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Revealed: Big tech’s new datacentres will take water from the world’s driest areas

Big tech companies like Amazon, Microsoft, and Google are operating datacentres in some of the world's driest areas and are planning to build more, raising concerns about their impact on water scarcity.
Efforts to mitigate water use by Amazon have faced opposition from within, with concerns raised about the ethics of their plans.
Datacentres, crucial for remote data storage and processing, require water for cooling, with 38 active datacentres in water-scarce areas identified.
Amazon, Microsoft, and Google are planning a 78% increase in datacentres globally to meet the rising demand for storage, raising environmental concerns.
In regions facing water scarcity, the high water usage of datacentres poses significant challenges, with Microsoft and Google reporting percentages of water consumption in such areas.
Amazon's planned datacentres in Spain's Aragon region have raised concerns over water consumption and energy usage, impacting local communities.
There are worries about the ecological impact of datacentre expansions in dry regions, exacerbating water scarcity and environmental crises.
Amazon, Microsoft, and Google aim to become 'water positive' by 2030 through water offsetting initiatives, which have sparked debates about their effectiveness.
Google and Microsoft are also investing in efficient water usage strategies, amid growing concerns about the local and global implications of water-intensive datacentres.
Trump's 'Project Stargate' highlights the growing investments in AI infrastructure, with concerns raised about water usage and the sustainability of datacentre operations.

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Alvinashcraft

Dew Drop – April 9, 2025 (#4400)

Microsoft Copilot in Azure is now generally available, offering AI assistance in coding tasks.
The Syncfusion Blazor UI Kit aims to simplify development and eliminate UI challenges for developers.
Test out .NET 10 Previews quickly in Dev Containers and Codespaces.
Prototyping Web Apps with AI allows for pair programming with a robot companion.
Azure SDK for Rust moves into beta, providing Rust developers with more tools for Azure development.
Optimizing JSON Serialization in .NET compares Newtonsoft.Json and System.Text.Json for efficiency.
The future of Server-Driven Web Apps sees the rise of technologies like htmx and ASP.NET.
Avalonia Accelerate enhances the power and capabilities of Avalonia apps for developers.
Neural Network Quantile Regression and Linear Support Vector Regression using C# are explored.
AI-assisted Infrastructure as Code with Pulumi leverages the Model Context Protocol Server for improved automation.

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TechBullion

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Breaking the Cloud Barrier: AI’s Shift to Edge Computing

AI is transitioning from cloud computing to edge computing as it embeds itself into various devices and systems for real-time operations.
Edge computing, valued at nearly $34 billion, is transforming AI deployment in applications where low latency, energy efficiency, and reliability are essential.
Cloud dependency becomes a liability in scenarios like autonomous vehicles and industrial automation, accelerating AI's migration to the edge for quick decision-making.
Real-time AI applications in autonomous vehicles, industrial automation, smart cities, and healthcare require processing data locally to avoid delays and ensure accuracy and safety.
Edge AI offers advantages in speed, security, and reliability, but deploying machine learning models on embedded systems poses challenges due to hardware constraints.
Optimizing AI models for edge devices involves addressing power consumption, memory efficiency, and computational constraints to enable efficient operations.
Techniques like quantization, pruning, and knowledge distillation help reduce the computational load of AI models on low-power processors without compromising accuracy.
Multi-sensor fusion, as seen in autonomous vehicles, requires seamless integration of diverse inputs in real time to enhance perception accuracy and minimize latency.
The future of edge AI will rely on advancements in low-power AI chips, neural processing units, and embedded AI frameworks to enable AI capabilities in smart sensors and wearables.
Edge AI offers benefits such as reduced operational costs, improved data security, privacy compliance, and resilience, even in the face of cloud connectivity loss.

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Tech Radar

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The Wizard of Oz is coming to the Las Vegas sphere in 16K thanks to the power of Google DeepMind AI

The Wizard of Oz is being reimagined for the 16k spherical screen at the Las Vegas Sphere thanks to Google's AI technology.
The film opens on August 28 and is a collaboration between Google DeepMind, Google Cloud, Sphere Studios, and Warner Bros. Discovery.
Google is using generative AI to create an immersive experience with iconic characters like Dorothy and Toto.
The project involves transforming the original 1939 footage into ultra-high definition imagery using AI tools like Veo, Imagen, and Gemini.
AI outpainting is used to expand scenes and make the shots seamless on the high-resolution screen at The Sphere.
Despite the advanced technology, the team behind the project ensures to maintain the essence and soul of the original film.
Additional materials like the shooting script and production illustrations are used to improve the quality and details of the reimagined version.
The immersive experience aims to bring the magic of The Wizard of Oz to the modern era while honoring its cinematic traditions.
The production is led by Buzz Hays, a veteran producer in Hollywood, working with a team of visual effects artists and AI models.
The success of similar projects like Wicked has paved the way for reimagining iconic stories using cutting-edge technology.

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Tech Radar

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Nvidia RTX 5060 Ti price rumor is what all gamers want to hear – these models may be cheaper than RTX 4060 Ti equivalents, especially the 16GB GPU

Rumors suggest Nvidia may lower the prices of RTX 5060 Ti models, with the 8GB version priced at $379 and the 16GB version at $429.
This pricing speculation comes from discussions on the Board Channels forum in China, known for reliable information from the supply chain.
Compared to the previous RTX 4060 Ti models, the potential prices of the RTX 5060 Ti models are lower, offering more affordability.
The RTX 5060 Ti launch is rumored to be in the middle of the month, with a possible sale date of April 16.
There is anticipation for the release of the RTX 5060 vanilla graphics card but its price remains undisclosed, suggesting a later launch.
Pre-release pricing rumors can be uncertain, but the speculation from the Board Channels forum adds some credibility to the potential price adjustments.
If the rumored prices come to fruition, Nvidia's adjustment could be seen as an attempt to win favor with gamers amidst ongoing supply challenges.
Lower pricing for the RTX 5060 Ti models could impact the positioning of higher-tier GPUs like the RTX 5070, influencing the market dynamics.
The speculated pricing shift may indicate a strategic decision by Nvidia to balance performance and pricing in its GPU lineup.
The potential launch of the RTX 5060 Ti models next week will provide more clarity on Nvidia's pricing strategy and product positioning in the market.

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Silicon

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Silicon In Focus Podcast: From Hype to Reality – The Real ROI of AI in Business

This episode of Silicon In Focus explores the real ROI of AI in business and separates hype from reality.
Topics discussed include setting realistic expectations for AI investments, measuring ROI effectively, and identifying the right metrics to track progress.
The episode also highlights leading industries in AI adoption and the barriers to widespread implementation.
Listeners will gain insights on striking a balance between quick wins and long-term strategic bets, as well as emerging trends in AI.

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Digitaltrends

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Nvidia’s RTX 5060 Ti might be cheaper than expected

Nvidia's RTX 5060 Ti might have a lower price than expected
Leak suggests the prices for the 8GB and 16GB versions to be $379 and $429 respectively
The potential price cut would be welcomed by gamers amid the ongoing GPU shortage
Speculation remains until Nvidia makes an official announcement

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Dev

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# 🚀 What Are Amazon ECS and ECR? A Beginner-Friendly Guide to Docker on AWS

Amazon ECS (Elastic Container Service) and Amazon ECR (Elastic Container Registry) are AWS services for running and managing Docker containers on AWS.
Amazon ECR is AWS's private container image repository, providing secure storage, access permission management, and seamless integration with other AWS services.
Amazon ECS is AWS's service for running and managing Docker containers at scale, taking care of orchestration tasks such as scheduling, scaling, and monitoring.
To use Docker on AWS, the typical workflow involves writing a Dockerfile, building and pushing the image to ECR, creating a Task Definition in ECS, and running the app via a Task or Service.

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Siliconangle

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Amazon Q Developer coding assistant now speaks multiple languages for coders worldwide

Amazon Q Developer, a generative AI-powered assistant for software engineers, now supports multiple written languages.
The new multilingual capability enables diverse multilingual teams and helps make development more inclusive.
Developers can use inline chat to communicate with Q Developer in their native language, eliminating the need for constant translation.
Expanded language support is available for integrated development environments and command line interfaces, with support for AWS Management Console coming soon.

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Medium

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Modernizing PLM Integrations with Serverless Cloud Services

PLM integrations with serverless cloud services offer a solution to overcome scalability limitations, high infrastructure costs, and maintenance overhead.
Using serverless compute, such as Azure Functions, Service Bus, and Logic Apps, offers better separation of concerns, scalability, and maintainability.
Replacing traditional middleware services with cloud native services, like Azure APIM, provides enhanced access control, response enrichment, and lightweight transformation.
Replacing file-based exports with cloud-based services unlocks capabilities such as auto-scaling, real-time processing, better security, and modern observability.

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Tech Radar

374

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We now have midrange GPUs launching at almost $1000 - PowerColor takes the Radeon RX 9070 XT to an absurd price point, and it's an ill omen of times to come

PowerColor has launched a new Radeon RX 9070 XT Red Devil Spectral White GPU at $899.99.
Best Buy features even older RDNA 3 GPUs at high prices above their MSRP.
The GPU market is experiencing another huge price hike on an AMD Radeon RX 9070 XT partner card.
The current state of the GPU market is predicted to lead to a graphics market crash.

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Tech Radar

216

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Nvidia RTX 5060 series shipping leak reveals the same memory bus as last-gen - but it's not as bad as you may think

A leak suggests Nvidia's RTX 5060 series will have the same 128-bit memory bus as the previous generation.
The RTX 5060 and 5060 Ti will reportedly feature GDDR7 VRAM, potentially offering higher speeds and bandwidth compared to their predecessors.
The entry-level GPUs' success in the market may depend on pricing, considering the challenges in availability and scalping.
Nvidia's upcoming GPUs, if priced competitively, could provide a boost for budget gamers facing GPU market challenges.

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Dev

220

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"How we automated ServiceNow change approvals using GPT-4 and AWS APIs."

Automating ServiceNow change approvals using AI-driven LLMs and AWS APIs can enhance efficiencies in change management processes in IT operations.
Benefits of automating change management include faster deployments, consistency in compliance, risk reduction, and cost efficiency.
Use cases for AI-powered change management span cloud infrastructure, networking, CI/CD pipelines, and database/storage changes.
Key components of the solution architecture include ServiceNow Change Module, LLM analysis, Cloud APIs, CI/CD Integration, and Approval Workflow.
Implementation steps involve training the LLM, integrating with ServiceNow, connecting to cloud APIs, setting up guardrails, and continuous monitoring and refinement.
Challenges like false positives/negatives, regulatory compliance, and integration complexity can be mitigated by starting small, maintaining human oversight, and using pre-built connectors.
The end-to-end AI-powered change management workflow includes high-level architecture diagrams and detailed workflow steps involving AI agents, integration points, and feedback loops.
AI agents can be hosted in the cloud (GPT-4, Bedrock), on-premises (Llama 3, Claude), or in a hybrid setup, each with its pros and cons.
Key integration points include ServiceNow API, AWS/Azure APIs, CI/CD tools, and monitoring tools for seamless automation of change approvals.
AI-powered change management streamlines processes, reduces manual work, accelerates deployments, and integrates LLMs to automate approvals while involving humans for critical decisions.
The article emphasizes piloting AI approvals for non-critical changes, measuring savings, and scaling for more complex workflows in IT change management.

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Medium

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Code in Motion: A Journey Through Software Development Methodologies.

Software Development Methodologies like Agile, DevOps, Microservices, and Serverless are explored in the article, highlighting their impact on software development.
Agile emphasizes quick work, clear goals, and small wins that build trust among team members through short cycles and frequent meetings.
DevOps focuses on collaboration and automation, bridging gaps between teams to fix code quickly and efficiently.
Microservices involve splitting work into small, clear parts that join to form a cohesive code base, emphasizing the importance of individual tasks.
Serverless promotes lean, scalable, and light code that runs without heavy tasks, freeing teams to work on clear, fast steps.
Integrating Agile, DevOps, Microservices, and Serverless practices leads to clear, strong code bases as different methodologies complement each other.
The article emphasizes the importance of teamwork, smart builds, and the need for open discussion and sharing of ideas in software development.
Looking ahead, the article envisions a future where new tools and methods will continue to enhance software development with a focus on trust, clear work, and bright ideas.
The call to action encourages readers to engage in discussions, share their views, and contribute to the ongoing evolution of software development practices.
The article concludes by emphasizing the importance of keeping the code alive through trust, clear steps, and the collaborative efforts of all team members.
Overall, the article serves as an invitation for readers to participate in shaping the future of software development by fostering innovation, trust, and shared knowledge.

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Dev

165

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Guide to Dify 1.1.3 on AWS using docker-compose, connected to Postgres RDS, with TLS through an AWS ALB

Setting up Dify 1.1.3 on AWS involves using docker-compose, connecting to Postgres RDS with TLS through an AWS ALB.
Storing data in RDS provides benefits such as data persistence during Dify updates and security features like high availability and easy backups.
Requirements include an EC2 instance, EBS storage, an EC2 instance profile, an ALB for TLS connection, and a Postgres RDS instance.
Installation steps involve setting up docker-compose, installing AWS CLI v2, setting up Dify and the database, and creating the .env file.
Changes need to be made to the docker-compose.yaml file to connect Dify to RDS and set up SSL requirements for the plugin daemon.
A vector store like pgvector can be set up in RDS to handle vector embeddings for enhanced search capabilities within Dify.
Starting up Dify with docker-compose involves running the command 'docker-compose -f docker-compose.yaml up -d'.
Troubleshooting tips include checking for DB-related errors, monitoring memory and storage usage, and pruning old images and volumes.
For further assistance or queries, the author welcomes comments and interaction to address any issues that may arise during the setup process.
The guide provides a detailed walkthrough of setting up Dify on AWS using docker-compose and RDS with emphasis on security and data persistence.

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