#Meta cloud API

[smsgatewayindia]

Start Your Own Business with SMPPCenter’s WhatsApp Business API Software

Discover how SMPPCenter’s innovative WhatsApp Business API software can help you start your own business. Seamlessly connect with the Meta cloud API, offer multi-channel support, and utilize our reseller white label program. Request a demo today!

[virtualrealitynewstoday]

Microsoft與Meta合作推出Windows Volumetric Apps API：開啟3D空間互動新時代

微軟宣布推出一項名為Windows Volumetric Apps的新API，這是一種“將 Windows 應用程式擴展到 3D 空間”的方法。 微軟與Meta的合作進入新階段，雙方致力於使Windows在Quest裝置上提供一流的體驗。利用Quest的獨特功能，Windows應用將能夠延伸至3D空間，為用戶創造出更加沉浸與互動的使用環境。 這一新API將作為Windows應用的擴展存在，當應用透過即將推出的微軟專為Quest設計的Windows應用進行流式傳輸時，用戶可以在Meta Quest頭戴顯示器的虛擬顯示器上，訪問位於本地網絡的實體PC或Windows 365 Cloud PC。 開發人員今天可以註冊開發人員預覽版，以獲得對「volumetric API」的存取權。 在Meta Connect…

[govindhtech]

How To Use Llama 3.1 405B FP16 LLM On Google Kubernetes

How to set up and use large open models for multi-host generation AI over GKE

Access to open models is more important than ever for developers as generative AI grows rapidly due to developments in LLMs (Large Language Models). Open models are pre-trained foundational LLMs that are accessible to the general population. Data scientists, machine learning engineers, and application developers already have easy access to open models through platforms like Hugging Face, Kaggle, and Google Cloud’s Vertex AI.

How to use Llama 3.1 405B

Google is announcing today the ability to install and run open models like Llama 3.1 405B FP16 LLM over GKE (Google Kubernetes Engine), as some of these models demand robust infrastructure and deployment capabilities. With 405 billion parameters, Llama 3.1, published by Meta, shows notable gains in general knowledge, reasoning skills, and coding ability. To store and compute 405 billion parameters at FP (floating point) 16 precision, the model needs more than 750GB of GPU RAM for inference. The difficulty of deploying and serving such big models is lessened by the GKE method discussed in this article.

Customer Experience

You may locate the Llama 3.1 LLM as a Google Cloud customer by selecting the Llama 3.1 model tile in Vertex AI Model Garden.

Once the deploy button has been clicked, you can choose the Llama 3.1 405B FP16 model and select GKE.Image credit to Google Cloud

The automatically generated Kubernetes yaml and comprehensive deployment and serving instructions for Llama 3.1 405B FP16 are available on this page.

Deployment and servicing multiple hosts

Llama 3.1 405B FP16 LLM has significant deployment and service problems and demands over 750 GB of GPU memory. The total memory needs are influenced by a number of parameters, including the memory used by model weights, longer sequence length support, and KV (Key-Value) cache storage. Eight H100 Nvidia GPUs with 80 GB of HBM (High-Bandwidth Memory) apiece make up the A3 virtual machines, which are currently the most potent GPU option available on the Google Cloud platform. The only practical way to provide LLMs such as the FP16 Llama 3.1 405B model is to install and serve them across several hosts. To deploy over GKE, Google employs LeaderWorkerSet with Ray and vLLM.

LeaderWorkerSet

A deployment API called LeaderWorkerSet (LWS) was created especially to meet the workload demands of multi-host inference. It makes it easier to shard and run the model across numerous devices on numerous nodes. Built as a Kubernetes deployment API, LWS is compatible with both GPUs and TPUs and is independent of accelerators and the cloud. As shown here, LWS uses the upstream StatefulSet API as its core building piece.

A collection of pods is controlled as a single unit under the LWS architecture. Every pod in this group is given a distinct index between 0 and n-1, with the pod with number 0 being identified as the group leader. Every pod that is part of the group is created simultaneously and has the same lifecycle. At the group level, LWS makes rollout and rolling upgrades easier. For rolling updates, scaling, and mapping to a certain topology for placement, each group is treated as a single unit.

Each group’s upgrade procedure is carried out as a single, cohesive entity, guaranteeing that every pod in the group receives an update at the same time. While topology-aware placement is optional, it is acceptable for all pods in the same group to co-locate in the same topology. With optional all-or-nothing restart support, the group is also handled as a single entity when addressing failures. When enabled, if one pod in the group fails or if one container within any of the pods is restarted, all of the pods in the group will be recreated.

In the LWS framework, a group including a single leader and a group of workers is referred to as a replica. Two templates are supported by LWS: one for the workers and one for the leader. By offering a scale endpoint for HPA, LWS makes it possible to dynamically scale the number of replicas.

Deploying multiple hosts using vLLM and LWS

vLLM is a well-known open source model server that uses pipeline and tensor parallelism to provide multi-node multi-GPU inference. Using Megatron-LM’s tensor parallel technique, vLLM facilitates distributed tensor parallelism. With Ray for multi-node inferencing, vLLM controls the distributed runtime for pipeline parallelism.

By dividing the model horizontally across several GPUs, tensor parallelism makes the tensor parallel size equal to the number of GPUs at each node. It is crucial to remember that this method requires quick network connectivity between the GPUs.

However, pipeline parallelism does not require continuous connection between GPUs and divides the model vertically per layer. This usually equates to the quantity of nodes used for multi-host serving.

In order to support the complete Llama 3.1 405B FP16 paradigm, several parallelism techniques must be combined. To meet the model’s 750 GB memory requirement, two A3 nodes with eight H100 GPUs each will have a combined memory capacity of 1280 GB. Along with supporting lengthy context lengths, this setup will supply the buffer memory required for the key-value (KV) cache. The pipeline parallel size is set to two for this LWS deployment, while the tensor parallel size is set to eight.

In brief

We discussed in this blog how LWS provides you with the necessary features for multi-host serving. This method maximizes price-to-performance ratios and can also be used with smaller models, such as the Llama 3.1 405B FP8, on more affordable devices. Check out its Github to learn more and make direct contributions to LWS, which is open-sourced and has a vibrant community.

You can visit Vertex AI Model Garden to deploy and serve open models via managed Vertex AI backends or GKE DIY (Do It Yourself) clusters, as the Google Cloud Platform assists clients in embracing a gen AI workload. Multi-host deployment and serving is one example of how it aims to provide a flawless customer experience.

Read more on Govindhtech.com

[kennak]

ここ数か月は、サプライ チェーン攻撃の急増 に関する（おそらく大げさな）懸念 、ユビキタス図書館 、あるいは責任の重い EU サイバーレジリエンス法 など、オープンソースのセキュリティにとって厳しい状況でした。 Open Source Security Foundation (OpenSSF) の新しい Malicious Packages Repository は、 「オープン ソース パッケージ リポジトリで公開されている悪意のあるパッケージのレポートをまとめた、包括的で高品質なオープン ソース データベース」として、こうした外部の不安への対応の一部として見ることができます。 OpenSSF の悪意のあるパッケージ リポジトリ このデータベースは主に、開発者が悪意のある依存関係が CI/CD パイプラインを通過するのを阻止し、検出エンジンを改良し、環境内での使用をスキャンして防止し、インシデント対応を迅速化することを支援することを目的としており、すでに約 15,000 件の悪意のあるパッケージのレポートが登録されています。 現在、各オープンソース パッケージ リポジトリには、悪意のあるパッケージを処理するための独自のアプローチがあります。 悪意のあるパッケージがコミュニティによって報告されると、パッケージ リポジトリのセキュリティ チームはパッケージとその関連メタデータを削除する可能性がありますが、すべてのリポジトリがパッケージの公開記録を作成するわけではありません。 これは、記録が多くの異種の公共ソース上、または独自の脅威インテリジェンス フィードを通じてのみ存在することを意味します。 OpenSSF は、業界横断的な財団として一定の管理者のような正当性を享受しており、そのメンバーには AWS、Alphabet (旧 Google)、GitHub、Dell、IMB、Meta (旧 Facebook)、Microsoft などのテクノロジー大手が含まれているという事実があります。これは、集中リポジトリへのこの取り組みが重要になる可能性があることを意味します。 背景のカオス Google オープンソース セキュリティ チームの Caleb Brown 氏と Checkmarx のソフトウェア サプライ チェーン セキュリティ部門の Jossef Harsh Kadouri 氏によると、このリポジトリは、システムに対する悪意のあるパッケージ攻撃の増加に直接対応して構築されたものであ���とのことです。 「今年初め、Lazarus Group（北朝鮮国家支援の多大なハッキンググループ）がブロックチェーンと仮想通貨セクターを標的にした。このグループは、欺瞞的なnpmパッケージを含む高度な手法を使用して、さまざまなソフトウェアサプライチェーンを侵害した。共有インテリジェンスの集中リポジトリは、コミュニティに攻撃についてより早く警告し、オープンソース コミュニティが脅威の全範囲を理解できるように支援しました」と二人は OpenSSF ブログ投稿 で書いています。 によると、つい先月、Telegram、AWS、Alibaba Cloud のユーザーが、悪意のあるパッケージを使用した独自のオープンソース サプライ チェーン攻撃の標的になりました Checkmarx のレポート 。 攻撃者は「kohlersbtuh15」という仮名で活動し、一連の悪意のあるパッケージを PyPi パッケージ マネージャーにアップロードすることで、オープンソース コミュニティを悪用しようとしました。 (「これらのパッケージ内の悪意のあるコードは、自動実行を実行するのではなく、関数内に戦略的に隠蔽され、これらの関数が呼び出された場合にのみトリガーされるように設計されていた」と Checkmarx 氏は指摘しています。) 悪意のあるパッケージ リポジトリ : 入手方法 Malicious Packages リポジトリのレポートでは、オープン ソース プロジェクトの脆弱性を指定するために使用される JSON 形式であるオープン ソース脆弱性 (OSV) が使用されます。 悪意のあるパッケージに OSV 形式を使用すると、osv.dev API、osv-scanner ツール、deps.dev などの既存の統合を利用できます。 OSV 形式は拡張可能でもあり、侵害の兆候や分類データなどの追加データを記録できます。 へのリポジトリについてコメントし、 The Stack セキュリティ研究者である Henrik Plate 氏は、電子メールで アプリケーションセキュリティスタートアップのEndor Labs の 次のように述べています。「特に学術研究者にとって、これはマルウェア検出への新しいアプローチを調査しテストする良い機会を提供します。基本的な作業を何度もやり直します。たとえば、PyPI や npm などのさまざまなパッケージ レジストリでの新しいパッケージの公開の監視などです。 「ありがたいことに、この部分は関連する OpenSSF パッケージ フィード プロジェクトと連携しています OpenSSFパッケージ分析 プロジェクトでカバーされており、ブログ投稿で言及されているデータベースにデータを取り込む に匹敵する AI/ML トレーニング用の貴重なデータセットになる可能性があります Backstabber's Knife Collection 。 このデータベースは、実際のマルウェア (Python ホイールや tarball など) も公開していれば、 。 これが将来的に変わることを願っています。 「技術的な観点から見ると、これは主に悪意のある動作の動的検出に依存しているようです。 この目的のために、彼らは gVisor サンドボックスにパッケージをインストールし、潜在的に悪意のあるアクティビティを監視します。 注目に値するのは、少なくとも Python の場合、実際に悪意のあるコードをトリガーするのに大いに役立っているということです。 たとえば、特定のパッケージに存在する Python モジュールをインポートし、その関数を呼び出そうとします...」 「このアプローチは、悪意のあるコードの実行条件が満たされないため、悪意のあるアクティビティを検出できないという動的検出の典型的な問題を克服しようとします。」

無料の悪意のあるパッケージ リポジトリは強力な資産です

[trendtoday]

Llamacon Meta was everything to undervalue Openai

On Tuesday, Meta held her first conference for Developers of him, Llamaco, at its headquarters Menlo Park, California. The company announced the start of a Meta Chatbot application facing the consumer, who will compete with the chatgpt, as well as an API facing the developer to enter LMama models in the Cloud. Both omissions aim to expand the adoption of Open Llama company models, but this…

[trendstodaynet]

Llamacon Meta was everything to undervalue Openai

On Tuesday, Meta held her first conference for Developers of him, Llamaco, at its headquarters Menlo Park, California. The company announced the start of a Meta Chatbot application facing the consumer, who will compete with the chatgpt, as well as an API facing the developer to enter LMama models in the Cloud. Both omissions aim to expand the adoption of Open Llama company models, but this…

[arbitcoin]

Openai معاينة أول نموذج مجاني مفتوح المصدر الذكاء الاصطناعي في غضون خمس سنوات: إنه يخطط لإضافة وظيفة "Cloud Relay" لمحاربة META و DEEPSEEK

قد تكون هذه نقطة تحول رئيسية في عالم الذكاء الاصطناعي مفتوح المصدر. سيعود Openai أخيرًا إلى صفوف "Open Open" ويستعد أيضًا لإضافة ميزة قاتلة. سيتم إصدار نموذج Openai "Open" هذا الصيف من المتوقع أن تصدر Openai نموذجًا جديدًا لمنظمة العفو الدولية في أوائل الصيف من هذا العام ، وأهم ما هو أبرز ما هذه المرة هو أن هذا سيكون أول نموذج "مفتوح المصدر" في السنوات الخمس الماضية ، ولا يتطلب ترخيص API ويمكن تنزيله واستخدامه مباشرة. وفق《TechCrunch》وفقًا للتقارير ، فإن Openai لديه آمال كبيرة لهذا النموذج ، بهدف تجاوز نماذج مفتوحة المصدر مماثلة من منافسين مثل Meta و Deepseek. على الرغم من أن الاسم والمواصفات الكاملة لم يتم الكشف عنها رسميًا ، إلا أن الأخبار الداخلية أشارت إلى أن هذا النموذج قد تم تدريبه بطريقة جديدة ، وليس مراجعة للنموذج القديم. من المتوقع أن يكون الأداء أدنى قليلاً من طراز O3 الخاص بـ Openai ، ولكن في بعض اختبارات الاستدلال ، سوف يتغلب على طراز R1's Deepseek. وظيفة "Cloud Relay": يمكن أن تصبح النماذج الصغيرة أيضًا خارقة للإنسان الأمر الأكثر إثارة هو أن Openai يعتزم تقديم تصميم غير مسبوق في هذا النموذج: وظيفة Handoff Cloud Model. وفقًا لمصدرين مطلعين على المناقشات الداخلية ، فإن مفهوم التصميم هذا هو تمكين النماذج المحلية من الاتصال تلقائيًا نماذج كبيرة في سحابة Openai للمساعدة في إكمال المهام عند معالجة الاستعلامات المعقدة. كانت هذه الميزة مستوحاة من اقتراح مطور في منتدى مجتمع مفتوح المصدر الذي عقدته Openai ، والذي يبدو أنه حصل على اهتمام كبير من الشركة. حتى أن هناك تقارير تفيد بأن الرئيس التنفيذي لشركة Openai ، ذكر Sam Altman أيضًا هذه الميزة في اتصاله بالمطورين ووصفها بأنها "Handoff" ، والتي تشبه تمرير عصا إلى لاعب أقوى للتنقل. بنية هجينة مماثلة لذكاء Apple إذا تم تنفيذ هذه الوظيفة بنجاح ، فستسمح بنموذج Openai مفتوح المصدر أن يكون لكل من كفاءة التنفيذ المحلية وقوة الحوسبة المستندة إلى السحابة. لا يمكن أن تساعد مثل هذه الهندسة المعمارية ولكن تذكير الأشخاص بـ "Apple Intelligence" التي أطلقتها Apple في عام 2024. تجمع هذه الوظيفة أيضًا بين طراز جانب الجهاز ونموذج السحابة الخاصة للتعامل مع مهام الذكاء الاصطناعي. من خلال تصميم الحوسبة الهجينة هذا ، لا يمكن لـ Openai تحسين قدرات النموذج على المستوى الفني فحسب ، بل يمكن أيضًا جذب المزيد من المطورين من المجتمعات المفتوحة للانضمام إلى نظامها الإيكولوجي لأعمالها. بالنسبة للمستخدمين ، لديهم أيضًا الفرصة للاستمتاع بتجربة التبديل المرنة بين النماذج المجانية والنماذج المدفوعة. لا تزال قيود الأسعار والاستخدام غير واضحة ومع ذلك ، لا تزال هذه وظيفة "Handoff" المزعومة في مرحلة التخطيط الأولية ، ولم يتم تأكيدها ما إذا كانت سيتم تنفيذها على أنها مجدولة ، ولا يزال الحد الأقصى للاتصالات ذات الصلة ، وحد مكالمات API وغيرها من التفاصيل غير واضحة. لم يستجب Openai للتقرير في الوقت الحاضر. إذا تم إطلاق هذه الوظيفة رسميًا ، فهذا يعني أنه حتى إذا كان المستخدم يدير نموذجًا مجانيًا مفتوح المصدر ، فيمكنه "ترقية" النموذج المتقدم في السحابة عند الضرورة ، مما يقلل بشكل كبير من العتبة لتطبيق الذكاء الاصطناعي ، وتوسيع قاعدة المستخدمين المحتملين وفرص الإيرادات في Openai. تصميم المجتمع مفتوح المصدر؟ يعرض Openai موقفًا جديدًا من السمات الرئيسية لتطوير هذا النموذج أن Openai يتفاعل بنشاط مع مجتمعات المصادر المفتوحة.من خلال سلسلة من منتديات المطورين وأنشطة جمع التعليقات، دع أعضاء المجتمع يشاركون في مفهوم والاقتراحات الوظيفية لتصميم النموذج. هذا يختلف عن الانطباع بأن Openai أعطى الناس في الماضي "عملية الصندوق الأسود". قد يكون أيضًا التنافس مع منافسي المصادر المفتوحة مثل Meta و Rebuild Trust في المجال المفتوح. يفتح Openai موقفًا جديدًا ، ستصبح Cloud Relay المفتاح على الرغم من أن العديد من التفاصيل حول هذا النموذج لا تزال غير واضحة ، إلا أن الميزات المجانية والمفتوحة للقابلة للتنزيل كافية لجذب الانتباه. بمجرد تنفيذ وظيفة "Handoff" بنجاح ، ستنشئ Openai نموذجًا جديدًا يجمع بين مجاني وتجاري في ساحة معركة AI مفتوحة المصدر. قد لا يعيد هذا فقط كتابة منطق استخدام نماذج الذكاء الاصطناعى ، ولكن أيضًا لديهم فرصة لقيادة الطريق في سوق تنافسية للغاية. تحذير المخاطراستثمارات العملة المشفرة محفوفة بالمخاطر للغاية ، وقد تتقلب أسعارها بشكل كبير وقد تفقد كل مديرك. يرجى تقييم المخاطر بحذر.

[protraderindiamember11]

Algo Trading Course: Build Profitable Bots

ICFM (Institute of Career in Financial Markets) offers a cutting-edge algo trading course designed for traders and programmers seeking to automate their strategies. This comprehensive program covers Python programming for finance, strategy development, back testing techniques, and execution algorithms using platforms like Meta Trader and Python libraries. The course curriculum includes machine learning applications in trading, quantitative analysis, and high-frequency trading strategies, taught by SEBI-registered analysts and algo trading specialists with live market experience. ICFM's practical approach provides hands-on training with real tick data, teaching students to code, test, and optimize profitable trading bots. Participants learn risk management protocols specific to algorithmic systems and receive templates for mean-reversion, momentum, and arbitrage strategies. The institute's cloud-based trading lab allows 24/7 access to practice coding and deployment, while weekly hackathons foster competitive strategy development. This algo trading course bridges the gap between theoretical quant finance and practical implementation, with modules on connecting APIs to broker platforms and handling live market feeds. ICFM offers flexible learning options including intensive classroom programs in Delhi and interactive online sessions with personalized code reviews. Graduates gain access to proprietary tools, a quant trading community, and placement opportunities with fintech firms. Whether you're a trader looking to automate your edge or a programmer entering quantitative finance, ICFM's algo trading course delivers institutional-grade training that transforms beginners into algorithmic trading professionals capable of developing, testing, and deploying robust automated systems across equities, derivatives, and currency markets.

[sruthypm]

Power Up Your Web Development Career with MERN Stack Training in Kochi – Techmindz

Web development has evolved dramatically, and companies today are seeking developers who can manage both frontend and backend with ease. That’s where MERN Stack comes in—a powerful JavaScript-based technology stack that's become a go-to for full stack development. If you're looking to master this in-demand skill set, Techmindz offers the most practical and job-oriented MERN Stack training in Kochi.

Whether you’re a student, fresher, or professional looking to switch to web development, Techmindz can help you become a full stack developer with real-world skills.

What is MERN Stack?

MERN stands for MongoDB, Express.js, React.js, and Node.js—a full stack combination used to build high-performance web applications.

MongoDB – A flexible NoSQL database that stores data in JSON-like documents.

Express.js – A fast, minimalistic backend web application framework for Node.js.

React.js – A leading frontend library by Meta (Facebook) for building user interfaces.

Node.js – A runtime environment that executes JavaScript on the server side.

Together, this stack allows developers to write the entire application—from client to server—using JavaScript, making it efficient, fast, and scalable.

Why Choose MERN Stack?

In-Demand Skillset: Companies are actively hiring MERN developers due to the scalability and performance of this stack.

Single Language Across the Stack: JavaScript handles both frontend and backend—easy to learn, easy to manage.

Rapid Development: Great tools and a huge community make the development process smoother and faster.

Versatile Career Paths: Once you master MERN, you can work as a frontend developer, backend developer, or full stack developer.

Why Techmindz Offers the Best MERN Stack Training in Kochi

At Techmindz, we go beyond traditional classroom learning. We provide industry-relevant training with hands-on experience, so our students don’t just learn—they build.

1. Comprehensive, Hands-On Curriculum

Our MERN Stack course is structured to take you from the basics to building fully functional, live web applications. You’ll learn:

Frontend Development with React

JSX, components, props, state, hooks

Routing, form handling, API integration

Responsive design with Bootstrap/Tailwind

Backend with Node.js & Express

REST APIs, middleware, routing

User authentication and authorization

Error handling, security practices

Database with MongoDB

CRUD operations, schema design, Mongoose

Integrating with Express and Node

Real-time data handling

Deployment & Version Control

Git & GitHub

Hosting apps on cloud platforms (Vercel, Render, Heroku)

By the end of the course, you will have built multiple real-world projects, which you can add to your portfolio.

2. Trainers with Industry Expertise

Our instructors are experienced MERN stack developers who bring real-world insights into every session. They provide live coding, debug with you, and mentor you on project development.

3. Project-Based Learning

Learning code isn’t enough. At Techmindz, we focus on building complete, functional applications. From e-commerce websites and dashboards to real-time chat apps and blog platforms, you’ll get hands-on experience that mimics what you’ll be doing in a job.

4. Placement-Focused Approach

Techmindz isn’t just a training institute—we’re your career partner. Our MERN Stack training in Kochi includes:

Resume & GitHub profile building

Mock interviews with coding rounds

Communication & soft skills training

Direct placement assistance with our hiring partners in Infopark and beyond

5. Flexible Learning Options

We offer both offline training at our Kochi center and online live sessions, making it easy for college students, job seekers, or working professionals to join at their convenience. Weekend batches are also available.

Who Can Join the MERN Stack Course?

This course is perfect for:

Students pursuing B.Tech, BCA, MCA, or any IT-related course

Fresh graduates looking to enter web development

Backend/frontend developers who want to become full stack developers

Freelancers and aspiring entrepreneurs building their own platforms

No prior experience in JavaScript? No problem! We start from the fundamentals and guide you all the way to advanced application development.

Career Paths After MERN Stack Training

After completing the course, you'll be ready to take up roles such as:

Full Stack Developer

Web Application Developer

JavaScript Developer

Frontend/Backend Developer

Freelance Web Developer

With tech companies in Kochi and across India shifting to modern web stacks, MERN developers are in high demand.

Final Thoughts

Techmindz is proud to offer one of the most practical and career-driven MERN Stack training programs in Kochi. We focus on outcomes—ensuring our students not only learn but launch their careers in web development with confidence.

Whether you dream of joining a top IT company, working on your own startup, or freelancing with international clients, learning the MERN Stack with Techmindz is the first step.

👉 Enroll today. Build real apps. Get hired. That’s the Techmindz way.

https://www.techmindz.com/mean-stack-training/

[hawkstack]

Understanding the Architecture of Red Hat OpenShift Container Storage (OCS)

As organizations continue to scale containerized workloads across hybrid cloud environments, Red Hat OpenShift Container Storage (OCS) stands out as a critical component for managing data services within OpenShift clusters—whether on-premises or in the cloud.

🔧 What makes OCS powerful?

At the heart of OCS are three main operators that streamline storage automation:

OCS Operator – Acts as the meta-operator, orchestrating everything for a supported and reliable deployment.

Rook-Ceph Operator – Manages block, file, and object storage across environments.

NooBaa Operator – Enables the Multicloud Object Gateway for seamless object storage management.

🏗️ Deployment Flexibility: Internal vs. External

1️⃣ Internal Deployment

Storage services run inside the OpenShift cluster.

Ideal for smaller or dynamic workloads.

Two modes:

Simple: Co-resident with apps—great for unclear storage needs.

Optimized: Dedicated infra nodes—best when storage needs are well defined.

2️⃣ External Deployment

Leverages an external Ceph cluster to serve multiple OpenShift clusters.

Perfect for large-scale environments or when SRE/storage teams manage infrastructure independently.

🧩 Node Roles in OCS

Master Nodes – Kubernetes API and orchestration.

Infra Nodes – Logging, monitoring, and registry services.

Worker Nodes – Run both applications and OCS services (require local/portable storage).

Whether you're building for scale, resilience, or multi-cloud, OCS provides the flexibility and control your architecture demands.

📌 Curious about how to design the right OpenShift storage strategy for your org? Let’s connect and discuss how we’re helping customers with optimized OpenShift + Ceph deployments at HawkStack Technologies.

For more details - https://training.hawkstack.com/red-hat-openshift-administration-ii-do280/

#RedHat #OpenShift #OCS #Ceph #DevOps #CloudNative #Storage #HybridCloud #Kubernetes #RHCA #Containers #HawkStack

[chatkar]

blue tick on whatsapp

 Welcome to the Future of Smartest WhatsApp Engagement

In today's digital-first business world, the ability to engage customers instantly and effectively determines success. WhatsApp has emerged as one of the most powerful communication tools, and businesses that embrace its potential are reaping the rewards. Whether you're a startup, SME, or enterprise, leveraging the official WhatsApp Business APIs can revolutionize your customer engagement. That’s where Chatkar comes into play — your go-to WhatsApp API service provider delivering seamless, scalable, and smartest WhatsApp engagement solutions.

Why Choose WhatsApp for Business Communication?

The rise of WhatsApp automation in customer support, marketing, and notifications has changed how businesses interact with users. Here’s why integrating WhatsApp Business API into your customer journey is a game-changer:

Wide Reach: With over 2 billion global users, your customers are already on WhatsApp.

Instant Delivery: Messages are delivered instantly with high open rates — over 98%.

End-to-End Encryption: Ensures secure and private communication.

Multi-purpose Tool: From marketing to support and sales, WhatsApp handles it all.

Blue Tick on WhatsApp: Verified business accounts boost trust and credibility.

Services Offered by Chatkar

As a reliable WhatsApp business API provider, Chatkar offers a comprehensive suite of services to meet every business need:

✅ WhatsApp Business API Integration Get verified and set up quickly with minimal technical fuss.

✅ WhatsApp Broadcast for Business Send bulk messages without getting blocked, powered by automation and compliance.

✅ WhatsApp Automation Engage users with auto-replies, chatbots, and workflow automation.

✅ WhatsApp Login Web & Dashboard Manage messages, users, campaigns, and analytics all in one powerful web panel.

✅ WhatsApp Cloud API Solutions Fast, reliable, and scalable cloud solutions for enterprises.

✅ Generate WhatsApp Link for Instant Chat Create custom links to start conversations instantly — great for campaigns and CTAs.

✅ WhatsApp Message Marketing Tools Design and execute marketing campaigns that drive conversions and build brand loyalty.

✅ Custom WhatsApp Software Development Tailored tools for automation, customer management, and reporting.

✅ Cheapest WhatsApp API Plans Budget-friendly pricing without compromising performance or compliance.

Benefits of Using WhatsApp API

When you partner with a trusted WhatsApp API provider, you unlock a wide range of benefits that give your business a competitive edge:

🚀 Instant Customer Engagement Connect with customers in real time using WhatsApp broadcast and chat automation.

📊 Actionable Insights Use detailed analytics to track engagement, campaign performance, and ROI.

💬 Two-Way Conversations Engage in meaningful, personalized interactions — not just one-sided alerts.

🌐 Omnichannel Integration Combine WhatsApp with CRMs, ERPs, and marketing platforms for holistic customer experiences.

🔐 Security & Compliance Enterprise-grade encryption and GDPR-ready solutions.

⭐ Verified Business Presence Get the blue tick on WhatsApp, giving your brand an official, professional edge.

Why with Us? Why Chatkar is the Best WhatsApp API Provider

Choosing Chatkar means partnering with a leader in WhatsApp marketing services and automation. Here's what sets us apart:

🧠 Expertise in Smart Engagement Our solutions go beyond messaging — we build intelligent engagement funnels.

🧩 Customizable & Scalable Whether you need basic messaging or full-fledged WhatsApp software, we tailor everything to your needs.

💸 Affordable Pricing We offer the cheapest WhatsApp API services in the market, perfect for businesses of all sizes.

📞 24/7 Support Our dedicated team is always available to support you.

🧰 Feature-Rich Dashboard From scheduling to automation and analytics, manage everything with ease from our web interface.

🛡️ Official WhatsApp Business API Provider We work directly with Meta to ensure you stay compliant and updated with the latest tools.

How to Get the Right WhatsApp API for Your Business

Navigating the world of WhatsApp Business API can be tricky, but with Chatkar, it’s a smooth ride. Here's how to get started:

Book a Demo Connect with our experts to understand how our WhatsApp API services align with your goals.

Choose Your Plan We offer flexible packages based on message volumes, automation needs, and support levels.

Get Verified We assist you in getting verified and set up with the official WhatsApp API.

Integrate & Launch Use our tools or integrate with your system to start broadcasting, automating, and engaging.

Analyze & Optimize Track campaign performance and continuously improve with real-time data and reports.

Conclusion

The future of business communication lies in smart, instant, and personalized messaging — and WhatsApp leads the way. By leveraging the best WhatsApp API, you can automate conversations, boost sales, offer better support, and ultimately, scale your business faster than ever.

Whether you're looking for WhatsApp login web, ways to generate WhatsApp links, or a full WhatsApp marketing service provider, Chatkar is your one-stop solution.

[trendtoday]

Openai wants his 'open' model he to call models in the cloud for help

For the first time in approximately five years, Openai is preparing to release a system that is really “open”, which means it will be available to be downloaded at no cost and not closed after an API. Techcrunch reported on Wednesday that Openai is aiming for an early summer launch, and aiming at superior performance to Meta and Deepseek’s open models. Beyond his standard performance, Openai may…

[trendstodaynet]

Openai wants his 'open' model he to call models in the cloud for help

For the first time in approximately five years, Openai is preparing to release a system that is really “open”, which means it will be available to be downloaded at no cost and not closed after an API. Techcrunch reported on Wednesday that Openai is aiming for an early summer launch, and aiming at superior performance to Meta and Deepseek’s open models. Beyond his standard performance, Openai may…

[govindhtech]

Introduction to Multi Agent Systems Enhancement in Vertex AI

Multi-agent systems introduction

Vertex AI offers new multi-agent system creation and management methods.

All businesses will need multi-agent systems with AI agents working together, regardless of framework or vendor. Intelligent systems with memory, planning, and reasoning can act for you. They can multi-step plan and complete projects across many platforms with your instruction.

Multi-agent systems require models like Gemini 2.5 with better reasoning. They need corporate data and process integration. Vertex AI, its comprehensive platform for coordinating models, data, and agents, seamlessly integrates these components. It combines an open approach with strong platform capabilities to ensure agents work reliably without disconnected and brittle solutions.

Today, Google Cloud unveils Vertex AI advancements so you can:

Develop open agents and implement corporate controls

The open-source Agent Development Kit (ADK) is based on Google Agentspace and Google Customer Engagement Suite (CES) agents. Agent Garden has several extendable sample agents and good examples.

Vertex AI's Agent Engine is a managed runtime that safely deploys your custom agents to production globally with integrated testing, release, and reliability.

Connect agents throughout your organisation ecosystem

The Agent2Agent protocol gives agents a single, open language to communicate regardless of framework or vendor. This open project is led by us and collaborates with over fifty industry professionals to further our multi-agent system vision.

Give agents your data using open standards like Model Context Protocol (MCP) or Google Cloud APIs and connections. Google Maps, your preferred data sources, or Google Search may power AI responses.

Creation of agents using an open methodology with Agent Garden and Agent Development Kit

Google's new open-source Agent Development Kit (ADK) simplifies agent creation and complicated multi-agent systems while maintaining fine-grained control over agent behaviour. You can construct an AI agent using ADK in under 100 lines of user-friendly code. Look at these examples.

Available currently in Python (other languages will be released later this year), you can:

With orchestration controls and deterministic guardrails, you can accurately govern agent behaviour and decision-making.

ADK's bidirectional audio and video streaming enable human-like agent conversations. Writing a few lines of code to establish genuine interactions with agents may turn text into rich, dynamic discourse.

Agent Garden, a suite of useful tools and samples in ADK, can assist you start developing. Use pre-built agent components and patterns to learn from working examples and expedite development.

Pick the model that fits you. ADK works with all Model Garden models, including Gemini. Anthropic, Meta, Mistral AI, AI21 Labs, CAMB.AI, Qodo, and others provide over 200 models in addition to Google's.

Choose a deployment destination for local debugging or containerised production like Cloud Run, Kubernetes, or Vertex AI. ADK also supports MCP for secure data-agent connections.

Launch production using Vertex AI's direct integration. The reliable, clear path from development to enterprise-grade deployment eliminates the difficulty of transitioning agents to production.

ADK is optimised for Gemini and Vertex AI but works with your chosen tools. Gemini 2.5 Pro Experimental's improved reasoning and tool-use capabilities allow ADK-developed AI agents to decompose complex challenges and communicate with your favourite platforms. ADK's direct connection to Vertex AI lets you deploy this agent to a fully controlled runtime and execute it at enterprise scale.

Agent Engine deploys AI agents with enterprise-grade controls

Agent Engine, Google Cloud's controlled runtime, simplifies AI agent building. Agent system rebuilding during prototype-to-production is no longer required. Agent Engine manages security, evaluation, monitoring, scaling complexity, infrastructure, and agent context. Agent Engine integrates with ADK (or your chosen framework) for a smooth develop-to-deploy process. Together, you can:

Use ADK, LangGraph, Crew.ai, or others to deploy agents. Choose any model, such Gemini, Claude from Anthropic, Mistral AI, or others. Flexibility is paired with enterprise-grade control and compliance.

Keep session context: The Agent Engine supports short-term and long-term memory, so you don't have to start over. This lets your agents remember your discussions and preferences as you handle sessions.

Vertex AI has several tools to evaluate and improve agent quality. Improve agent performance by fine-tuning models based on real-world usage or utilising the Example Store.

Linking to Agentspace can boost utilisation. You may register Agent Engine-hosted agents with Google Agentspace. Gemini, Google-quality search, and strong agents are available to employees on this corporate platform, which centralises management and security.

Google Cloud will improve Agent Engine in the next months with cutting-edge testing and tooling. Agents can utilise computers and programs. To ensure production reliability, test agents with many user personas and realistic tools in a specialist simulation environment.

The Agent2Agent protocol connects agents across your enterprise

One of the biggest barriers to corporate AI adoption is getting agents from different frameworks and suppliers to work together. Google Cloud worked with industry leaders that support multi-agent systems to create an open Agent2Agent (A2A) protocol.

Agent2Agent allows agents from different ecosystems to interact, regardless of framework (ADK, LangGraph, Crew.ai, etc.) or vendor. A2A lets agents securely cooperate while publicising their capabilities and choosing how to connect with users (text, forms, bidirectional audio/video).

Your agents must collaborate and access your enterprise truth, the informational environment you developed utilising data sources, APIs, and business capabilities. Instead of beginning from scratch, you may give agents your corporate truth data using any method:

ADK supports Model Context Protocol (MCP), so your agents may connect to the growing ecosystem of MCP-compatible devices to access your numerous data sources and capabilities.

ADK lets you directly connect agents to corporate capabilities and systems. Data from AlloyDB, BigQuery, NetApp, and other systems, as well as more than 100 pre-built interfaces and processes established using Application Integration, are included. Your NetApp data may be used to create AI agents without data duplication.

ADK makes it easy to connect to call tools from MCP, LangChain, CrewAI, Application Integration, OpenAPI endpoints, and your present agents in other frameworks like LangGraph.

We manage over 800K APIs that operate your organisation within and outside Google Cloud. Your agents may utilise ADK to access these API investments from anywhere with the correct permission.

After linking, you may supplement your AI replies using Google Search or Zoominfo, S&P Global, HGInsights, Cotality, and Dun & Bradstreet data. For geospatial agents, we now allow Google Maps grounding. To maintain accuracy, we refresh 100 million Maps data points daily. Grounding with Google Maps lets your agents reply with geographical data from millions of US locales.

Create trustworthy AI agents with enterprise-grade security

Incorrect content creation, unauthorised data access, and prompt injection attacks threaten corporate AI agents' functionality and security. Google Cloud's Gemini and Vertex AI building addresses these difficulties on several levels. You could:

Manage agent output with Gemini's system instructions that limit banned subjects and match your brand voice and configurable content filters.

Identity controls can prevent privilege escalation and inappropriate access by determining whether agents work with dedicated service accounts or for individual users.

Google Cloud's VPC service controls can restrict agent activity inside secure perimeters, block data exfiltration, and decrease the impact radius to protect sensitive data.

Set boundaries around your agents to regulate interactions at every level, from parameter verification before tool execution to input screening before models. Defensive boundaries can limit database queries to certain tables or use lightweight models with safety validators.

Automatically track agent activities with rich tracing features. These traits reveal an agent's execution routes, tool choices, and reasoning.

Build multi-agent systems

Vertex AI's value depends in its whole functionality, not simply its features. Integration of solutions from various sources is now easy on a single platform. This unified strategy eliminates painful model trade-offs, corporate app and data integration, and production readiness.

[christianbale121]

The Ultimate Guide to AI Agent Development for Enterprise Automation in 2025

In the fast-evolving landscape of enterprise technology, AI agents have emerged as powerful tools driving automation, efficiency, and innovation. As we step into 2025, organizations are no longer asking if they should adopt AI agents—but how fast they can build and scale them across workflows.

This comprehensive guide unpacks everything you need to know about AI agent development for enterprise automation—from definitions and benefits to architecture, tools, and best practices.

🚀 What Are AI Agents?

AI agents are intelligent software entities that can autonomously perceive their environment, make decisions, and act on behalf of users or systems to achieve specific goals. Unlike traditional bots, AI agents can reason, learn, and interact contextually, enabling them to handle complex, dynamic enterprise tasks.

Think of them as your enterprise’s digital co-workers—automating tasks, communicating across systems, and continuously improving through feedback.

🧠 Why AI Agents Are Key to Enterprise Automation in 2025

1. Hyperautomation Demands Intelligence

Gartner predicts that by 2025, 70% of organizations will implement structured automation frameworks, where intelligent agents play a central role in managing workflows across HR, finance, customer service, IT, and supply chain.

2. Cost Reduction & Productivity Gains

Enterprises using AI agents report up to 40% reduction in operational costs and 50% faster task completion rates, especially in repetitive and decision-heavy processes.

3. 24/7 Autonomy and Scalability

Unlike human teams, AI agents work round-the-clock, handle large volumes of data, and scale effortlessly across cloud-based environments.

🏗️ Core Components of an Enterprise AI Agent

To develop powerful AI agents, understanding their architecture is key. The modern enterprise AI agent typically includes:

Perception Layer: Integrates with sensors, databases, APIs, or user input to observe its environment.

Reasoning Engine: Uses logic, rules, and LLMs (Large Language Models) to make decisions.

Planning Module: Generates action steps to achieve goals.

Action Layer: Executes commands via APIs, RPA bots, or enterprise applications.

Learning Module: Continuously improves via feedback loops and historical data.

🧰 Tools and Technologies for AI Agent Development in 2025

Developers and enterprises now have access to an expansive toolkit. Key technologies include:

🤖 LLMs (Large Language Models)

OpenAI GPT-4+, Anthropic Claude, Meta Llama 3

Used for task understanding, conversational interaction, summarization

🛠️ Agent Frameworks

LangChain, AutoGen, CrewAI, MetaGPT

Enable multi-agent systems, memory handling, tool integration

🧩 Integration Platforms

Zapier, Make, Microsoft Power Automate

Used for task automation and API-level integrations

🧠 RAG (Retrieval-Augmented Generation)

Enables agents to access external knowledge sources, ensuring context-aware and up-to-date responses

🔄 Vector Databases & Memory

Pinecone, Weaviate, Chroma

Let agents retain long-term memory and user-specific knowledge

🛠️ Steps to Build an Enterprise AI Agent in 2025

Here’s a streamlined process to develop robust AI agents tailored to your enterprise needs:

1. Define the Use Case

Start with a clear objective. Popular enterprise use cases include:

IT support automation

HR onboarding and management

Sales enablement

Invoice processing

Customer service response

2. Choose Your Agent Architecture

Decide between:

Single-agent systems (for simple tasks)

Multi-agent orchestration (for collaborative, goal-driven tasks)

3. Select the Right Tools

LLM provider (OpenAI, Anthropic)

Agent framework (LangChain, AutoGen)

Vector database for memory

APIs or RPA tools for action execution

4. Develop & Train

Build prompts or workflows

Integrate APIs and data sources

Train agents to adapt and improve from user feedback

5. Test and Deploy

Run real-world scenarios

Monitor behavior and adjust reasoning logic

Ensure enterprise-grade security, compliance, and scalability

🛡️ Security, Privacy, and Governance

As agents operate across enterprise systems, security and compliance must be integral to your development process:

Enforce role-based access control (RBAC)

Use private LLMs or secure APIs for sensitive data

Implement audit trails and logging for transparency

Regularly update models to prevent hallucinations or bias

📊 KPIs to Measure AI Agent Performance

To ensure ongoing improvement and ROI, track:

Task Completion Rate

Average Handling Time

Agent Escalation Rate

User Satisfaction (CSAT)

Cost Savings Per Workflow

🧩 Agentic AI: The Future of Enterprise Workflows

2025 marks the beginning of agentic enterprises, where AI agents become core building blocks of decision-making and operations. From autonomous procurement to dynamic scheduling, businesses are building systems where humans collaborate with agents, not just deploy them.

In the near future, we’ll see:

Goal-based agents with autonomy

Multi-agent systems negotiating outcomes

Cross-department agents driving insights

🏁 Final Thoughts: Start Building Now

AI agents are not just another automation trend—they are the new operating layer of enterprises. If you're looking to stay competitive in 2025 and beyond, investing in AI agent development is not optional. It’s strategic.

Start small, scale fast, and always design with your users and business outcomes in mind.

📣 Ready to Develop Your AI Agent?

Whether you're automating workflows, enhancing productivity, or creating next-gen customer experiences, building an AI agent tailored to your enterprise is within reach.

Partner with experienced AI agent developers to move from concept to implementation with speed, security, and scale.