#GPU-based cloud computing

[sharon-ai]

Unveiling the Future of AI: Why Sharon AI is the Game-Changer You Need to Know

Artificial Intelligence (AI) is no longer just a buzzword; it’s the backbone of innovation in industries ranging from healthcare to finance. As businesses look to scale and innovate, leveraging advanced AI services has become crucial. Enter Sharon AI, a cutting-edge platform that’s reshaping how organizations harness AI’s potential. If you haven’t heard of Sharon AI yet, it’s time to dive in.

Why AI is Essential in Today’s World

The adoption of artificial intelligence has skyrocketed over the past decade. From chatbots to complex data analytics, AI is driving efficiency, accuracy, and innovation. Businesses that leverage AI are not just keeping up; they’re leading their industries. However, one challenge remains: finding scalable, high-performance computing solutions tailored to AI.

That’s where Sharon AI steps in. With its GPU-based computing infrastructure, the platform offers solutions that are not only powerful but also sustainable, addressing the growing need for eco-friendly tech.

What Sets Sharon AI Apart?

Sharon AI specializes in providing advanced compute infrastructure for high-performance computing (HPC) and AI applications. Here’s why Sharon AI stands out:

Scalability: Whether you’re a startup or a global enterprise, Sharon AI offers flexible solutions to match your needs.

Sustainability: Their commitment to building net-zero energy data centers, like the 250 MW facility in Texas, highlights a dedication to green technology.

State-of-the-Art GPUs: Incorporating NVIDIA H100 GPUs ensures top-tier performance for AI and HPC workloads.

Reliability: Operating from U.S.-based data centers, Sharon AI guarantees secure and efficient service delivery.

Services Offered by Sharon AI

Sharon AI’s offerings are designed to empower businesses in their AI journey. Key services include:

GPU Cloud Computing: Scalable GPU resources tailored for AI and HPC applications.

Sustainable Data Centers: Energy-efficient facilities ensuring low carbon footprints.

Custom AI Solutions: Tailored services to meet industry-specific needs.

24/7 Support: Expert assistance to ensure seamless operations.

Why Businesses Are Turning to Sharon AI

Businesses today face growing demands for data-driven decision-making, predictive analytics, and real-time processing. Traditional computing infrastructure often falls short, making Sharon AI’s advanced solutions a must-have for enterprises looking to stay ahead.

For instance, industries like healthcare benefit from Sharon AI’s ability to process massive datasets quickly and accurately, while financial institutions use their solutions to enhance fraud detection and predictive modeling.

The Growing Demand for AI Services

Searches related to AI solutions, HPC platforms, and sustainable computing are increasing as businesses seek reliable providers. By offering innovative solutions, Sharon AI is positioned as a leader in this space.If you’re searching for providers or services such as GPU cloud computing, NVIDIA GPU solutions, or AI infrastructure services, Sharon AI is a name you’ll frequently encounter. Their offerings are designed to cater to the rising demand for efficient and sustainable AI computing solutions.

[govindhtech]

Tech Breakdown: What Is a SuperNIC? Get the Inside Scoop!

The most recent development in the rapidly evolving digital realm is generative AI. A relatively new phrase, SuperNIC, is one of the revolutionary inventions that makes it feasible.

Describe a SuperNIC

On order to accelerate hyperscale AI workloads on Ethernet-based clouds, a new family of network accelerators called SuperNIC was created. With remote direct memory access (RDMA) over converged Ethernet (RoCE) technology, it offers extremely rapid network connectivity for GPU-to-GPU communication, with throughputs of up to 400Gb/s.

SuperNICs incorporate the following special qualities:

Ensuring that data packets are received and processed in the same sequence as they were originally delivered through high-speed packet reordering. This keeps the data flow’s sequential integrity intact.

In order to regulate and prevent congestion in AI networks, advanced congestion management uses network-aware algorithms and real-time telemetry data.

In AI cloud data centers, programmable computation on the input/output (I/O) channel facilitates network architecture adaptation and extension.

Low-profile, power-efficient architecture that effectively handles AI workloads under power-constrained budgets.

Optimization for full-stack AI, encompassing system software, communication libraries, application frameworks, networking, computing, and storage.

Recently, NVIDIA revealed the first SuperNIC in the world designed specifically for AI computing, built on the BlueField-3 networking architecture. It is a component of the NVIDIA Spectrum-X platform, which allows for smooth integration with the Ethernet switch system Spectrum-4.

The NVIDIA Spectrum-4 switch system and BlueField-3 SuperNIC work together to provide an accelerated computing fabric that is optimized for AI applications. Spectrum-X outperforms conventional Ethernet settings by continuously delivering high levels of network efficiency.

Yael Shenhav, vice president of DPU and NIC products at NVIDIA, stated, “In a world where AI is driving the next wave of technological innovation, the BlueField-3 SuperNIC is a vital cog in the machinery.” “SuperNICs are essential components for enabling the future of AI computing because they guarantee that your AI workloads are executed with efficiency and speed.”

The Changing Environment of Networking and AI

Large language models and generative AI are causing a seismic change in the area of artificial intelligence. These potent technologies have opened up new avenues and made it possible for computers to perform new functions.

GPU-accelerated computing plays a critical role in the development of AI by processing massive amounts of data, training huge AI models, and enabling real-time inference. While this increased computing capacity has created opportunities, Ethernet cloud networks have also been put to the test.

The internet’s foundational technology, traditional Ethernet, was designed to link loosely connected applications and provide wide compatibility. The complex computational requirements of contemporary AI workloads, which include quickly transferring large amounts of data, closely linked parallel processing, and unusual communication patterns all of which call for optimal network connectivity were not intended for it.

Basic network interface cards (NICs) were created with interoperability, universal data transfer, and general-purpose computing in mind. They were never intended to handle the special difficulties brought on by the high processing demands of AI applications.

The necessary characteristics and capabilities for effective data transmission, low latency, and the predictable performance required for AI activities are absent from standard NICs. In contrast, SuperNICs are designed specifically for contemporary AI workloads.

Benefits of SuperNICs in AI Computing Environments

Data processing units (DPUs) are capable of high throughput, low latency network connectivity, and many other sophisticated characteristics. DPUs have become more and more common in the field of cloud computing since its launch in 2020, mostly because of their ability to separate, speed up, and offload computation from data center hardware.

SuperNICs and DPUs both have many characteristics and functions in common, however SuperNICs are specially designed to speed up networks for artificial intelligence.

The performance of distributed AI training and inference communication flows is highly dependent on the availability of network capacity. Known for their elegant designs, SuperNICs scale better than DPUs and may provide an astounding 400Gb/s of network bandwidth per GPU.

When GPUs and SuperNICs are matched 1:1 in a system, AI workload efficiency may be greatly increased, resulting in higher productivity and better business outcomes.

SuperNICs are only intended to speed up networking for cloud computing with artificial intelligence. As a result, it uses less processing power than a DPU, which needs a lot of processing power to offload programs from a host CPU.

Less power usage results from the decreased computation needs, which is especially important in systems with up to eight SuperNICs.

One of the SuperNIC’s other unique selling points is its specialized AI networking capabilities. It provides optimal congestion control, adaptive routing, and out-of-order packet handling when tightly connected with an AI-optimized NVIDIA Spectrum-4 switch. Ethernet AI cloud settings are accelerated by these cutting-edge technologies.

Transforming cloud computing with AI

The NVIDIA BlueField-3 SuperNIC is essential for AI-ready infrastructure because of its many advantages.

Maximum efficiency for AI workloads: The BlueField-3 SuperNIC is perfect for AI workloads since it was designed specifically for network-intensive, massively parallel computing. It guarantees bottleneck-free, efficient operation of AI activities.

Performance that is consistent and predictable: The BlueField-3 SuperNIC makes sure that each job and tenant in multi-tenant data centers, where many jobs are executed concurrently, is isolated, predictable, and unaffected by other network operations.

Secure multi-tenant cloud infrastructure: Data centers that handle sensitive data place a high premium on security. High security levels are maintained by the BlueField-3 SuperNIC, allowing different tenants to cohabit with separate data and processing.

Broad network infrastructure: The BlueField-3 SuperNIC is very versatile and can be easily adjusted to meet a wide range of different network infrastructure requirements.

Wide compatibility with server manufacturers: The BlueField-3 SuperNIC integrates easily with the majority of enterprise-class servers without using an excessive amount of power in data centers.

[uniquelish]

World's Most Powerful Business Leaders: Insights from Visionaries Across the Globe

In the fast-evolving world of business and innovation, visionary leadership has become the cornerstone of driving global progress. Recently, Fortune magazine recognized the world's most powerful business leaders, acknowledging their transformative influence on industries, economies, and societies.

Among these extraordinary figures, Elon Musk emerged as the most powerful business leader, symbolizing the future of technological and entrepreneurial excellence.

Elon Musk: The Game-Changer

Elon Musk, the CEO of Tesla, SpaceX, and X (formerly Twitter), has redefined innovation with his futuristic endeavors. From pioneering electric vehicles at Tesla to envisioning Mars colonization with SpaceX, Musk's revolutionary ideas continue to shape industries. Recognized as the most powerful business leader by Fortune, his ventures stand as a testament to what relentless ambition and innovation can achieve. Digital Fraud and Cybercrime: India Blocks 59,000 WhatsApp Accounts and 6.7 Lakh SIM Cards Also Read This....

Musk's influence extends beyond his corporate achievements. As a driver of artificial intelligence and space exploration, he inspires the next generation of leaders to push boundaries. His leadership exemplifies the power of daring to dream big and executing with precision.

Mukesh Ambani: The Indian Powerhouse

Mukesh Ambani, the chairman of Reliance Industries, represents the epitome of Indian business success. Ranked among the top 15 most powerful business leaders globally, Ambani has spearheaded transformative projects in telecommunications, retail, and energy, reshaping India's economic landscape. His relentless focus on innovation, particularly with Reliance Jio, has revolutionized the digital ecosystem in India.

Under his leadership, Reliance Industries has expanded its global footprint, setting new benchmarks in business growth and sustainability. Ambani’s vision reflects the critical role of emerging economies in shaping the global business narrative.

Defining Powerful Leadership

The criteria for identifying powerful business leaders are multifaceted. According to Fortune, leaders were evaluated based on six key metrics:

Business Scale: The size and impact of their ventures on a global level.

Innovation: Their ability to pioneer advancements that redefine industries.

Influence: How effectively they inspire others and create a lasting impact.

Trajectory: The journey of their career and the milestones achieved.

Business Health: Metrics like profitability, liquidity, and operational efficiency.

Global Impact: Their contribution to society and how their leadership addresses global challenges.

Elon Musk and Mukesh Ambani exemplify these qualities, demonstrating how strategic vision and innovative execution can create monumental change.

Other Global Icons in Leadership

The list of the world's most powerful business leaders features numerous iconic personalities, each excelling in their respective domains:

Satya Nadella (Microsoft): A transformative leader who has repositioned Microsoft as a cloud-computing leader, emphasizing customer-centric innovation.

Sundar Pichai (Alphabet/Google): A driving force behind Google’s expansion into artificial intelligence, cloud computing, and global digital services.

Jensen Huang (NVIDIA): The architect of the AI revolution, whose GPUs have become indispensable in AI-driven industries.

Tim Cook (Apple): Building on Steve Jobs' legacy, Cook has solidified Apple as a leader in innovation and user-centric design.

These leaders have shown that their influence isn’t confined to financial success alone; it extends to creating a better future for the world.

Leadership in Action: Driving Innovation and Progress

One common thread unites these leaders—their ability to drive innovation. For example:

Mary Barra (General Motors) is transforming the auto industry with her push toward electric vehicles, ensuring a sustainable future.

Sam Altman (OpenAI) leads advancements in artificial intelligence, shaping ethical AI practices with groundbreaking models like ChatGPT.

These visionaries have proven that impactful leadership is about staying ahead of trends, embracing challenges, and delivering solutions that inspire change.

The Indian Connection: Rising Global Influence

Apart from Mukesh Ambani, Indian-origin leaders such as Sundar Pichai and Satya Nadella have earned global recognition. Their ability to bridge cultural boundaries and lead multinational corporations demonstrates the increasing prominence of Indian talent on the world stage.

Conclusion

From technological advancements to economic transformation, these powerful business leaders are shaping the future of our world. Elon Musk and Mukesh Ambani stand at the forefront, representing the limitless potential of visionary leadership. As industries continue to evolve, their impact serves as a beacon for aspiring leaders worldwide.

This era of leadership emphasizes not only achieving success but also leveraging it to create meaningful change. In the words of Elon Musk: "When something is important enough, you do it even if the odds are not in your favor." Rajkot Job Update

[mosaicp]

wait can i talk my shit for a sec part of the reason that the new generation is so tech illiterate is because of those shitty ass chromebooks because they're cloud-based and you can't really open up a terminal or like a proper settings or anything and while I know not every school has the budget for bulkier thick client computers and that stuff is restricted as a security thing but a worrying amount of people don't even know about basic components (cpu, ram, gpu, etc) and I feel like we need to fund better education on these types of things and I know some schools have pilot programs (army jrotc cyber for example) but this needs to be a more widespread thing!!!! you don't even need to go that much in-depth just like "ok kids this is a terminal" even make it a game like a little flash game or something!!!! I'm tired of people seeing a command line and/or inspect element and immediately go "omg they're hacking!!!!!!1!111!!!!" and that's lowk the media's fault with the "hacker" character in the 90s and 00s "I'm into the mainframe!" and that along with the tech industry boom really made compsci and cysec seem really inaccessible which it really isn't at the basic level! but alas, the things I've suggested aren't in place right now so go out and educate yourself! I don't have any resources to do that (so much for practicing what I preach) but if anyone has any that'd be really cool. the rise of GUI and cloud-based systems isn't completely at fault but it is still a part of the problem

via mosaic twitter

[nebumine]

Bitcoin Mining

The Evolution of Bitcoin Mining: From Solo Mining to Cloud-Based Solutions

Introduction

Bitcoin mining has come a long way since its early days when individuals could mine BTC using personal computers. Over the years, advancements in technology and increasing network difficulty have led to the rise of more sophisticated mining methods. Today, cloud mining solutions like NebuMine are revolutionizing cryptocurrency mining by making it more accessible and efficient. This article explores the journey of Bitcoin mining, from solo efforts to large-scale cloud mining operations.

The Early Days of Bitcoin Mining

In the beginning, Bitcoin mining was simple. Miners could use regular CPUs to solve cryptographic puzzles and validate transactions. However, as more participants joined the network, mining difficulty increased, leading to the adoption of more powerful GPUs.

As BTC mining grew, miners began forming mining pools to combine computing power and share rewards. This shift marked the transition from individual mining to more collective efforts in cryptocurrency mining.

The Rise of ASIC Mining

The introduction of Application-Specific Integrated Circuits (ASICs) in Bitcoin mining changed the game completely. These highly specialized machines offered unmatched efficiency, significantly increasing mining power while consuming less energy than GPUs.

However, ASICs also made mining more competitive, pushing small-scale miners out of the market. This led to the rise of large mining farms, further centralizing BTC mining operations.

The Shift to Cloud Mining

As the mining landscape became more challenging, cloud mining emerged as a viable alternative. Instead of investing in expensive hardware, users could rent mining power from platforms like NebuMine, enabling them to participate in Bitcoin mining without technical expertise or maintenance costs.

Cloud mining offers several advantages:

Accessibility: Users can start crypto mining without purchasing expensive equipment.

Scalability: Miners can adjust their computing power based on market conditions.

Convenience: No need for hardware setup, electricity costs, or cooling management.

With platforms like NebuMine, cloud mining has become a practical way for individuals and businesses to engage in BTC mining and Ethereum mining without the hassle of traditional setups.

Ethereum Mining and the Future of Crypto Mining

While Bitcoin mining has dominated the industry, Ethereum mining has also played a crucial role in the crypto space. With Ethereum’s shift to Proof-of-Stake (PoS), many miners have sought alternatives, further driving interest in cloud mining services.

Cryptocurrency mining continues to evolve, with new innovations such as AI-driven mining optimization and decentralized mining pools shaping the future. Platforms like NebuMine are at the forefront of this transformation, making cloud mining more accessible, efficient, and sustainable.

Conclusion

The evolution of Bitcoin mining highlights the industry's rapid advancements, from solo mining to industrial-scale operations and now cloud mining. As technology continues to advance, cloud mining solutions like NebuMine are paving the way for the future of cryptocurrency mining, making it easier for users to participate in BTC mining and Ethereum mining without technical barriers.

Check out our website to get more information about Cryptocurrency mining!

[realcleverscience]

AI & Data Centers vs Water + Energy

We all know that AI has issues, including energy and water consumption. But these fields are still young and lots of research is looking into making them more efficient. Remember, most technologies tend to suck when they first come out.

Deploying high-performance, energy-efficient AI

"You give up that kind of amazing general purpose use like when you're using ChatGPT-4 and you can ask it everything from 17th century Italian poetry to quantum mechanics, if you narrow your range, these smaller models can give you equivalent or better kind of capability, but at a tiny fraction of the energy consumption," says Ball."...

"I think liquid cooling is probably one of the most important low hanging fruit opportunities... So if you move a data center to a fully liquid cooled solution, this is an opportunity of around 30% of energy consumption, which is sort of a wow number.... There's more upfront costs, but actually it saves money in the long run... One of the other benefits of liquid cooling is we get out of the business of evaporating water for cooling...

The other opportunity you mentioned was density and bringing higher and higher density of computing has been the trend for decades. That is effectively what Moore's Law has been pushing us forward... [i.e. chips rate of improvement is faster than their energy need growths. This means each year chips are capable of doing more calculations with less energy. - RCS] ... So the energy savings there is substantial, not just because those chips are very, very efficient, but because the amount of networking equipment and ancillary things around those systems is a lot less because you're using those resources more efficiently with those very high dense components"

New tools are available to help reduce the energy that AI models devour

"The trade-off for capping power is increasing task time — GPUs will take about 3 percent longer to complete a task, an increase Gadepally says is "barely noticeable" considering that models are often trained over days or even months... Side benefits have arisen, too. Since putting power constraints in place, the GPUs on LLSC supercomputers have been running about 30 degrees Fahrenheit cooler and at a more consistent temperature, reducing stress on the cooling system. Running the hardware cooler can potentially also increase reliability and service lifetime. They can now consider delaying the purchase of new hardware — reducing the center's "embodied carbon," or the emissions created through the manufacturing of equipment — until the efficiencies gained by using new hardware offset this aspect of the carbon footprint. They're also finding ways to cut down on cooling needs by strategically scheduling jobs to run at night and during the winter months."

AI just got 100-fold more energy efficient

Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies...

“Today, most sensors collect data and then send it to the cloud, where the analysis occurs on energy-hungry servers before the results are finally sent back to the user,” said Northwestern’s Mark C. Hersam, the study’s senior author. “This approach is incredibly expensive, consumes significant energy and adds a time delay...

For current silicon-based technologies to categorize data from large sets like ECGs, it takes more than 100 transistors — each requiring its own energy to run. But Northwestern’s nanoelectronic device can perform the same machine-learning classification with just two devices. By reducing the number of devices, the researchers drastically reduced power consumption and developed a much smaller device that can be integrated into a standard wearable gadget."

Researchers develop state-of-the-art device to make artificial intelligence more energy efficient

""This work is the first experimental demonstration of CRAM, where the data can be processed entirely within the memory array without the need to leave the grid where a computer stores information,"...

According to the new paper's authors, a CRAM-based machine learning inference accelerator is estimated to achieve an improvement on the order of 1,000. Another example showed an energy savings of 2,500 and 1,700 times compared to traditional methods"

[lyingbard]

Update: Patience Fans Winning Again (Import Poll Decisions Too!)

So I've got a version working with all the features for a solid beta, but... only on my computer.

It should work when I set it up with all the cloud nonsense it needs, but it costs money, I don't wanna spend money right now, and I can't really make you pay for a product I'm not sure will work right.

So here's the deal: I'm starting a paid internship soon, so once I have that income I'll be willing to spend money on this. Problem: it's a National Parks internship so I won't have internet or the computer that I make LyingBard with until I get back... in 3-6 months.

But! I also want to ask you an important question...

I've been making LyingBard as a Cloud-Based app. It allows you to use it from anywhere through my website, but it also means I incur the cost of finding GPUs to run this with while you have a perfectly good GPU sitting in your computer doing nothing (also cloud apps are a pain in the ass).

I could also make it a Desktop App, but it would be about 1GB (blame PyTorch), it would only run on NVIDIA GPUs, and you'd have to set up your own discord bots and stuff (although I can try and streamline that for you).

Another big problem is money. Cloud-Based stuff costs money to run and becomes basically a job even if it doesn't pay enough. I'm considering a career in the National Parks Service so I won't be available most of the time if something goes wrong and stuff can go VERY wrong with a paid cloud app (think leaked passwords, compromised payment processor (Stripe) account, that sort of thing).

A Desktop App, however, can be left alone without spontaneously deleting itself or attacking you, but I would certainly have to focus on a career unless you donated a whole heck of a lot.

So DECIDE TIME! After carefully (or carelessly, idc) considering these options. WHICH DO YOU CHOOSE?!

Unfortunately, LyingBard isn't releasing until I get back regardless. Even the desktop app is just a bit too much work for me to get done before I leave.

[mvishnukumar]

How can you optimize the performance of machine learning models in the cloud?

Optimizing machine learning models in the cloud involves several strategies to enhance performance and efficiency. Here’s a detailed approach:

Choose the Right Cloud Services:

Managed ML Services: 

Use managed services like AWS SageMaker, Google AI Platform, or Azure Machine Learning, which offer built-in tools for training, tuning, and deploying models.

Auto-scaling: 

Enable auto-scaling features to adjust resources based on demand, which helps manage costs and performance.

Optimize Data Handling:

Data Storage: 

Use scalable cloud storage solutions like Amazon S3, Google Cloud Storage, or Azure Blob Storage for storing large datasets efficiently.

Data Pipeline: 

Implement efficient data pipelines with tools like Apache Kafka or AWS Glue to manage and process large volumes of data.

Select Appropriate Computational Resources:

Instance Types: 

Choose the right instance types based on your model’s requirements. For example, use GPU or TPU instances for deep learning tasks to accelerate training.

Spot Instances: 

Utilize spot instances or preemptible VMs to reduce costs for non-time-sensitive tasks.

Optimize Model Training:

Hyperparameter Tuning: 

Use cloud-based hyperparameter tuning services to automate the search for optimal model parameters. Services like Google Cloud AI Platform’s HyperTune or AWS SageMaker’s Automatic Model Tuning can help.

Distributed Training: 

Distribute model training across multiple instances or nodes to speed up the process. Frameworks like TensorFlow and PyTorch support distributed training and can take advantage of cloud resources.

Monitoring and Logging:

Monitoring Tools: 

Implement monitoring tools to track performance metrics and resource usage. AWS CloudWatch, Google Cloud Monitoring, and Azure Monitor offer real-time insights.

Logging: 

Maintain detailed logs for debugging and performance analysis, using tools like AWS CloudTrail or Google Cloud Logging.

Model Deployment:

Serverless Deployment: 

Use serverless options to simplify scaling and reduce infrastructure management. Services like AWS Lambda or Google Cloud Functions can handle inference tasks without managing servers.

Model Optimization: 

Optimize models by compressing them or using model distillation techniques to reduce inference time and improve latency.

Cost Management:

Cost Analysis: 

Regularly analyze and optimize cloud costs to avoid overspending. Tools like AWS Cost Explorer, Google Cloud’s Cost Management, and Azure Cost Management can help monitor and manage expenses.

By carefully selecting cloud services, optimizing data handling and training processes, and monitoring performance, you can efficiently manage and improve machine learning models in the cloud.

[webyildiz]

Intel Xeon is a series of server and workstation CPUs (central processing units) designed and manufactured by Intel. These processors are specifically built for demanding workloads, such as those commonly used in data centers, enterprise-level computing tasks, and high-performance computing. Xeon processors typically have higher core counts, larger cache sizes, and support for more memory than consumer-grade CPUs, as well as features that enhance reliability and security for mission-critical applications. Certainly! Here's an ultimate guide about Intel Xeon processors: Overview: Intel Xeon processors are designed for server and workstation environments, emphasizing performance, reliability, and scalability. Xeon processors are part of Intel's lineup of high-performance CPUs and are optimized for demanding workloads, such as data centers, cloud computing, virtualization, scientific research, and professional applications. Performance and Architecture: Xeon processors are built on the x86 architecture, which provides compatibility with a wide range of software applications. They feature multiple cores and threads, allowing for parallel processing and improved multitasking capabilities. Xeon processors often have larger cache sizes compared to consumer-grade processors, enabling faster access to frequently used data. They support technologies like Turbo Boost, which dynamically increases clock speeds for improved performance, and Hyper-Threading, which allows each physical core to handle multiple threads simultaneously. Generational Improvements: Intel releases new generations of Xeon processors regularly, introducing enhancements in performance, power efficiency, and feature sets. Each generation may be based on a different microarchitecture, such as Haswell, Broadwell, Skylake, Cascade Lake, Ice Lake, etc. Newer generations often offer higher core counts, improved clock speeds, larger cache sizes, and support for faster memory and storage technologies. Enhanced security features, such as Intel Software Guard Extensions (SGX) and Intel Trusted Execution Technology (TXT), are also introduced in newer Xeon processors. Product Segments: Intel categorizes Xeon processors into various product segments based on performance and capabilities. Entry-level Xeon processors provide basic server functionality and are suitable for small businesses, low-demand workloads, and cost-sensitive environments. Mid-range and high-end Xeon processors offer more cores, higher clock speeds, larger caches, and advanced features like support for multiple sockets, massive memory capacities, and advanced virtualization capabilities. Intel also offers specialized Xeon processors for specific workloads, such as Xeon Phi processors for high-performance computing (HPC) and Xeon Scalable processors for data centers and cloud computing. Memory and Connectivity: Xeon processors support various generations of DDR memory, including DDR3, DDR4, and, in more recent models, DDR5. They typically offer support for large memory capacities, allowing servers to accommodate extensive data sets and run memory-intensive applications efficiently. Xeon processors feature multiple high-speed PCIe lanes for connecting peripherals like storage devices, network cards, and GPUs, facilitating high-performance data transfer. Software Ecosystem and Support: Xeon processors are compatible with a wide range of operating systems, including Windows Server, Linux distributions, and virtualization platforms like VMware and Hyper-V. They are well-supported by software vendors and have extensive compatibility with server-class applications, databases, and enterprise software. Intel provides regular firmware updates, software optimization tools, and developer resources to ensure optimal performance and compatibility with Xeon processors. When choosing an Intel

Xeon processor, consider factors such as workload requirements, core counts, clock speeds, memory support, and specific features needed for your application. It's also important to check Intel's product documentation and consult with hardware experts to select the appropriate Xeon processor model for your server or workstation setup.

[govindhtech]

A3 Ultra VMs With NVIDIA H200 GPUs Pre-launch This Month

Strong infrastructure advancements for your future that prioritizes AI

To increase customer performance, usability, and cost-effectiveness, Google Cloud implemented improvements throughout the AI Hypercomputer stack this year. Google Cloud at the App Dev & Infrastructure Summit:

Trillium, Google’s sixth-generation TPU, is currently available for preview.

Next month, A3 Ultra VMs with NVIDIA H200 Tensor Core GPUs will be available for preview.

Google’s new, highly scalable clustering system, Hypercompute Cluster, will be accessible beginning with A3 Ultra VMs.

Based on Axion, Google’s proprietary Arm processors, C4A virtual machines (VMs) are now widely accessible

AI workload-focused additions to Titanium, Google Cloud’s host offload capability, and Jupiter, its data center network.

Google Cloud’s AI/ML-focused block storage service, Hyperdisk ML, is widely accessible.

Trillium A new era of TPU performance

Trillium A new era of TPU performance is being ushered in by TPUs, which power Google’s most sophisticated models like Gemini, well-known Google services like Maps, Photos, and Search, as well as scientific innovations like AlphaFold 2, which was just awarded a Nobel Prize! We are happy to inform that Google Cloud users can now preview Trillium, our sixth-generation TPU.

Taking advantage of NVIDIA Accelerated Computing to broaden perspectives

By fusing the best of Google Cloud’s data center, infrastructure, and software skills with the NVIDIA AI platform which is exemplified by A3 and A3 Mega VMs powered by NVIDIA H100 Tensor Core GPUs it also keeps investing in its partnership and capabilities with NVIDIA.

Google Cloud announced that the new A3 Ultra VMs featuring NVIDIA H200 Tensor Core GPUs will be available on Google Cloud starting next month.

Compared to earlier versions, A3 Ultra VMs offer a notable performance improvement. Their foundation is NVIDIA ConnectX-7 network interface cards (NICs) and servers equipped with new Titanium ML network adapter, which is tailored to provide a safe, high-performance cloud experience for AI workloads. A3 Ultra VMs provide non-blocking 3.2 Tbps of GPU-to-GPU traffic using RDMA over Converged Ethernet (RoCE) when paired with our datacenter-wide 4-way rail-aligned network.

In contrast to A3 Mega, A3 Ultra provides:

With the support of Google’s Jupiter data center network and Google Cloud’s Titanium ML network adapter, double the GPU-to-GPU networking bandwidth

With almost twice the memory capacity and 1.4 times the memory bandwidth, LLM inferencing performance can increase by up to 2 times.

Capacity to expand to tens of thousands of GPUs in a dense cluster with performance optimization for heavy workloads in HPC and AI.

Google Kubernetes Engine (GKE), which offers an open, portable, extensible, and highly scalable platform for large-scale training and AI workloads, will also offer A3 Ultra VMs.

Hypercompute Cluster: Simplify and expand clusters of AI accelerators

It’s not just about individual accelerators or virtual machines, though; when dealing with AI and HPC workloads, you have to deploy, maintain, and optimize a huge number of AI accelerators along with the networking and storage that go along with them. This may be difficult and time-consuming. For this reason, Google Cloud is introducing Hypercompute Cluster, which simplifies the provisioning of workloads and infrastructure as well as the continuous operations of AI supercomputers with tens of thousands of accelerators.

Fundamentally, Hypercompute Cluster integrates the most advanced AI infrastructure technologies from Google Cloud, enabling you to install and operate several accelerators as a single, seamless unit. You can run your most demanding AI and HPC workloads with confidence thanks to Hypercompute Cluster’s exceptional performance and resilience, which includes features like targeted workload placement, dense resource co-location with ultra-low latency networking, and sophisticated maintenance controls to reduce workload disruptions.

For dependable and repeatable deployments, you can use pre-configured and validated templates to build up a Hypercompute Cluster with just one API call. This include containerized software with orchestration (e.g., GKE, Slurm), framework and reference implementations (e.g., JAX, PyTorch, MaxText), and well-known open models like Gemma2 and Llama3. As part of the AI Hypercomputer architecture, each pre-configured template is available and has been verified for effectiveness and performance, allowing you to concentrate on business innovation.

A3 Ultra VMs will be the first Hypercompute Cluster to be made available next month.

An early look at the NVIDIA GB200 NVL72

Google Cloud is also awaiting the developments made possible by NVIDIA GB200 NVL72 GPUs, and we’ll be providing more information about this fascinating improvement soon. Here is a preview of the racks Google constructing in the meantime to deliver the NVIDIA Blackwell platform’s performance advantages to Google Cloud’s cutting-edge, environmentally friendly data centers in the early months of next year.

Redefining CPU efficiency and performance with Google Axion Processors

CPUs are a cost-effective solution for a variety of general-purpose workloads, and they are frequently utilized in combination with AI workloads to produce complicated applications, even if TPUs and GPUs are superior at specialized jobs. Google Axion Processors, its first specially made Arm-based CPUs for the data center, at Google Cloud Next ’24. Customers using Google Cloud may now benefit from C4A virtual machines, the first Axion-based VM series, which offer up to 10% better price-performance compared to the newest Arm-based instances offered by other top cloud providers.

Additionally, compared to comparable current-generation x86-based instances, C4A offers up to 60% more energy efficiency and up to 65% better price performance for general-purpose workloads such as media processing, AI inferencing applications, web and app servers, containerized microservices, open-source databases, in-memory caches, and data analytics engines.

Titanium and Jupiter Network: Making AI possible at the speed of light

Titanium, the offload technology system that supports Google’s infrastructure, has been improved to accommodate workloads related to artificial intelligence. Titanium provides greater compute and memory resources for your applications by lowering the host’s processing overhead through a combination of on-host and off-host offloads. Furthermore, although Titanium’s fundamental features can be applied to AI infrastructure, the accelerator-to-accelerator performance needs of AI workloads are distinct.

Google has released a new Titanium ML network adapter to address these demands, which incorporates and expands upon NVIDIA ConnectX-7 NICs to provide further support for virtualization, traffic encryption, and VPCs. The system offers best-in-class security and infrastructure management along with non-blocking 3.2 Tbps of GPU-to-GPU traffic across RoCE when combined with its data center’s 4-way rail-aligned network.

Google’s Jupiter optical circuit switching network fabric and its updated data center network significantly expand Titanium’s capabilities. With native 400 Gb/s link rates and a total bisection bandwidth of 13.1 Pb/s (a practical bandwidth metric that reflects how one half of the network can connect to the other), Jupiter could handle a video conversation for every person on Earth at the same time. In order to meet the increasing demands of AI computation, this enormous scale is essential.

Hyperdisk ML is widely accessible

For computing resources to continue to be effectively utilized, system-level performance maximized, and economical, high-performance storage is essential. Google launched its AI-powered block storage solution, Hyperdisk ML, in April 2024. Now widely accessible, it adds dedicated storage for AI and HPC workloads to the networking and computing advancements.

Hyperdisk ML efficiently speeds up data load times. It drives up to 11.9x faster model load time for inference workloads and up to 4.3x quicker training time for training workloads.

With 1.2 TB/s of aggregate throughput per volume, you may attach 2500 instances to the same volume. This is more than 100 times more than what big block storage competitors are giving.

Reduced accelerator idle time and increased cost efficiency are the results of shorter data load times.

Multi-zone volumes are now automatically created for your data by GKE. In addition to quicker model loading with Hyperdisk ML, this enables you to run across zones for more computing flexibility (such as lowering Spot preemption).

Developing AI’s future

Google Cloud enables companies and researchers to push the limits of AI innovation with these developments in AI infrastructure. It anticipates that this strong foundation will give rise to revolutionary new AI applications.

Read more on Govindhtech.com

[xublimetech]

Best Open-Source AI Frameworks for Developers in 2025

Artificial Intelligence (AI) is transforming industries, and open-source frameworks are at the heart of this revolution. For developers, choosing the right AI tools can make the difference between a successful project and a stalled experiment. In 2025, several powerful open-source frameworks stand out, each with unique strengths for different AI applications—from deep learning and natural language processing (NLP) to scalable deployment and edge AI.

Here’s a curated list of the best open-source AI frameworks developers should know in 2025, along with their key features, use cases, and why they matter.

1. TensorFlow – The Industry Standard for Scalable AI

Developed by Google, TensorFlow remains one of the most widely used AI frameworks. It excels in building and deploying production-grade machine learning models, particularly for deep learning and neural networks.

Why TensorFlow?

Flexible Deployment: Runs on CPUs, GPUs, and TPUs, with support for mobile (TensorFlow Lite) and web (TensorFlow.js).

Production-Ready: Used by major companies for large-scale AI applications.

Strong Ecosystem: Extensive libraries (Keras, TFX) and a large developer community.

Best for: Enterprises, researchers, and developers needing scalable, end-to-end AI solutions.

2. PyTorch – The Researcher’s Favorite

Meta’s PyTorch has gained massive popularity for its user-friendly design and dynamic computation graph, making it ideal for rapid prototyping and academic research.

Why PyTorch?

Pythonic & Intuitive: Easier debugging and experimentation compared to static graph frameworks.

Dominates Research: Preferred for cutting-edge AI papers and NLP models.

TorchScript for Deployment: Converts models for optimized production use.

Best for: AI researchers, startups, and developers focused on fast experimentation and NLP.

3. Hugging Face Transformers – The NLP Powerhouse

Hugging Face has revolutionized natural language processing (NLP) by offering pre-trained models like GPT, BERT, and T5 that can be fine-tuned with minimal code.

Why Hugging Face?

Huge Model Library: Thousands of ready-to-use NLP models.

Easy Integration: Works seamlessly with PyTorch and TensorFlow.

Community-Driven: Open-source contributions accelerate AI advancements.

Best for: Developers building chatbots, translation tools, and text-generation apps.

4. JAX – The Next-Gen AI Research Tool

Developed by Google Research, JAX is gaining traction for high-performance numerical computing and machine learning research.

Why JAX?

Blazing Fast: Optimized for GPU/TPU acceleration.

Auto-Differentiation: Simplifies gradient-based ML algorithms.

Composable Functions: Enables advanced research in AI and scientific computing.

Best for: Researchers and developers working on cutting-edge AI algorithms and scientific ML.

5. Apache MXNet – Scalable AI for the Cloud

Backed by Amazon Web Services (AWS), MXNet is designed for efficient, distributed AI training and deployment.

Why MXNet?

Multi-Language Support: Python, R, Scala, and more.

Optimized for AWS: Deep integration with Amazon SageMaker.

Lightweight & Fast: Ideal for cloud and edge AI.

Best for: Companies using AWS for scalable AI model deployment.

6. ONNX – The Universal AI Model Format

The Open Neural Network Exchange (ONNX) allows AI models to be converted between frameworks (e.g., PyTorch to TensorFlow), ensuring flexibility.

Why ONNX?

Framework Interoperability: Avoid vendor lock-in by switching between tools.

Edge AI Optimization: Runs efficiently on mobile and IoT devices.

Best for: Developers who need cross-platform AI compatibility.

Which AI Framework Should You Choose?

The best framework depends on your project’s needs:

For production-scale AI → TensorFlow

For research & fast prototyping → PyTorch

For NLP → Hugging Face Transformers

For high-performance computing → JAX

For AWS cloud AI → Apache MXNet

For cross-framework compatibility → ONNX

Open-source AI tools are making advanced machine learning accessible to everyone. Whether you're a startup, enterprise, or researcher, leveraging the right framework can accelerate innovation.

[jamesmilleer2407]

How Top NASDAQ Stocks Reflect Industry Transformation

The Nasdaq Composite Index has shown consistent strength in 2025, supported by innovation in cloud computing, artificial intelligence, and semiconductors. As technology firms continue to lead digital transformation, several companies listed on the NASDAQ have emerged as standout names. These Top NASDAQ Stocks have gained attention for their resilience, adaptability, and strategic growth, shaping the broader index's momentum.

Tech-Centric Momentum in the Nasdaq Composite

The Nasdaq Composite remains heavily weighted toward technology companies. The recent market environment has reflected continued gains in enterprise IT services, automation, and AI infrastructure. Digital adoption across industries has helped several tech-focused firms deliver consistent performance. The index’s movement signals confidence in software platforms, chipmakers, and digital service providers that power today’s business infrastructure.

Advancements in AI integration and machine learning applications have played a critical role in shaping product strategies. Companies with exposure to scalable platforms and automation tools continue to benefit from structural trends, supporting their strong presence among Top NASDAQ Stocks.

Microsoft and Alphabet Maintain Digital Leadership

Microsoft has demonstrated consistent growth across its cloud and productivity segments. Its investments in AI-driven products and enterprise software services have enabled stronger digital transitions for global businesses. Meanwhile, Alphabet continues to strengthen its position through its cloud services and advertising platforms, backed by ongoing development in machine learning and search technology.

Both companies reflect how demand for scalable cloud ecosystems and AI-enabled software continues to impact market performance. Their broad service portfolios, combined with innovation in automation and data analytics, reinforce their role among the leading contributors to the Nasdaq Composite.

Nvidia and AMD Boost Chip Innovation

Semiconductors remain at the heart of the digital economy, and companies like Nvidia and AMD have played significant roles in advancing this segment. Nvidia’s GPU technology supports AI computing, data centers, and high-performance processing, driving broad applications across multiple sectors. AMD, on the other hand, has executed well on its product roadmap, strengthening its position in personal computing and enterprise server markets.

These companies have benefited from the ongoing demand for faster, more efficient processors. Their product innovation and relevance across gaming, cloud services, and edge computing make them key components among Top NASDAQ Stocks.

Amazon and Meta Drive Digital Growth

Amazon continues to scale its influence in both retail and cloud computing. The company’s focus on e-commerce optimization, logistics automation, and AWS growth supports its diverse revenue structure. At the same time, Meta’s platforms have maintained engagement and advertising growth, driven by AI integration and infrastructure scaling.

Both companies highlight how digital ecosystems and platform-based business models continue to shape consumer and enterprise behavior. Their strategic focus on personalization, content delivery, and connectivity has reinforced their market positions in 2025.

Apple and Tesla Drive Consumer Innovation

Apple maintains a strong presence in the NASDAQ through its ecosystem of consumer devices and services. The company’s ability to blend hardware innovation with recurring revenue from digital services has helped deliver stable growth. Wearables, smartphones, and content platforms all contribute to Apple’s long-term value proposition.

Tesla represents a convergence between automotive innovation and smart energy solutions. Its advancements in electric vehicles and autonomous technology reflect changing preferences in mobility and clean energy. Tesla’s inclusion among Top NASDAQ Stocks signals how diversified tech applications extend beyond traditional software and hardware sectors.

Trends Behind Top NASDAQ Stocks

The sustained momentum in Top NASDAQ Stocks is backed by broader themes across technology and enterprise spending. Ongoing adoption of AI solutions, increased reliance on cloud infrastructure, and digital-first consumer engagement have created a favorable environment for these companies.

Macroeconomic conditions, earnings performance, and global demand for smart devices continue to shape investor sentiment. Additionally, trends in data privacy, supply chain efficiency, and scalable platforms have made resilience a critical factor for long-term success among Nasdaq-listed companies.

The Nasdaq Composite Index remains a key barometer for digital innovation and technology-driven performance in 2025. Leading companies across semiconductors, cloud computing, digital platforms, and consumer tech have demonstrated their ability to navigate change and maintain growth. These Top NASDAQ Stocks not only reflect current trends but also shape the broader dynamics of modern economic activity.

As industries evolve and adopt smarter technologies, the firms driving the Nasdaq Composite are well-positioned to remain central to ongoing transformation. Their performance continues to offer insight into the direction of innovation-led growth and the importance of adaptability in the tech sector.

[the-lady-bryan]

Okay but like, I literally just bought a refurbed business computer that's been upgraded to today's standards (it originally came out in 2012/2013).

Works soooooooo much better than any new laptop I've ever had, and still has room for further upgrades. Like, I only bought the lower-end refurb that the seller had available. DVD rw drive. Integrated graphics. Intel i5 core processor (I forget the specifics on that) and 8gb ram with 500gb HDD. But I can further upgrade it by converting to SSD using an adapter and have a few tb of internal storage and it handles up to 32 GB ram.

And that's before I get around to things like sound cards and GPUs. That's just the refurb stock model I've got.

The laptop I have from 2017 can handle only up to 16gb ram. And has 500gb SSD but the motherboard can't really handle more than that. Basic DVD drive, but it is cd rw just not DVD rw. My husband's 2020 laptop? 8gb ram but it's soldered in and can't be upgraded with 500gb SSD that also can't be upgraded. No DVD/CD drive.

Took a look at Walmart a few days ago, most affordable laptops are basically chromebooks with windows 11 on em. 250gb internal storage but with a Microsoft subscription you can have a TB of cloud storage! Also, 4gb ram, with the midrange models having gasp! 8gb ram and still only 250ssd internal storage! By the way the "affordable" price range at my local Walmart starts at $350ish. Not a single model available from any brand has a DVD or CD drive. For that you've got to drop nearly $1000 for the desktop tower alone. And that's for base models with 8-16gb and 500gb-1tb internal storage. Monitor, keyboard, mouse, gpu not included. But hey you get rgb decorative lighting I guess.

And this is just at my local Walmart in a rural-ish town where the only other place to buy a computer is Staples. And even then, they don't have nearly as much as Walmart.

The only reason I bought my refurb desktop from a discount seller was because my laptops are too old and costly to repair at the moment and I needed SOMETHING to be able to start streaming again and playing Minecraft with my husband. And I only paid $98 for it with tax. Free shipping. It came with a free keyboard and mouse and surprise internal speakers that didn't originally come on that particular model. It was cheap, better than any of the laptops I have, still fully upgradeable, and while it doesn't have fancy HDMI connector due simply to the fact the motherboard just doesn't come with one, I can remedy that with an adapter for under $20 if I choose.

So yeah, with the damn near unusability of newer model laptops in the market and the near unaffordability of even the bottom end models yeah, I can completely see a return to desktops. Especially with the ease of availability of upgraded refurbs on the market nowadays as many businesses are forced to upgrade to less capable machines with newer software that requires always online cloud computing in order to still function as a business. So their older perfectly serviceable machines are being sold off and or salvaged and resold to regular folks.

i give it scant years before desktop computers come back fashion a la record players and people talk about bringing back computer rooms, not taking your computer around the house, "intentional" computing, "it's really important to take the time out and just sit with your computer and really absorb the action of computing" it will be no different from the romance and nostalgia surrounding the notion of throwing on a vinyl

[bliiot-jerry]

ARM-Based Industrial PCs + Azure IoT Edge for Smart Industrial Automation

Case Details

I. Why ARM Industrial PCs + Azure IoT Edge?

1. Cost-Effective, High-Reliability Edge Intelligence

Energy Efficiency Revolution: The TDP (thermal design power) of ARM processors (such as the Cortex-A series) is usually less than 15W, which can significantly reduce cooling costs in harsh industrial environments, support 24/7 continuous operation, and is suitable for deployment in scenarios with limited power resources (such as remote oil fields and distributed production lines).

Real-Time Control: ARM industrial computers equipped with real-time operating systems (such as RT-Linux or FreeRTOS) can provide microsecond response accuracy to meet high real-time requirements such as PLC control and motion control. Hardware-level watchdog and redundant power supply design further ensure system stability.

Industrial Durability: Wide-temperature operation (-40°C to 85°C), anti-vibration, and dustproof design ensure 24/7 uninterrupted operation.

2. Cloud-Powered Edge Computing

Edge AI Deployment: Run AI models directly on industrial PCs (e.g., defect detection, equipment lifespan prediction), achieving 10x faster response and 90% lower bandwidth costs.

Offline Autonomy: Local rule engines execute critical operations (e.g., emergency shutdowns, quality sorting) during network outages, preventing production line downtime.

Seamless Cloud Integration: Manage millions of devices via Azure IoT Hub, enable bidirectional data synchronization, and support remote diagnostics and OTA updates.

II. Industrial Use Cases: From Automation to Intelligence

Case 1: Predictive Maintenance for Smart Production Lines

Pain Point: Traditional PLCs cannot analyze equipment vibration or temperature trends, leading to unplanned downtime costing thousands per minute.

Solution:

ARM industrial PCs collect sensor data (vibration, current, temperature) in real time, running edge-based FFT spectrum analysis and LSTM models to predict bearing wear risks 7 days in advance.

Azure IoT Edge syncs alerts with cloud digital twins, auto-generating maintenance orders to reduce unplanned downtime by 30%.

Case 2: Autonomous Visual Inspection

Pain Point: Manual inspections are inefficient (<200 units/hour) with over 5% defect leakage.

Solution:

ARM industrial PCs with industrial cameras deploy lightweight YOLOv5 models for millisecond-level detection of surface scratches or assembly defects.

Results are uploaded to Azure AI for continuous model optimization (99.9% accuracy), cutting labor costs by 70%.

Case 3: Energy Management Optimization

Pain Point: Dispersed energy data hinders real-time optimization.

Solution:

ARM industrial PCs aggregate data from meters, HVAC, and compressors, computing real-time KPIs (e.g., energy consumption per unit output).

Azure Stream Analytics dynamically adjusts equipment operation modes, reducing annual energy consumption by 15–20%.

III. Tangible Business Value

Cost Savings: 40% lower hardware costs, 60% reduced energy consumption.

Efficiency Gains: Fault response time shortened from hours to minutes, 25% improvement in OEE (Overall Equipment Effectiveness).

Data-Driven Insights: Capture full lifecycle equipment data to optimize processes and supply chain decisions.

IV. Global Success Stories

Automotive: A German automaker deployed 200+ ARM industrial PCs for real-time health monitoring of welding robots, cutting annual maintenance costs by $1.2M.

Food Packaging: A Southeast Asian dairy producer reduced product defects from 0.8% to 0.05% using edge visual inspection, avoiding $5M+ in annual recall losses.

Smart Water Management: A North American municipal water system achieved 98% accuracy in pipeline leak detection, saving 4M tons of water yearly.

V. Future Trends: The Edge Intelligence Frontier

5G + TSN Integration: ARM industrial PCs with 5G modules enable microsecond-level network synchronization for flexible manufacturing.

AI Accelerators: NPU/GPU-powered edge devices unlock large-model inference (e.g., generative AI for process optimization).

Sustainable Manufacturing: Edge-based carbon footprint tracking and optimization help meet ESG goals.

Conclusion: The Gold-Standard Combo for Industrial Intelligence

ARM industrial PCs and Azure IoT Edge redefine industrial operations—lower costs, faster decisions, unmatched resilience. Whether in discrete manufacturing or process industries, this synergy builds a closed loop of edge sensing, cloud optimization, and global intelligence, positioning enterprises at the forefront of smart manufacturing.

Act Now: Start with single-node deployments and scale to plant-wide intelligence—transform every machine into a data-driven decision-maker!

[arbitcoin]

يطلق FWB ، نادي سرير Cryptocurrency Community ، أصدقاء مع مشروع Builders الجديد ، مع مكافآت XX؟

يشبه العثور على بناة إيجاد زميل في الفراش ، ويستغرق الأمر الكثير من الوقت والطاقة. فريق FOMO المعروف FWB (أصدقاء مع الفوائد أطلقت مؤسسة شريك السرير اللامركزي) مشروعًا جديدًا: FWB (أصدقاء مع بناة) ؛ تدعي أنها تساعد فريق التطوير والموظفين الإبداعيين على تطوير منتجات ويب 3 جديدة. تشمل وحدات التعاون AWS و Alchemy و Thirdweb و QuickNode و Akave و Pilecoin و Base و World وغيرها من المؤسسات المعروفة. تؤكد FWB أن هذا ليس Hackathon آخر ، ولن تكون هناك مكافآت ، والمكافأة هي السماح للباني بإنتاج منتجات. ما هو الأصدقاء مع الفوائد؟ إن قراء أخبار السلسلة هم جميعهم شباب متعلمين من التعليم العالي. ربما لم يكونوا قد سمعوا عن العامية الأمريكية المبتذلة للأصدقاء مع الفوائد ، والتي تُرجم حرفيًا كأصدقاء يتمتعون بمصالح باللغة الإنجليزية ، والمعنى الفعلي هو "رفيق الفراش". ظهر الأصدقاء مع الفوائد في عام 2020 عندما تم استنشاق Ethereum إلى السماء ، هزت دائرة العملة المشفرة باسم مباشر بمجرد إطلاقها. ما هو أكثر من ذلك ، يشمل أعضائها عرابة الروح إريكا بادو. المؤسس المشارك لـ FWB هو Trevor McFedries ، DJ المعروف ومنتج الموسيقى. FWB هو أول DAO يعتمد على سلسلة Ethereum مع Social كغرض رئيسي. في ذلك الوقت ، اشترى أكثر من 6000 شخص. أصدقاء مع الفوائد انضم رمز $ fwb إلى DAO ، وكانت هناك منظمات فرعية في جميع أنحاء العالم. انضم الشباب من نيويورك إلى لوس أنجلوس إلى FWB DAO للمشاركة في مهرجان موسيقى الكرنفال الحصري للأعضاء. يُطلق على FWB اسم "Soho House غير المركزي" من قبل صحيفة نيويورك تايمز ، وهي صالة كبار الشخصيات التي ذهب إليها أحد كبار أعضاء الشباب. كان يطلق على FWB ذات مرة أغنى داو في دائرة العملة المشفرة. قام FWB ، مثله مثل العديد من DAOS الذين كانوا يرتفعون في ذلك الوقت ، بسحب الناس إلى مجتمع Discord لمدة 24 ساعة للدردشة ، حتى وصل Mood Fomo إلى أعلى نقطة مثيرة ، واشترى أكوامًا من العملات المعدنية غير المعروفة وسقطت في الفخ. لا تزال FWB نشطة وقد أنتجت مشاريع جديدة ، ليس للشباب الذين يحبون الحفلات ، ولكن للمهندسين وفرق التطوير. تحولت FWB؟ تريد أن تصبح مؤسسة تعليمية جادة W3؟ إن التحول من الأصدقاء مع فوائد إلى الأصدقاء مع بناة يعني أن FWB سيكون مؤسسة تعليمية على شبكة الإنترنت 3 جادة؟ أصدقاء مع بناة فكرة هناك وثائق تطبيق عامة متاحة للرجوع إليها الفرق المهتمة بالمشاركة. يتضمن اللحم البقري الناتج عن شبكة شركاء FWB نقاط المكافآت بقيمة 5000 دولار على نظام AWS Cloud ، ونقاط مكافأة GPU لدورات تدريب AI التي توفرها Compute Labs ، ونقاط مكافأة AI Agen Agent التي توفرها Loomlay ، وبالقرب من دورات التعلم والتكنولوجيا التي تركز على AI (لا نقاط ونقاط!). توفر القاعدة 15000 دولار في نقاط مكافأة الغاز وموارد التطوير ، وما إلى ذلك. يبدو أن المكافأة تعطي الكثير من النقاط! هل سيركز الأصدقاء مع البناة على تطوير منتجات الذكاء الاصطناعى على أساس blockchain (Ethereum)؟ تتم مكافأة المكافآت لمنتجات وكيل الذكاء الاصطناعى المستقبلية المطورة بدلاً من مجرد توفير المكافآت. يعمل FWB مع WorldCoin تمامًا مثل الأصدقاء مع Benfits يستضيف العديد من الأحداث الموسيقية والحفلات لتغني الجو ، فإن الأصدقاء مع بناة لديهم أيضًا أحداث للمطورين. يمكن للمهندسين وفرق التطوير الذين يتقدمون بنجاح على البرنامج حضور الأطراف الحصرية ، والمنتجات المتقدمة تتاح لها الفرصة للتغلب على المستثمرين. تعاونت FWB و WorldCoin مؤخرًا لإجراء عمليات تجريبية ، وتلقى ما مجموعه 140 طلبًا ، وأنشأ فريق التطوير المشارك 40 تطبيقًا صغيرًا. حصل فريقان من المشروع على رسائل نوايا استثمارية قدمها المستثمرون. تحذير المخاطراستثمارات العملة المشفرة محفوفة بالمخاطر للغاية ، وقد تتقلب أسعارها بشكل كبير وقد تفقد كل مديرك. يرجى تقييم المخاطر بحذر.