPT-4.5 preview.In creative writing, Grok 4.1 ranks second only to Polaris Alpha (an early GPT-5.1 variant), with the “thinking” model earning a score of 1721.9 on the Creative Writing v3 benchmark. This marks a roughly 600-point improvement over previous Grok iterations. Similarly, in the Arena Expert leaderboard, which aggregates feedback from professional reviewers, Grok 4.1 Thinking again leads the field with a score of 1510.The gains are especially notable given that Grok 4.1 was released only two months after Grok 4 Fast, highlighting the accelerated development pace at xAI.<h3>Core Improvements Over Previous Generations</h3>Technically, Grok 4.1 represents a significant leap in real-world usability. Visual capabilities—previously limited in Grok 4—have been upgraded to enable robust image and video understanding, including chart analysis and OCR-level text extraction. Multimodal reliability was a pain point in prior versions and has now been addressed.Token-level latency has been reduced by approximately 28 percent while preserving reasoning depth. In long-context tasks, Grok 4.1 maintains coherent output up to 1 million tokens, improving on Grok 4’s tendency to degrade past the 300,000 token mark.xAI has also improved the model&#x27;s tool orchestration capabilities. Grok 4.1 can now plan and execute multiple external tools in parallel, reducing the number of interaction cycles required to complete multi-step queries. According to internal test logs, some research tasks that previously required four steps can now be completed in one or two.Other alignment improvements include better truth calibration—reducing the tendency to hedge or soften politically sensitive outputs—and more natural, human-like prosody in voice mode, with support for different speaking styles and accents.<h3>Safety and Adversarial Robustness</h3>As part of its risk management framework, xAI evaluated Grok 4.1 for refusal behavior, hallucination resistance, sycophancy, and dual-use safety.The hallucination rate in non-reasoning mode has dropped from 12.09 percent in Grok 4 Fast to just 4.22 percent — a roughly 65% improvement.The model also scored 2.97 percent on FActScore, a factual QA benchmark, down from 9.89 percent in earlier versions.In the domain of adversarial robustness, Grok 4.1 has been tested with prompt injection attacks, jailbreak prompts, and sensitive chemistry and biology queries. Safety filters showed low false negative rates, especially for restricted chemical knowledge (0.00 percent) and restricted biological queries (0.03 percent). The model’s ability to resist manipulation in persuasion benchmarks, such as MakeMeSay, also appears strong—it registered a 0 percent success rate as an attacker.<h3>Limited Enterprise Access via API</h3>Despite these gains, Grok 4.1 remains unavailable to enterprise users through xAI’s API. According to the company’s <a href="https://docs.x.ai/docs/models">public documentation</a>, the latest available models for developers are Grok 4 Fast (both reasoning and non-reasoning variants), each supporting up to 2 million tokens of context at pricing tiers ranging from $0.20 to $0.50 per million tokens. These are backed by a 4M tokens-per-minute throughput limit and 480 requests per minute (RPM) rate cap.By contrast, Grok 4.1 is accessible only through xAI’s consumer-facing properties—X, Grok.com, and the mobile apps. This means organizations cannot yet deploy Grok 4.1 via fine-tuned internal workflows, multi-agent chains, or real-time product integrations.<h3>Industry Reception and Next Steps</h3>The release has been met with strong public and industry feedback. Elon Musk, founder of xAI, posted a brief endorsement, calling it “a great model” and congratulating the team. AI benchmark platforms have praised the leap in usability and linguistic nuance.For enterprise customers, however, the picture is more mixed. Grok 4.1’s performance represents a breakthrough for general-purpose and creative tasks, but until API access is enabled, it will remain a consumer-first product with limited enterprise applicability.As competitive models from OpenAI, Google, and Anthropic continue to evolve, xAI’s next strategic move may hinge on when—and how—it opens Grok 4.1 to external developers.

أطلقت شركة xAI التابعة لإيلون ماسك Grok 4.1، وهو نموذج لغوي كبير مصمم لتحسين تجربة المستخدم على Grok.com والشبكة الاجتماعية X. يتميز هذا النموذج الجديد بتحسينات مثل التفكير الأسرع، والذكاء العاطفي الأفضل، ومعدل هلاوس منخفض بشكل كبير. حاليًا، لا يوجد وصول لواجهة برمجة التطبيقات متاح للمطورين. يأتي هذا الإطلاق في الوقت الذي تستعد فيه جوجل للكشف عن نموذجها Gemini 3، الذي تم التعرف عليه كأقوى نموذج لغوي كبير من قبل مقيمين مستقلين.

La startup de Elon Musk, xAI, ha lanzado Grok 4.1, un modelo de lenguaje diseñado para mejorar la experiencia del usuario en Grok.com y en la red social X. Este nuevo modelo presenta mejoras como un razonamiento más rápido, mejor inteligencia emocional y una tasa de alucinaciones significativamente reducida. Actualmente, no hay acceso a API disponible para desarrolladores. Este lanzamiento se produce mientras Google se prepara para presentar su modelo Gemini 3, reconocido como el modelo de lenguaje más potente por evaluadores independientes.

La startup d'Elon Musk, xAI, a lancé Grok 4.1, un modèle de langage conçu pour améliorer l'expérience utilisateur sur Grok.com et le réseau social X. Ce nouveau modèle présente des améliorations telles qu'un raisonnement plus rapide, une meilleure intelligence émotionnelle et un taux de hallucination considérablement réduit. Actuellement, aucun accès API n'est disponible pour les développeurs. Ce lancement intervient alors que Google se prépare à dévoiler son modèle Gemini 3, reconnu comme le modèle de langage le plus puissant par des évaluateurs indépendants.

Elon Musk's AI startup xAI has launched Grok 4.1, a large language model designed to enhance user experience on Grok.com and the social network X. This new model features improvements such as faster reasoning, better emotional intelligence, and a significantly reduced hallucination rate. Currently, there is no API access available for developers. The launch comes as Google prepares to unveil its Gemini 3 model, which has been recognized as the most powerful large language model by independent evaluators.

Musk's xAI launches Grok 4.1 with lower hallucination rate on the web and apps — no API access (for now)

<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.

Weibo's new open source AI model VibeThinker-1.5B outperforms DeepSeek-R1 on $7,800 post-training budget

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