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Behind the Scenes of Netflix: Unpacking Its World-Class Infrastructure

Netflix's world-class infrastructure handles over 260 million subscribers and billions of hours of content monthly, relying on cloud-native architecture and a custom CDN for global delivery.
Evolution: Originating as a DVD rental service, Netflix transitioned to AWS in 2008 for scalability and resilience, moving to a microservices-based ecosystem.
Architecture: Netflix operates entirely on AWS, utilizing services like EC2, S3, RDS, DynamoDB, and CloudFront for deployment, storage, and content delivery.
Microservices: Netflix runs over a thousand microservices that communicate through REST, gRPC, and Kafka for scalability and decoupling.
Video Pipeline: Netflix's streaming pipeline encodes videos in various formats, optimizing content delivery through a custom CDN called Open Connect for low latency.
Personalization: Netflix leverages machine learning for personalized recommendations, conducting extensive A/B testing to enhance user experience.
Observability: Netflix ensures reliability through chaos engineering tools like Chaos Monkey, and monitoring systems like Atlas, Zipkin, and ELK Stack.
Global Availability: Netflix deploys across AWS regions, smartly routing requests to nearest servers for low latency and adaptive streaming.
Developer Tooling: Netflix empowers developers with Spinnaker and Titus for safe deployments, and follows strict security measures for data protection.
Conclusion: Netflix's infrastructure showcases cloud-native engineering excellence, providing insights for building scalable and intelligent systems.

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Dev

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Azure Fundamentals: Microsoft.ADHybridHealthService Registered By Default

Microsoft.ADHybridHealthService (Azure AD Connect Health) is a crucial service for monitoring and troubleshooting hybrid identity issues between on-premises and Azure AD.
The service ensures real-time monitoring of synchronization errors, alerts for authentication bottlenecks, and insights into AD FS performance.
It has helped companies like Contoso Ltd. reduce identity-related outages significantly by catching synchronization failures early.
Microsoft.ADHybridHealthService plays a vital role in supporting cloud-native apps, zero-trust architectures, and seamless migrations in modern enterprises.
It functions as a 'fitness tracker' for hybrid identity setups, tracking sync errors, authentication server performance, and security risks.
The service addresses issues like login failures post-password change, overloaded AD FS servers, and dormant accounts vulnerable to attacks.
Key components include lightweight agents on AD Connect and AD FS servers, Azure Portal dashboard, and proactive alerting engine.
Industries such as healthcare, finance, and education benefit from the service by ensuring compliance, fraud detection, and scaling capabilities.
Top features include sync error analytics, AD FS performance monitoring, and custom alert rules to address specific monitoring needs.
The service enables proactive troubleshooting, eliminates manual log parsing, and closes security gaps caused by orphaned accounts or sync failures.
Microsoft.ADHybridHealthService empowers IT teams to address issues efficiently and improve overall security while ensuring consistent hybrid identity operations.

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Dev

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GCP Fundamentals: AI Platform Training & Prediction API

Google Cloud's AI Platform Training & Prediction API is a managed service simplifying the machine learning lifecycle.
Enterprises are shifting ML workloads to the cloud for scalability and cost efficiency.
GCP's AI services integrate with other cloud providers and prioritize sustainability.
Real-world success stories include Spotify for music recommendations and HSBC for fraud detection.
The AI Platform Training & Prediction API offers scalable ML model training and real-time prediction serving.
Core components include Training Service, Prediction Service, and Model Registry.
The API evolved to support TensorFlow 2.x and custom containers by 2022.
Benefits include reduced DevOps tasks for developers and faster time-to-market for businesses.
Use cases range from retail demand forecasting to fraud detection in banking.
Key features include distributed training, AutoML integration, custom containers, and versioned model deployment.
Practical use cases exhibit low-latency predictions and seamless ecosystem integration.
Pricing details encompass training costs and prediction expenses.
Security measures involve using service accounts and enabling VPC Service Controls.
Integration with other GCP services like BigQuery and Cloud Functions is seamless.
Pros of the API include fully managed infrastructure and TensorFlow integration, with the cons being a steep learning curve for beginners.
Best practices include monitoring jobs and setting alerts for failed processes.
In conclusion, the AI Platform Training & Prediction API simplifies AI infrastructure, making scalable ML accessible to all.

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Alvinashcraft

176

Dew Drop – June 19, 2025 (#4443)

GitHub Copilot introduces Copilot Spaces to bring contextual suggestions.
Uno Platform Team discusses building Cross-Platform Media Applications in .NET with MediaPlayerElement.
MSSQL Extension for VS Code now supports Local Containers and GitHub Copilot Agent Mode.
Azure Developers delve into AI Agents with MCP.
Windows 365 and Azure Virtual Desktop enhance business resilience and security.
TypeScript introduces support for the new class Iterator at the type level.
Pulumi ESC now allows Bring Your Own Keys.
Deno 2.3 adds support for Local NPM Packages.
EF Core Query Hacks are shared to boost app performance.
OCI Artifacts are chosen by Docker for AI Model Packaging.

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Silicon

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OpenAI’s Altman Hits Out At Meta’s ‘Crazy’ Sign-On Bonuses

OpenAI’s co-founder and CEO, Sam Altman, criticized Meta for offering 'crazy' $100m sign-on bonuses to poach top AI experts.
Tech giants like Meta are keen on acquiring AI skills to achieve their AI ambitions, facing challenges due to AI skills shortages.
Altman expressed concerns about Meta's strategy of offering significant upfront compensation over focusing on work and mission, affecting company culture.
Despite Meta's heavy AI investments, Altman questioned the impact of AI advancements on improving the world significantly.
Altman believes AI's real impact will be in discovering new science, overshadowing other achievements.
Meta's investment in AI infrastructure is substantial, with plans to increase spending by $65bn in 2025, focusing on superintelligence development.
Meta saw success with its Meta AI chatbot, reaching one billion monthly active users, and acquired a stake in Scale AI for AI development.
Other tech giants like Microsoft and Google are also engaging in the AI talent war, investing heavily and poaching experts.
Venture capitalist Deedy Das highlighted the intense competition for AI talent, pointing out Meta's struggle to retain candidates despite offering high salaries.

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Dev

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💾 What Is AWS Storage & Databases? (And How to Choose the Right One for Your Project)

AWS offers a variety of storage and database options to choose from based on specific project needs.
Different AWS storage services cater to varying requirements, such as quick access, security, scalability, and structured data.
Key storage options include Block Storage (EC2 Instance Store, Amazon EBS), Object Storage (Amazon S3), File Storage (Amazon EFS), Relational Databases (Amazon RDS, Amazon Aurora), NoSQL Databases (Amazon DynamoDB), Big Data Analytics (Amazon Redshift), and Database Migration (AWS DMS).
Specialized databases like Amazon DocumentDB, Neptune, QLDB, and Managed Blockchain serve unique purposes.
Speed boosters like ElastiCache and DAX are available for caching needs.
Choosing the right AWS storage service depends on the specific use case, such as files (S3), virtual hard drive (EBS), shared drive (EFS), relational SQL (RDS or Aurora), NoSQL (DynamoDB), big data analytics (Redshift), or migration (DMS).
Each service is designed to meet particular requirements and workload demands efficiently.
Understanding individual project needs helps in selecting the most suitable AWS tool, allowing AWS to manage the technical aspects in the background.
AWS's diversified storage and database offerings cater to the uniqueness of each project, ensuring optimal performance and scalability.

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Engadget

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The best budget gaming laptops for 2025

Gaming laptops have become more affordable, offering impressive performance and features.
Budget gaming laptops now come with high-refresh-rate displays and some even feature OLED screens.
These laptops can run modern games at high settings and provide added versatility and performance.
Best budget gaming laptops for 2025 cater to those looking for affordability and quality performance.
When it comes to budget gaming laptops, the focus is on models under $1,000.
Laptops priced between $1,000 and $2,000 are considered mid-range.
Cheap gaming laptops provide decent performance with CPUs like Intel and AMD's latest offerings.
Entry-level GPUs such as NVIDIA's RTX 3050 are common in budget gaming laptops.
It is recommended to aim for at least 16GB of RAM and 512GB SSD for gaming laptops.
Build quality of cheap gaming laptops may have more plastic components, but they are still durable.
Affordable gaming laptops are heavier and thicker than higher-end models, with less optimal Wi-Fi and connectivity options.
Battery life can be a trade-off in budget gaming laptops.
Cheap gaming laptops are worth it for those looking to save money and are aware of the limitations at this price point.
It's important to avoid extremely low-priced gaming laptops with insufficient RAM or storage.
For more details, the original article can be found on Engadget.
To enjoy gaming without breaking the bank, modern budget gaming laptops offer a compelling balance of affordability and performance

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Dev

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Automating AWS Infrastructure Provisioning with CodePipeline and CloudFormation Nested Stacks

The article discusses automating AWS infrastructure provisioning using CodePipeline and CloudFormation nested stacks for a GitOps-style deployment.
AWS CodePipeline automates build, test, and deployment phases, integrating with CodeBuild, CloudFormation, CodeStar Connections, and GitHub.
The architecture includes development, staging, and production environments, one Git repo, and CloudFormation nested stacks for modularity.
Creation of prerequisite components like GitHubConnection, PipelineArtifactStoreS3Bucket, CodeBuildServiceRole, etc., is detailed using CloudFormation.
The process involves creating, linting, and deploying CloudFormation stacks through CodePipeline, ensuring syntactic correctness and automated deployments.
Steps for authorizing GitHub in CodeStar Connection and watching the pipeline run, including linting and infrastructure provisioning, are provided.
The guide covers making and deploying changes, cleanup steps for stack deletion, and concludes by highlighting the advantages of this automated pipeline.
The setup enhances deployment consistency, integrates well with developer workflows, and simplifies infrastructure provisioning through automation.
GitHub repo reference and GitOps environment modeling information are included for further exploration and understanding.

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VoIP

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South Korea Unveils Largest AI Data Center with AWS & SK

South Korea is set to unveil the country’s largest AI data center through a partnership between SK Group and Amazon Web Services (AWS) in Ulsan at the Mipo National Industrial Complex.
The project aims for 60,000 GPU deployments, the largest in South Korea, with a phased rollout achieving 41 MW by November 2027 and a full capacity of 103 MW by February 2029.
AWS is investing $4 billion, while SK Group subsidiaries like SK Telecom and SK Broadband will supply resources, with total investments of around 3.4 trillion won ($2.5 billion) by 2028.
The AI data center is strategically located near SK Gas’s LNG power plant for reliable power supply, crucial for such extensive AI infrastructure.
AWS and SK Telecom have existing infrastructure in South Korea, with SK Telecom focusing on AI initiatives like AI data centers and GPU services.
SK has launched a cloud-based GPU service to democratize computing power access for businesses and developers with AI needs.
SK is also enhancing its edge computing capability for low-latency processing in mobile and IoT applications, increasing real-time analysis and decision-making.
The project's ambition underscores South Korea's commitment to AI infrastructure development in Asia, with SK and AWS leading the way.
The collaborative effort aims to handle both domestic and international AI workloads through hyperscale AI data centers.
The partnership between SK Group and AWS signifies a significant investment and commitment to advancing AI infrastructure in South Korea.
The data center project complements existing facilities in Seoul and development activities in Incheon, reinforcing SK Telecom's dedicated pursuit of AI initiatives.
The AI Infrastructure Superhighway concept by SK highlights a holistic approach, encompassing AI data centers, GPU services, and edge AI capabilities.
The proximity to SK Gas’s power plant ensures reliable power supply for sustaining the extensive AI infrastructure planned for the data center.
This collaboration will contribute to expanding AI capabilities in South Korea, allowing for innovative solutions and advancements in AI technology.
SK's focus on democratizing computing power access and enhancing edge computing capabilities aligns with the growing demand for AI solutions and real-time processing.
In summary, the partnership between SK Group and AWS heralds a significant milestone in AI infrastructure development, aiming to meet the evolving needs of businesses and developers in South Korea and beyond.

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Medium

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Building Terminal Defender: My Experience with Amazon Q

The author created an educational terminal-based game called Terminal Defender inspired by Amazon Q.
The game teaches bash commands in a fun way by having players type commands to defeat enemies.
Effective prompting techniques learned during development include starting with a clear concept, being specific about changes, identifying problems precisely, and requesting specific enhancements.
Amazon Q handled game development challenges like creating a clean game loop, collision detection, targeting, and difficulty progression.
Development automation by Amazon Q saved time by handling project scaffolding and generating a database of bash commands with descriptions.
AI-generated solutions included adding a command history feature to the game over screen efficiently.
Working with Amazon Q was efficient, understanding game development concepts well and producing clean code for Terminal Defender.
Iterating on ideas and fixing issues quickly was a valuable aspect of working with Amazon Q.
The author aims to make learning computer science concepts fun through educational games like Terminal Defender.
The game can be played at https://github.com/devinnicholson/terminal-defender.
Thanks to Red Hat for the inspiration.

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Tech Radar

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Google Gemini’s super-fast Flash-Lite 2.5 model is out now - here’s why you should switch today

Google has introduced the Gemini 2.5 Flash-Lite model, known for its speed and cost-effectiveness, aimed at tasks requiring minimal processing like translation and data organization.
The Flash-Lite model is in preview, offering faster processing speed compared to Gemini 2.5 Flash and Gemini 2.5 Pro, which are now generally available.
Flash-Lite is ideal for tasks where speed is critical and suitable for large but relatively simple tasks such as bulk translation, data classification, and information organization.
The model is an upgrade from Gemini 2.0 Flash-Lite and performs better in tasks like math, science, logic, and coding. It is about 1.5 times faster than older models.
Flash-Lite's cost efficiency sets it apart, allowing users to toggle between more powerful reasoning tools depending on the task, making it unique among Gemini models.
Despite its affordability and speed, Flash-Lite can handle significant tasks, with a context window of one million tokens for tasks like translating large documents.
Gemini 2.5 Flash and Pro, previously in preview, are now generally available, expanding Google's Gemini model offerings tailored to specific needs across various organizations.
Google's integrated products give it an advantage over competitors in the fast-and-cheap AI model market, with Gemini 2.5 Flash-Lite excelling in tasks like classifying social media posts and translating content into multiple languages.

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Siliconangle

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Microsoft reportedly planning new round of layoffs with sales roles likely to be hit

Microsoft is reportedly planning to lay off several thousand employees next month, with sales roles likely to be impacted.
The job cuts are expected to occur after Microsoft's fiscal year ends on June 30 and could affect various departments, not just sales teams.
This potential round of layoffs would be the second major one by Microsoft this year, following a prior layoff of around 6,000 employees in May.
Microsoft has around 228,000 employees, with 45,000 in sales and marketing.
The company's Chief Commercial Officer indicated plans to use third-party firms for software sales to small and mid-size customers, affecting in-house sellers.
While Microsoft has not confirmed the layoffs, a spokesperson previously mentioned the evolution of the sales organization to align with the AI era.
Other tech companies like Autodesk, Google, Meta Platforms, Intel, and CrowdStrike have also announced job cuts this year amid AI's impact on various sectors.
Amazon's CEO anticipates a smaller workforce in the future due to increased AI automation, with tasks shifting to generative AI tools and AI agents.
AI advancements are expected to lead to job redundancies in companies, requiring workforce adjustments across different industries.

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Siliconangle

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AWS to introduce updated server CPU, new AI training chip

Amazon Web Services Inc. is set to release an updated version of its AWS Graviton4 CPU and introduce a new chip in the Trainium series.
AWS' Annapurna Labs unit, acquired in 2015, develops both chip families.
Graviton4 is the fourth iteration with 96 cores based on Arm Holdings' Neoverse N2 design and increased cache size.
Graviton4 features cybersecurity optimizations, including BIT technology and encrypted data traffic interfaces.
The updated Graviton4 is expected to offer significantly improved network and memory bandwidth.
Trainium2, the upcoming AI training chip, is set to launch by the end of this year.
Trainium2 boasts 1.29 petaflops of performance with NeuronCore-V3 design and 96 gibibytes of memory.
Next-generation Trainium3 chip is anticipated to double performance compared to Trainium2 and be 50% more energy-efficient.
AWS utilizes other custom chips like the Nitro Security Chip and Nitro Cards for server protection and network management.
The Graviton4 release schedule and Trainium2 launch are to be announced soon.
AWS aims to enhance AI training performance and energy efficiency with its upcoming chips.
The Trainium2 chip allows developers to combine compute and memory resources for large-scale AI training projects.
AWS plans to continue innovating with custom processors to power its cloud platform.
The company's custom chips contribute to optimizing server security and network traffic management.
The continuous development of AWS chip technology signifies the importance of hardware optimization in cloud computing.
AWS remains committed to enhancing performance and security features through its custom-designed processors.

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Dbi-Services

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Experiencing SQLBits 2025 in London

Attending SQLBits 2025 conference in London for the second time, previously attended in 2022 virtually due to COVID-19 concerns.
SQLBits 2025 is a major Microsoft data platform event in Europe covering SQL Server, Azure, Power BI, AI, and data engineering.
Event runs from June 18 to 21, featuring numerous sessions, experts, MVPs, and Microsoft team members for learning and networking.
Participating in various sessions and a full-day training on data engineering at the conference.
Key trends include AI integration, Microsoft Fabric, Analytics, Power BI, DataOps, and Data Transformation.
Attended sessions on topics like Data API builder, Mirroring in Microsoft Fabric, AI Agents, Azure SQL Managed Instance, Real-Time Intelligence, B-Trees, V-Order, and Deadlocks resolution.
Impressed by the quality of sessions, expertise of speakers, and vast amount of information at the conference.
Highly recommends Microsoft Data professionals to consider attending SQLBits for learning and networking opportunities.

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Siliconangle

289

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From academic labs to banking: How private cloud is powering real-world change

Private cloud offers a middle path, combining flexibility and governance.
Broadcom's VMware Cloud Foundation 9 aims to eliminate tradeoffs, providing agility and security.
University of Bristol and Taipei Fubon Bank benefit from VCF for digital transformation.
Bristol University utilizes private cloud for flexibility, control, and cost benefits.
Automation through VCF enhances research capabilities and strategic value.
Taipei Fubon Bank consolidates infrastructure with VMware Cloud Foundation for efficiency.
The bank adopts a unified management layer for hybrid and private cloud operations.
Taipei Fubon invests in private AI models for rapid testing and flexibility.
Both cases demonstrate the advantages of private cloud in different sectors.
The discussions highlight how private cloud fosters innovation while maintaining control.

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