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Androidauthority
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Google isn’t done with its Wallet/Pay identity crisis (APK teardown)
- Google is exploring adding back the Google Pay branding in certain areas within the Google Wallet app.
- Changes are not live, but we managed to activate them for a quick look.
- Google is also exploring a new divider before the Everything Else option when adding new items to Google Wallet, giving the menu some visual distinction.
- This suggests that Google Wallet is still facing an identity crisis, as the Google Pay branding might make a comeback.
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Cybersecurity-Insiders
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The Dark Side of Google Searches: How Simple keywords can Lead to Cyber Threats
- Google searches can expose users to cyber threats through various techniques like fake customer service pages and SEO poisoning.
- Cybercriminals create fake websites to resemble customer service pages of well-known brands and use SEO techniques to push their fraudulent pages to the top of Google’s search results.
- Individuals searching for emergency loans can be directed to what appear to be legitimate loan websites that are only fronts for data harvesting operations and personal information collection.
- Fraudulent streaming websites entice users by advertising the latest movies or music albums for free but often contain malware downloads that can compromise device’s security, steal personal information, or hijack your system.
- Searches for sensitive health topics like sexual wellness products and medications can lead users to fake ads, fraudulent online pharmacies, or malicious websites that steal personal data or infect devices with malware.
- Always be cautious with financial offers that seem too good to be true and avoid unfamiliar or suspicious websites that offer heavy discounts.
- Be vigilant while browsing and verify the authenticity of websites by searching for secure connections (HTTPS) and legitimate customer reviews.
- Use antivirus software and firewalls to protect your device from malware and cyber threats in the world of cybersecurity.
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BGR
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Google Gemini AI will protect your Pixel from spam calls and rogue apps
- Google's Gemini AI will protect Pixel phones from spam calls and rogue apps.
- Gemini uses AI to detect patterns in calls and app behavior that users may not catch.
- The new features include Scam Detection for voice calls and real-time alerts in Google Play Protect for apps.
- Initially available on Pixel 6 series phones, the features will eventually be extended to other Android handsets.
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TechBullion
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Google’s November Core Update Shakes Rankings
- Google's November Core Update, rolled out on November 11th, 2024, has caused ranking fluctuations.
- The update aims to improve search result quality and prioritize content useful to users.
- Websites should shift toward people-first content strategies and focus on high-quality, user-centric content.
- The impact of the update may take weeks to observe, but implementing these strategies can lead to long-term success.
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Droid-Life
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Google Play Protect Gets Live Threat Detection of Dangerous Apps
- Google Play Protect introduces live threat detection for dangerous apps.
- The real-time scanning of apps can identify malicious behavior and notify users.
- Initially, the focus is on stalkerware that collects personal data without user consent.
- The feature is available on Pixel 6 and newer devices through Google Play Protect.
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Androidauthority
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Tired of scam calls? Google has another great reason for you to switch to a Pixel
- Google is rolling out a new AI-powered Scam Detection feature to its Pixel devices.
- The feature uses on-device AI models to recognize conversation patterns commonly associated with scams and alert users of potential scam calls.
- Scam Detection is rolling out with the latest beta release of the Phone app in the US on Pixel 6 and newer models.
- The feature delivers audio and haptic alerts, as well as a visual warning, to prevent users from falling for scam calls.
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Androidauthority
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Your Pixel can now detect malicious apps that collect data without your consent
- Google Play Protect's Live Threat Detection feature is now available on Pixel 6 and newer models.
- The feature uses on-device AI models to identify harmful apps and provide real-time alerts.
- Live Threat Detection analyzes app behavior to detect suspicious activity.
- Users will receive notifications and can remove unsafe apps with the help of Play Protect.
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Hackernoon
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Optimizing User Acquisition with Google DV360: Creative Strategies for Success
- User acquisition (UA) advertising has seen significant changes in recent years, and creative is now king across channels like Facebook, TikTok, and others.
- A top-performing creative can outperform even low-performing creatives by five times or more.
- You can think of creatives with Google DV360 in different mindsets and environments, depending on how users engage with each in a given channel.
- With DV360, you have more formats to choose from, including CTV, out-of-home, in-game interstitials, small banners, etc.
- DV360 is a comprehensive advertising platform developed by Google that helps advertisers and marketers plan, manage and optimize their display, video, and programmatic campaigns across channels.
- To achieve campaign success, set optimal frequency levels around 2 - 2.5 times per week, but this may vary depending on channel and user context.
- For banners, you should consider the most popular formats and run a test flight to score them by potential reach.
- For video, you should consider the context and select the format that suits each channel. Grouping game publishers in separate Line Items can help you provide gameplay videos or playable ads on Native Video.
- For Out-of-Home, consider user context and their surroundings such as district, buildings, events, time of the year, etc., and increase frequency as appropriate.
- If you are in a mature company with a solid product-market fit and looking for your next big win, Google DV360 could be an option worth exploring.
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Cloudblog
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Empower your teams with self-service Kubernetes using GKE fleets and Argo CD
- Combining Google Kubernetes Engine (GKE) fleets and Argo CD creates a powerful and secure way to manage applications across multiple Kubernetes clusters, particularly when they span different environments or cloud providers.
- Connect Gateway and Workload Identity can streamline authentication and enhance security, permitting Argo CD to manage clusters securely without needing to manage Kubernetes Services Accounts.
- Using GKE Enterprise Teams can enable granular access control, ensuring each team has proper permissions and namespaces within this secure framework.
- The fleet-argocd-plugin is a custom Argo CD generator that automatically imports your GKE Fleet cluster list into Argo CD and maintains synchronized cluster information, making it easier for platform admins to manage resources and for application teams to focus on deployments.
- The setup process comprises creating a GKE fleet with control and application clusters, deploying Argo CD on the control cluster configured with Connect Gateway and Workload Identity, and setting up GKE Enterprise Teams for granular access control.
- Finally, the fleet-argocd-plugin is installed to simplify cluster management by automatically importing the GKE Fleet cluster list into Argo CD and maintaining synchronized cluster information.
- By leveraging the power of GKE Fleets, Argo CD, Connect Gateway, Workload Identity, and GKE Enterprise Teams's advanced features, a user can streamline Kubernetes operations, improve security, and empower teams to efficiently manage and deploy applications across your fleet.
- Furthermore, it is possible to enhance the setup by exploring advanced deployment strategies with Argo CD, achieving zero-downtime releases and minimizing risk during updates.
- GKE fleets and Argo CD lay a robust, secure, and automated multi-cluster platform to build on using automation, GitOps principles, and team-based management to unleash the full potential of Kubernetes infrastructure.
- To sum up, GKE fleets and Argo CD provide a solid foundation for building a secure, efficient, and scalable platform while enabling automation, GitOps principles, and team-based management.
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Googleblog
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A Smoother Ride: Android Emulator Stability and Performance Updates
- The Android Studio team has focused on improving the stability, reliability, and performance of the Android Emulator through Project Quartz.
- Project Quartz aimed to reduce Emulator crashes and improve overall stability and reliability.
- Several measures were taken, including updating the UI framework, hypervisor framework, and graphics libraries, as well as adding more end-to-end tests.
- The latest stable version of Android Studio has seen a 30% reduction in crashes, resulting in a more stable and reliable experience for developers.
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Cloudblog
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Data loading best practices for AI/ML inference on GKE
- As AI models increase in sophistication, there’s increasingly large model data needed to serve them.
- Loading the models and weights along with necessary frameworks to serve them for inference can add seconds or even minutes of scaling delay, impacting both costs and the end-user’s experience.
- Inference servers such as Triton, Text Generation Inference (TGI), or vLLM are packaged as containers that are often over 10GB in size.
- This can make them slow to download, and extend pod startup times in Kubernetes.
- This blog explores techniques to accelerate data loading for both inference serving containers and downloading models + weights.
- Accelerating container load times using secondary boot disks to cache container images with your inference engine and applicable libraries directly on the GKE node.
- Accelerating model + weight load times from Google Cloud Storage with Cloud Storage Fuse or Hyperdisk ML.
- GKE lets you pre-cache your container image into a secondary boot disk that is attached to your node at creation time.
- Using Cloud Storage as the source of truth, there are two main products to retrieve your data at the GKE-pod level: Cloud Storage Fuse and Hyperdisk ML (HdML).
- Loading large AI models, weights, and container images into GKE-based AI models can delay workload startup times.
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Droid-Life
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Pixel 5a Gets November Patch, Reaches End of Update Road
- Google is no longer supporting the Pixel 5a with major OS upgrades or security updates.
- Despite this, the Pixel 5a is receiving the November patch.
- The software version for this build of Android 14 is slightly different from the previous update.
- There is no official changelog provided for this update.
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Droid-Life
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Google’s Black Friday Pixel 9 Pro Deal is $150 Off
- Google will discount the Pixel 9 Pro and Pixel 9 Pro XL by $150, dropping the starting price to $849.
- All versions and colors of the Pixel 9 Pro and Pixel 9 Pro XL are expected to be $150 off.
- The discounted pricing for the Pixel 9 Pro ranges from $849 to $1,299, depending on the storage capacity.
- Google's Black Friday sale will start on November 21, with other retailers likely to match the discounted pricing.
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Cloudblog
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65,000 nodes and counting: Google Kubernetes Engine is ready for trillion-parameter AI models
- Google Kubernetes Engine now supports up to 65,000-node clusters, providing an unmatched scale for training or inference with much-needed capacity for the world’s most resource-hungry AI workloads.
- This advancement enables customers to reduce model training time or scale models to multi-trillion parameters or more.
- GKE’s capacity offers more than 10X larger scale than the other two largest public cloud providers, according to Google.
- GKE is transitioning from the open-source etcd, distributed key-value store, to a new, more robust, key-value store based on Spanner, Google’s distributed database that delivers virtually unlimited scale.
- On top of supporting larger GKE clusters, this change will provide improved latency of cluster operations and a stateless cluster control plane.
- GKE now scales significantly faster with a control plane that automatically adjusts to high-volume operations, while maintaining predictable operational latencies.
- Guided by Google's long-standing and robust open-source culture, GKE provides substantial contributions to the open-source community, including when it comes to scaling Kubernetes.
- Recent innovations in this space include Fully managed DCGM metrics for improved accelerator monitoring and Custom compute classes, which offer greater control over compute resource allocation and scaling.
- GKE’s support for larger clusters will help companies like Anthropic accelerate AI innovation and work more efficiently across diverse workloads.
- At Google Cloud, the company is dedicated to providing the best platform for running containerized workloads, consistently pushing the boundaries of innovation.
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Cloudblog
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Unlocking LLM training efficiency with Trillium — a performance analysis
- Google's sixth-generation Tensor Processing Unit (TPU), Trillium, can deliver up to 1.8x better performance-per-dollar compared to prior-generation Cloud TPU v5p.
- MLPerf 4.1 training benchmarks showed that the Trillium delivers a 99% scaling efficiency (throughput).
- The metrics used for hardware accelerator comparison include peak throughput, effective throughput, throughput scaling efficiency, utilization performance, scaling efficiency and convergence scaling efficiency.
- Convergence scaling efficiency, which focuses on the fundamental goal of training, is measured by the ratio of the speedup in convergence time to the increase in cluster size.
- The convergence scaling efficiency of Trillium is close to that of Cloud TPU v5p which is commendable, but Trillium delivers the convergence at a lower cost.
- Trillium achieves 99% scaling efficiency even when operating across data-center networks using Cloud TPU multislice technology, outperforming the 94% scaling efficiency of Cloud TPU v5p cluster within a single ICI domain.
- Trillium provides better performance per dollar, improvement in convergence scaling efficiency and scaling properties, which make it the most cost-efficient TPU training system to date.
- Applying multiple dimensions of performance and efficiency, like effective model FLOPS utilization (EMFU), memory bandwidth utilization (MBU), regions using ICI domains and scaling characteristics, for ML-accelerator evaluation is suggested to make data-driven decisions based on workload requirements.
- Trillium has been launched to address the demands of next-generation models by providing performance at scale, from the chip to the system to Google data center deployments.
- Throughput scaling efficiency and metrics like EMFU and MBU provide more meaningful insights into an accelerator's abilities beyond simple metrics like peak performance.
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