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European Broadcasting Union and NVIDIA Partner on Sovereign AI to Support Public Broadcasters

NVIDIA and the European Broadcasting Union (EBU) collaborate on sovereign AI for European public service media.
The partnership aims to provide public broadcasters with access to high-quality cloud and AI technologies.
Focus on building sovereign AI and cloud frameworks, workforce development, and cultivating an AI ecosystem.
Sovereign AI ensures the development and deployment of AI using local infrastructure and datasets.
Goal is to preserve cultural identity, enhance public trust, and support innovation specific to European countries' needs.
Collaboration will establish frameworks that prioritize independence, public trust, and compliance with European policies.
Partnership includes developing hybrid cloud architectures and interoperable workflows for public service media.
NVIDIA and EBU to focus on personalized services, transparent content recommendations, and investment in sovereign cloud and AI infrastructure.
NVIDIA will provide training programs through its Deep Learning Institute to support European media organizations.
Resources like NVIDIA GB200 Grace Blackwell Superchip and RTX PRO Servers will support secure and cost-efficient AI training.
Collaboration crucial for the development of standards and best practices for sovereign European cloud and AI.
The aim is to foster trust, innovation, and resilience in the European media landscape.

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SiliconCanals

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London-based Maze secures €22.5M to beat hackers at their own game through an AI agent

London-based AI-native cloud security platform Maze secures $25M (€22.5M) in Series A funding led by Theory Ventures.
The startup, founded in 2024 by Harry Wetherald, Adrian Jozwik, and Santiago Castineira, has raised a total of $31M (€27.9M) within a year of its founding.
Funding will drive team growth and expansion into new use cases for its AI-native security platform, focusing on autonomous vulnerability investigation and resolution.
Maze plans to extend its security solution from vulnerability management to other cloud security applications.
Existing investors Cherry Ventures and Tapestry VC participate in the funding round, which includes deployment of AI agents with various organizations.
Maze's AI agents mimic human behavior, investigating cloud data to pinpoint exploitable vulnerabilities and automatically resolve critical security issues.
In 2024, vulnerability exploitation increased by 34%, with CVE growth at 40%, signifying the importance of AI-native security solutions in addressing security incidents.
The Maze team's collective experience includes working at companies such as Tessian, Elastic, and Amazon, with Theory Ventures partner Andy Triedman joining the Maze board.
Maze's rapid funding trajectory, from seed to Series A in nine months, underscores the demand and validation for AI-native security solutions in the market.
The company's AI agent is designed to automate investigations and triage, offering a new approach compared to traditional vulnerability management tools.

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Dev

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🚀 Built a Serverless Image Optimizer with AWS Lambda + S3 (Free Tier Safe)

The project involves building a serverless image optimizer using AWS Lambda and S3.
The process includes uploading an image to S3, triggering Lambda to compress it, and displaying the optimized version.
The project is designed to be beginner-friendly, fully serverless, and cost-effective within the AWS Free Tier.
Key components of the tech stack include AWS Lambda, Amazon S3, IAM Roles, Pillow library, and Shell Scripts.
The project structure comprises Lambda code, deployment scripts, sample images, dependencies, and README.
Uploading an image to S3 triggers Lambda to compress it and save the optimized version as optimized-filename.jpg.
The project aims to be Free Tier compatible, avoiding unexpected AWS charges by utilizing simple setup and teardown scripts.
The target audience includes Cloud beginners, indie developers, DevOps learners, and freelancers interested in automation and AWS services.
Key learnings involve securely connecting Lambda to S3, optimizing Lambda deployment, IAM roles usage, and staying within the Free Tier limits.
The creator welcomes feedback, collaboration, and discussions related to serverless ideas.

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Medium

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Soar To Success: Expert AI ML Training Institute In Bangalore

Artificial Intelligence (AI) and Machine Learning (ML) involve the development of computer systems that can perform human-like tasks through learning and reasoning.
A career in AI and ML is suitable for those passionate about technology, innovation, and problem-solving.
AI and ML experts play roles involving tasks like data analysis, programming, and algorithm development.
Salary for AI and ML professionals in India varies based on factors like experience, location, and employer type.
Individuals interested in AI and ML can start pursuing a career in these fields after completing the 12th grade.
Courses in AI and ML cover topics such as data analysis, machine learning algorithms, and AI applications.
Eduleem School of Design & IT in Bangalore provides an affordable AI and ML course designed to equip students with industry-relevant skills.
Eduleem School of Design & IT offers training in AI and ML for individuals aspiring to enter these fields.
With the right training and skills, individuals can succeed in the AI and ML industry by working with cutting-edge technologies.
Eduleem's AI and ML course is a cost-effective option for those seeking quality training in Bangalore.
Eduleem is recommended as a budget-friendly AI and ML institute in Bangalore for individuals looking to kickstart a career in these fields.
Initiating a career in AI and ML can lead to a gratifying journey working with advanced technologies and intelligent systems.
Eduleem School of Design & IT stands out as a recommended choice for those interested in AI and ML training in Bangalore.
Individuals can contact Eduleem for more information on their AI and ML courses.
For those aspiring to pursue AI and ML careers in Bangalore, Eduleem offers an affordable starting point in gaining the necessary skills and expertise.

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TechJuice

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OpenAI Pivots to Google Cloud From Microsoft In A Shocking Move

OpenAI has partnered with Google Cloud to enhance its AI infrastructure, moving away from its reliance on Microsoft Azure since 2019.
The collaboration aims to provide the necessary computing power for ChatGPT, which serves millions of users, with Google's TPUs playing a crucial role.
Despite being competitors in the AI field with products like ChatGPT and Gemini, OpenAI and Google prioritize computing power over corporate rivalry.
This unexpected shift has had a positive impact on Alphabet's stock, showcasing Google Cloud's competitiveness against other major cloud providers like AWS and Azure.

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Tech Radar

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Microsoft’s ROG Xbox Ally will feature a new “Xbox full-screen experience” to finally rival the Steam Deck’s ease of use – and more Windows 11 gaming handhelds will get it too

Microsoft is updating its Xbox Windows 11 app for the new ROG Xbox Ally handheld with a 'full-screen experience' for handheld users.
This user-friendly interface will also be available on other Windows 11 handhelds in 2026, allowing access to games from different stores and applications.
The aim is to create a streamlined hub for gaming, similar to Valve's SteamOS, providing a simpler start-up-and-play experience.
The performance improvements and impact on gaming experience will be key factors in determining the success of Microsoft's update for Windows 11 handhelds.

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Dev

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Multi Cloud Observability with Open Telemetry

Cloud workloads are becoming more complex, leading to the need for open-source and vendor-agnostic solutions like OpenTelemetry for observability across multiple cloud environments.
Before OpenTelemetry, observability was fragmented and vendor-specific, creating challenges like application coupling to specific cloud providers and inconsistent telemetry data formats.
OpenTelemetry, a vendor-neutral open-source framework, simplifies the generation, collection, and exporting of telemetry data, making it vendor-agnostic and industry-standard.
Key benefits of OpenTelemetry include vendor-agnosticism, unified data collection, and core components like SDKs, the Collector, and standardized APIs for multi-cloud environments.

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Siliconangle

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GitLab’s revenue outlook comes up short and its stock gets hammered

GitLab Inc. faced a dip in its stock price after falling slightly short on its sales guidance while increasing full-year profit outlook.
In the first quarter, GitLab reported earnings of 17 cents per share, surpassing Wall Street's target of 15 cents, with revenue rising by 27% to $214.5 million.
The company reduced its net loss to $36.2 million from $55.4 million in the year-ago period, functioning as a major player in the DevOps market.
Despite the positive financial performance and raised earnings forecast, GitLab's stock fell over 11% in after-hours trading, bringing its year-to-date decline to over 13%.

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Javacodegeeks

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Infrastructure as Code Showdown: Pulumi vs Terraform with AWS

A comparison between Pulumi and Terraform for Infrastructure as Code (IaC) with AWS.
Pulumi offers flexibility and real programming with TypeScript, Python, and Go, while Terraform is stable and mature for large-scale operations.
Both Pulumi and Terraform provide IaC benefits such as reproducible environments, version-controlled infrastructure, and automated scaling.
Pulumi uses imperative code allowing full programming power, while Terraform follows a declarative DSL approach.

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Siliconangle

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AWS DC Summit 2025: The cloud becomes strategic national infrastructure for the AI era

The AWS DC Summit highlighted the importance of AI, cloud infrastructure, and public-private partnerships as national priorities that are currently in progress.
With significant investments in data centers, AWS is building the foundation for an AI-driven U.S. economy, essential for innovation, energy modernization, and security.
Partnerships with companies like Salesforce and Snowflake showcased how AI is moving from pilot to production across various sectors, including national security and healthcare.
AWS's next-generation data center infrastructure is tailored for AI workloads, with initiatives like Project Rainier deploying advanced chips and focusing on energy efficiency.
AWS's emphasis on workforce development and community impact, including job initiatives and local partnerships, reflects a commitment to driving economic advancement.
Public-private partnerships were highlighted as crucial for trusted innovation, with AWS collaborating with partners to ensure regulatory compliance and security standards.
The AWS DC Summit emphasized that cloud and AI are no longer experiments but strategic imperatives for both businesses and governments.
The event showcased discussions on the future of AI, regulatory policies, and the importance of secure cloud adoption in the public sector.
AWS's focus on building the AI-driven digital backbone of the future was at the core of the summit, highlighting the significant role of technology in shaping national and economic landscapes.
Overall, the AWS DC Summit underscored the critical need for innovation through trusted partnerships and investments in AI infrastructure for a resilient and competitive future.

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Siliconangle

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Atlassian rival Linear raises $82M to expand developer tools and target enterprise adoption

Linear Orbit Inc., a competitor to Atlassian Corp., has raised $82 million in new funding to enhance its developer tools and target larger enterprise customers.
The funding round was raised on a valuation of $1.25 billion, with Linear offering issue-tracking and project management platform focusing on efficiency, clarity, and developer-first workflows.
Linear's platform features AI-assisted bug triage and sprint tools, along with a Triage system to efficiently manage incoming issues. It provides analytics tools for progress tracking and integrates with services like GitHub and Slack.
The Series C round was led by Accel Partners LP, with participation from existing investors 01A Advisors and Sequoia Capital Operations, and new investors Seven Seven Six and Designer Fund. Linear has raised a total of $134.2 million to date.

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Unite

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Evogene and Google Cloud Unveil Foundation Model for Generative Molecule Design, Pioneering a New Era in Life-Science AI

Evogene and Google Cloud have revealed a groundbreaking generative AI foundation model for small-molecule design, enhancing the discovery of new compounds in pharmaceuticals and agriculture.
The model, part of Evogene's ChemPass AI platform, enables simultaneous optimization of efficacy, toxicity, and stability in a single design cycle, accelerating R&D.
Unlike traditional step-by-step screening, the AI paradigm allows for multi-parameter design, reducing late-stage failures and fostering innovation.
Evogene's model employs transformer neural network architecture, learning from a vast chemical dataset to design novel, drug-like molecules with high precision.
The AI leverages multi-objective optimization, balancing potency, toxicity, and stability concurrently, enhancing drug discovery efficacy.
ChemPass AI's generative model operates by predicting molecule structures in a sequence-based approach, combining sequence and graph neural network models for accuracy and creativity.
The AI's ability to handle multi-objective optimization distinguishes it from traditional methods, allowing for tailored design strategies and increased success rates.
ChemPass AI's multi-traits evaluation, chemical diversity expansion, speed, scale, and integrated knowledge offer significant advantages over manual approaches in R&D.
Evogene's AI-powered tech-engines, including MicroBoost AI and GeneRator AI, form an integrated ecosystem to revolutionize product discovery in life sciences.
Generative AI's role in revolutionizing molecule discovery, facilitating multi-goal compound design in a single step, presents a transformative future in pharmaceuticals, agriculture, and beyond.
The validation of AI-generated molecules through experimentation will continually refine models, driving innovation and discovery in materials, food, sustainability, and various industries.

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Dev

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Compressing for Performance over Cost in Opensearch

Opensearch allows users to configure data storage at the index level, enabling optimization for efficiency and speed based on specific use cases.
Recommended compression algorithms vary based on the frequency of writes and reads to an index.
Codecs like zstd_no_dict, zstd, and best_compression offer different trade-offs between compression ratio, speed, CPU, and memory usage.
Factors such as node size, CPU usage, and real-time requirements should influence codec selection.
LZ4 prioritizes speed with low CPU and memory usage, while zstd offers a balance between compression ratio and speed.
Zstd_no_dict provides faster write operations with reduced disk footprint compared to other codecs.
LZ4 excels in compression and decompression speeds, making it ideal for scenarios where speed is crucial.
Tips for optimizing performance and storage costs in Opensearch clusters include sharding, adjusting compression levels, and utilizing hardware acceleration.
Understanding developer usage patterns and access needs is key to selecting the appropriate codec for a cluster.
Balancing trade-offs and considering long-term cluster management are essential in codec selection decisions.

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Dev

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Inside Kubernetes: How ClusterIP Services Route Traffic to Pods With Real-World Debugging Case Study

ClusterIP is the default service type in Kubernetes that enables intra-cluster communication by exposing a virtual IP address.
Understanding how ClusterIP routes traffic internally is crucial for resolving production outages quickly.
Real-world case studies illustrate common ClusterIP issues such as label mismatches and backend unreachability.
Best practices to avoid ClusterIP failures include using readiness probes, monitoring Endpoints objects, auditing NetworkPolicies, enabling kube-proxy alerts, and aligning pod labels and selectors.

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Siliconangle

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Three insights you might have missed from theCUBE’s coverage of Red Hat Summit

Red Hat Summit highlighted the company's focus on AI automation, open-source infrastructure, and trusted tools.
Red Hat's AI strategy aims to simplify operations and make AI more accessible across the stack.
Red Hat is emphasizing transparency, usability, and sustainability in its AI infrastructure initiatives.
Collaborations with chipmakers like Intel and AMD are driving flexible and efficient AI infrastructure solutions.
Red Hat works on closing the AI skills gap and promoting responsible data practices, especially in emerging markets.
Partnerships with Intel and AMD focus on translating open-source code into cost-effective AI solutions.
Red Hat's AI strategy includes stable platforms, open-source tools, and a growing partner ecosystem for easy AI adoption.
The company aims to make AI production-ready and scalable, with recent open-source projects facilitating real-world deployment.
Red Hat is expanding its AI partner ecosystem with validation tools to support scalable outcomes across infrastructure and services.
The coverage by theCUBE at Red Hat Summit emphasized the importance of leveraging open-source solutions and collaborations for advancing AI infrastructure.

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