# Featured Published articles for Featured. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Group Replication Beyond a Single Cluster: DC-DR with Percona (PS MySQL) Operator DevFeed: [Group Replication Beyond a Single Cluster: DC-DR with Percona (PS MySQL) Operator]() Original publisher: [Read original article]() Author: Anil Joshi Published: 2026-09-17T06:43:32Z Content type: tutorial Language: en Sources: [Blog - Percona]() Topics: [Replication](), [export](), [Percona Server for MySQL](), [configuration](), [Server](), [Kubernetes](), [Database]() Tags: [cloud](), [configuration](), [database-trends](), [featured](), [innodb-clusterset](), [insight-for-dbas](), [kubernetes](), [kubernetes-operators](), [mysql](), [mysql-group-replication](), [mysql-high-availability](), [mysql-replication](), [open-source](), [operator](), [percona-kubernetes-operators](), [percona-operator-for-mysql](), [percona-server-for-mysql](), [percona-software](), [replication](), [uncategorized](), [yaml]() ### AI overview This tutorial explains how to configure cross-site replication between two Percona Operator for MySQL clusters to create a ClusterSet environment for disaster recovery and switchover between data-center and disaster-recovery members. It covers deploying the clusters, retrieving endpoints and cluster names, transferring credentials, and initializing the DR cluster. ### Source excerpt A while ago, we discussed the cross-site replication feature of the Percona PXC operator. Recently, a similar cross-site replication feature was introduced in the Percona (PS MySQL) operator v1.2.0, a topology based on Group Replication/InnoDB Cluster. In this blog post, we will explore how to add a DR Cluster to an existing DC Cluster to ... Continued The post Group Replication Beyond a Single Cluster: DC-DR with Percona (PS MySQL) Operator appeared first on Percona. ## Building smarter AMRs with the Arduino® VENTUNO™ Q board DevFeed: [Building smarter AMRs with the Arduino® VENTUNO™ Q board]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-11T11:10:16Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Physical AI](), [Microcontroller](), [Linux](), [Computer vision](), [real-time](), [systems]() Tags: [amr](), [arduino](), [autonomous-mobile-robots](), [computer-vision](), [featured](), [linux](), [mcu](), [physical-ai](), [real-time](), [robotics](), [robots](), [ros](), [sensor](), [sensors](), [ventuno-q]() ### AI overview The article explains how Arduino's VENTUNO Q board could support autonomous mobile robots by combining a Linux-capable MPU with a real-time MCU. The MPU can run Linux, ROS 2, navigation, computer vision, and AI workloads, while the MCU handles motor control, encoder feedback, inertial measurements, local sensing, and motor-driver communication. ### Source excerpt Physical AI is based on the idea that intelligence shouldn't stop at perception: instead, it should bring to life systems able to sense their environment, reason about it, and act on it - all in one continuous loop. It's what makes the difference between a device that observes and one that acts. Autonomous mobile robots [...] The post Building smarter AMRs with the Arduino® VENTUNO™ Q board appeared first on Arduino Blog. ## How Featured's users make 100K media pitches per month on Vercel DevFeed: [How Featured's users make 100K media pitches per month on Vercel]() Original publisher: [Read original article]() Author: Susan Aziz Published: 2026-09-11T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [migration](), [vercel ai sdk](), [AI Chat](), [SDKs]() Tags: [ai](), [ai-gateway](), [aws](), [featured](), [infrastructure](), [migration](), [models](), [vercel](), [vercel-ai-sdk](), [workflow]() ### AI overview Featured describes migrating 374 Sanity sites from AWS Elastic Beanstalk to Vercel while using Vercel's AI SDK, AI Gateway, and Workflow SDK to reduce infrastructure work for a three-engineer team. ### Source excerpt Featured on Vercel 3 engineers supporting 3 brands and 100,000+ users on Vercel Migrated 374 Sanity sites from AWS Elastic Beanstalk to Vercel AI SDK and AI Gateway power Featured's chat bot across 17 models Workflow SDK replaced custom long-running job infrastructure Featured is a co-pilot for public relations (PR) that subject matter experts and PR teams use to find media opportunities. Tell Featured's agents what you know, and it surfaces opportunities across journalist requests, podcasts, awards, and GEO, with no PR background required. Founder Brett Farmiloe knows from experience how hard and time consuming getting press is. He spent 10 years running Markitors, a digital marketing agency with 500 small business clients. Every client, from an eyelash extension supplier to an equipment financing company, had real expertise to share, but no way to get it in front of journalists. PR, as Farmiloe puts it, "has always been about who has access to what." He founded Featured to change the question from who has access to who has knowledge. Featured connects one of their users with a journalist or publisher every 6 seconds. Their agents deliver more than 100,000 media pitches per month, and have sent over 100 million Help A Reporter Out (HARO) emails in the past year. Behind it all is an engineering team of just three people. With a team that lean, there's no time to manage servers or piece together custom integrations. Every hour spent on infrastructure is an hour taken away from building features what will help their customers land more media placements. The cost of managing infrastructure by hand Before Vercel, Featured's infrastructure work pulled the team away from product development. Hosting lived on AWS Elastic Beanstalk, AI features depended on custom provider integrations, and long-running, multi-step jobs ran on separate orchestration infrastructure. Each layer worked, but each one added operational overhead for a three-person team supporting multiple brands. ## Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive DevFeed: [Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive]() Original publisher: [Read original article]() Author: Dave Simon Published: 2026-09-10T14:04:38Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Amazon S3](), [Cloud](), [Streaming](), [Filesystems](), [mount](), [data](), [migration]() Tags: [architecture](), [automation](), [b2cloud](), [cloud](), [cloud-storage](), [data](), [featured](), [featured-cloud-storage](), [media](), [media-workflow](), [migration](), [mount](), [nas](), [object-storage](), [s3](), [storage](), [streaming]() ### AI overview Backblaze B2 integrates with Suite Studios' S3 Native File Streaming to provide drive-like, high-performance access to cloud-stored data. Teams can mount B2 buckets, stream only the file portions applications need, and use standard S3-compatible objects without duplicating or relocating datasets. The integration supports media production and other workflows involving large cloud datasets. ### Source excerpt Backblaze B2 now integrates with Suite Studios S3 Native File Streaming, giving teams high-performance, drive-like access to cloud data. Work directly with standard S3-compatible objects across media, scientific, geospatial, and engineering workflows without duplicating datasets or creating new storage silos. The post Backblaze B2 x Suite Studios: S3 Native File Streaming Turns B2 Cloud Storage Into a High-Performance Drive appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Announcing .NET 11 Release Candidate 1 DevFeed: [Announcing .NET 11 Release Candidate 1]() Original publisher: [Read original article]() Author: .NET Team Published: 2026-09-08T21:30:00Z Content type: release Language: en Sources: [.NET Blog]() Topics: [.NET](), [WinForms]() Tags: [announcements](), [asp-net-core](), [c-sharp](), [f-sharp](), [featured](), [net](), [net-11](), [net-maui](), [nuget](), [production](), [release](), [release-notes](), [updates](), [visual-studio](), [visual-studio-2026](), [windows](), [winforms]() ### AI overview .NET 11 Release Candidate 1 is available with a go-live support license. It includes updates across the runtime, SDK, MSBuild, NuGet, C#, F#, ASP.NET Core, .NET MAUI, and Windows Forms. ### Source excerpt .NET 11 Release Candidate 1 is available with improvements across libraries, runtime, SDK, MSBuild, NuGet, C#, F#, ASP.NET Core, .NET MAUI, and Windows Forms. The post Announcing .NET 11 Release Candidate 1 appeared first on .NET Blog. ## Introducing the Backblaze B2 MCP Server DevFeed: [Introducing the Backblaze B2 MCP Server]() Original publisher: [Read original article]() Author: Jeronimo De Leon Published: 2026-09-08T18:13:00Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [MCP Server](), [Model Context Protocol](), [Cloud](), [Authorization](), [Open Source]() Tags: [agents](), [ai](), [ai-ml](), [api](), [b2cloud](), [cloud](), [cloud-storage](), [developers](), [featured](), [featured-cloud-storage](), [mcp](), [mcp-server](), [object-storage](), [open-source](), [storage](), [uncategorized]() ### AI overview Backblaze introduces an open-source B2 MCP Server that lets AI agents operate cloud object storage through MCP-compatible clients. Access is constrained by the permissions of the connected B2 application key, including limits on buckets and operations. ### Source excerpt The Backblaze B2 MCP Server gives AI agents a safe, standard way to operate cloud object storage. Connect MCP-compatible clients to scoped B2 application keys, manage files and buckets, move large objects directly, and control destructive actions with configurable safeguards. The post Introducing the Backblaze B2 MCP Server appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Four Platforms, One Timeline: Bringing TAMS Workflow to Life on Backblaze B2 DevFeed: [Four Platforms, One Timeline: Bringing TAMS Workflow to Life on Backblaze B2]() Original publisher: [Read original article]() Author: Dave Simon Published: 2026-09-03T14:23:57Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [cloud-infrastructure](), [API](), [Kubernetes](), [browser](), [systems]() Tags: [api](), [article](), [b2cloud](), [browser](), [cloud](), [cloud-native](), [cloud-storage](), [featured](), [featured-cloud-storage](), [kubernetes](), [media](), [object-storage](), [storage]() ### AI overview The article explains how Time Addressable Media Store (TAMS) organizes live media by timeline-based, timestamped segments in object storage. It describes a cloud-native, interoperable workflow built around Backblaze B2 and ecosystem partners LiveWyer, CuttingRoom, and Drastic Technologies, enabling ingest, editing, distribution, playback, and archiving through shared access to the same content. ### Source excerpt TAMS organizes live media by time, giving distributed production teams faster access to shared footage. See how Backblaze B2, LiveWyer, CuttingRoom, and Drastic Technologies create an interoperable, cloud-native workflow for ingest, editing, playback, distribution, and archive with economics at scale. The post Four Platforms, One Timeline: Bringing TAMS Workflow to Life on Backblaze B2 appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Code42 Is Shutting Down. Is CrashPlan the Safe Choice, or Just the Convenient One? DevFeed: [Code42 Is Shutting Down. Is CrashPlan the Safe Choice, or Just the Convenient One?]() Original publisher: [Read original article]() Author: Kari Wilson Published: 2026-09-01T16:00:37Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [migration](), [Tooling](), [data loss prevention]() Tags: [alternatives](), [backing-up](), [backup](), [blog](), [businessbackup](), [cloud](), [cloud-storage](), [evaluation](), [featured](), [featured-backing-up](), [migration](), [post]() ### AI overview The article examines whether CrashPlan is an appropriate replacement for Code42 after Code42 stopped new sales in 2025 and plans to end support in 2026, with subsequent deletion of backup archives. It explains that Code42's backup business became a separate CrashPlan company in 2022 and urges IT teams to evaluate alternatives rather than assume CrashPlan is an automatic successor. It highlights migration incentives and a possible 5TB storage cap on some CrashPlan plans. ### Source excerpt Code42 support ends in 2026, but CrashPlan isn't the automatic successor it appears to be. Before migrating, understand the companies' split, CrashPlan's ownership, potential storage limits, evolving positioning, and the questions IT teams should ask when evaluating better backup alternatives. The post Code42 Is Shutting Down. Is CrashPlan the Safe Choice, or Just the Convenient One? appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## From voice command to robotic arm: how agentic AI on the edge is changing the factory floor DevFeed: [From voice command to robotic arm: how agentic AI on the edge is changing the factory floor]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-01T12:20:24Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Artificial Intelligence](), [foundation-models](), [Automation](), [Arduino](), [UNO Q](), [AI Agent](), [Physical AI](), [multimodal]() Tags: [agentic-ai](), [ai](), [arduino](), [automation](), [featured](), [foundation-models](), [industrial](), [inference](), [latency](), [manufacturing](), [multimodal](), [robotic-arm](), [robotics](), [smart-factory](), [uno-q](), [usb](), [voice-commands](), [voice-control]() ### AI overview The article describes a demonstration in which Forgis uses a foundation model running on an Arduino UNO Q board to convert voice commands into robotic-arm actions. The system processes multimodal factory data and performs inference locally, enabling real-time control without a cloud round trip. ### Source excerpt For years, bringing real intelligence to industrial automation meant expensive infrastructure, proprietary systems, and steep learning curves. That's changing - fast. Foundation models powerful enough to run at the edge are turning natural language into machine control, and the factory floor is starting to look a lot more like a conversation. AI as the new [...] The post From voice command to robotic arm: how agentic AI on the edge is changing the factory floor appeared first on Arduino Blog. ## Backblaze B2 To Encrypt New Uploads by Default DevFeed: [Backblaze B2 To Encrypt New Uploads by Default]() Original publisher: [Read original article]() Author: Backblaze Published: 2026-08-27T13:00:00Z Content type: release Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Cloud](), [Encryption](), [Security](), [configuration]() Tags: [b2cloud](), [cloud](), [cloud-storage](), [configuration](), [encryption](), [featured](), [featured-cloud-storage](), [secure-by-default](), [security](), [storage](), [uncategorized](), [update]() ### AI overview Backblaze B2 will automatically apply server-side AES-256 encryption to newly uploaded and copied object data at rest. SSE-B2 requires no application changes, added cost, or performance tradeoff, while SSE-C remains available for objects encrypted with customer-provided keys. ### Source excerpt Starting September 14, Backblaze B2 will automatically encrypt every new upload and destination copy at rest with AES-256. Always-on SSE-B2 strengthens security without application changes, added costs, or performance impact, while preserving SSE-C for customer-managed encryption keys when required instead. The post Backblaze B2 To Encrypt New Uploads by Default appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## How Adobe Reduced GPU Idle Time in Generative AI Training Through Faster Data Access and Checkpointing DevFeed: [How Adobe Reduced GPU Idle Time in Generative AI Training Through Faster Data Access and Checkpointing]() Original publisher: [Read original article]() Author: Maddie Presland Published: 2026-08-25T15:22:30Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [GPU](), [Training AI Models](), [cloud-infrastructure](), [networking](), [data]() Tags: [ai-ml](), [ai-training](), [b2cloud](), [cloud](), [cloud-storage](), [compute](), [ethernet](), [featured](), [featured-cloud-storage](), [gpu](), [model-training](), [networking]() ### AI overview Adobe's generative AI training pipeline left roughly two-thirds of GPU time waiting for data. The article attributes the waste to storage and retrieval bottlenecks, networking limits, and checkpointing overhead, and describes Adobe's use of a high-performance networking fabric and fragmented checkpoint storage to reduce delays. ### Source excerpt Adobe's experience reveals why GPUs sit idle during AI model training: slow storage, insufficient throughput, and uneven workloads. Learn how storage bottlenecks and uneven data loading leave expensive GPUs idle--and how always-hot, high-throughput object storage keeps AI training pipelines running efficiently at scale while reducing wasted compute costs. The post Why Your GPU Is Sitting Idle: The Data Pipeline Problem No One Talks About appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## How Generative Recommenders Are Redefining RecSys at Scale DevFeed: [How Generative Recommenders Are Redefining RecSys at Scale]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [recommendation systems](), [AI, ML & Data Engineering](), [Training AI Models](), [Language models]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [data-center-cloud](), [developer-tools-techniques](), [embedding](), [featured](), [generative](), [gpu](), [inference](), [latency](), [llms](), [machine-learning](), [machine-learning-artificial-intelligence](), [recommenders-personalization](), [scale]() ### AI overview The article examines the shift toward generative recommender systems and the challenges of training and serving them at large scale. ### Source excerpt Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and... ## Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents DevFeed: [Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-19T22:22:37Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Holoscan](), [AI-assisted coding](), [Command-line interface](), [Artificial Intelligence](), [real-time](), [Medical imaging](), [Robotics]() Tags: [agentic-ai-generative-ai](), [agents](), [ai-coding](), [clara](), [cli](), [computer-vision-video-analytics](), [edge](), [edge-computing](), [featured](), [healthcare-life-sciences](), [holoscan](), [medical-devices](), [medical-imaging](), [monai](), [nvidia](), [real-time](), [robotics](), [skills](), [video-analytics]() ### AI overview An NVIDIA Holoscan developer article about building real-time edge AI applications with CLI skills and AI coding agents, covering use cases including medical imaging and robotics. ### Source excerpt NVIDIA Holoscan is a platform for building real-time AI applications at the edge, from medical imaging to robotics. HoloHub is its companion repository: a... ## Building Federated Multimodal AI Workflows with NVIDIA FLARE DevFeed: [Building Federated Multimodal AI Workflows with NVIDIA FLARE]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-19T17:50:47Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [vlm](), [SDKs]() Tags: [agentic-ai-generative-ai](), [ai](), [data-science](), [edge-computing](), [featured](), [federated-learning](), [multimodal](), [multimodal-ai](), [nvidia](), [python](), [streaming](), [training](), [training-ai-models](), [updates](), [vlms](), [workflows]() ### AI overview The article explains how NVIDIA FLARE supports federated training for multimodal and vision-language models when data remains distributed across sites. It focuses on deciding which model state to exchange and on efficiently transferring and aggregating large updates through externalization, tensor streaming, and disk-backed aggregation. ### Source excerpt Modern vision-language models (VLMs) can support tasks such as visual question answering, captioning, and image-text reasoning. In practice, however, the data... ## Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control DevFeed: [Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Cosmos](), [AI, ML & Data Engineering]() Tags: [agentic-ai-generative-ai](), [cosmos](), [deployment](), [edge](), [edge-computing](), [featured](), [foundation-models](), [inference](), [jetson](), [latency](), [nvidia](), [on-device](), [physical-ai](), [post-training](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [thor](), [tutorial]() ### AI overview A tutorial on post-training NVIDIA Cosmos 3 Edge as an on-device robot manipulation policy, serving it on Jetson Thor, running receding-horizon inference, and evaluating it in closed-loop simulation. ### Source excerpt Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for... ## Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator DevFeed: [Evaluating AI Agent Skill Performance with NVIDIA SkillEvaluator]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Agent Skills](), [AI Agent](), [Benchmark](), [Nvidia](), [Open Source](), [Agent Harness](), [Security]() Tags: [agent](), [agent-skill](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [benchmark](), [build-ai-agents](), [codex](), [cursor](), [developer-tools-techniques](), [featured](), [nvidia](), [open-source](), [prompt-injection](), [sandbox](), [security](), [simulation-modeling-design](), [trustworthy-ai]() ### AI overview NVIDIA SkillEvaluator is an open-source evaluation layer for measuring how packaged skills affect AI-agent performance. It compares agent runs with and without a skill, using static validation, embedding-based distinctiveness checks, and live task evaluations in isolated sandboxes. The article reports benchmark results for more than 300 verified skills across over 30 NVIDIA products and describes integrations with Claude Code, Codex, Cursor, Skills.sh, ClawHub, and Hermes Hub. ### Source excerpt AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding... ## Jamf Administrators: Your Backup Deployment Just Got Simpler DevFeed: [Jamf Administrators: Your Backup Deployment Just Got Simpler]() Original publisher: [Read original article]() Author: Kari Wilson Published: 2026-08-19T13:28:18Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Deployment](), [Provisioning](), [configuration](), [App](), [Orchestration]() Tags: [app](), [backing-up](), [backup](), [businessbackup](), [configuration](), [deployment](), [featured](), [featured-backing-up](), [onboarding](), [provisioning]() ### AI overview This article explains how Backblaze simplifies backup deployment for Mac fleets managed with Jamf. It describes fixed-email matching for devices with known owners, dynamic user detection for devices assigned later, and the ability to combine both methods within one deployment. ### Source excerpt Simplify Mac backup deployment with Backblaze and Jamf. Flexible user matching reduces manual setup, streamlines onboarding, and helps IT teams protect every device using their existing Jamf workflows. The post Jamf Administrators: Your Backup Deployment Just Got Simpler appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit DevFeed: [How AI Coding Agents Can Unlock Materials Simulation with NVIDIA ALCHEMI Toolkit]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-18T18:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [ALCHEMI](), [AI-assisted coding](), [Agent Skills](), [PyTorch](), [CUDA](), [GPU](), [Python](), [AI Development]() Tags: [agent-skills](), [agentic-ai-generative-ai](), [ai-agent](), [ai-coding](), [alchemi](), [coding](), [computational-chemistry-materials-science](), [compute](), [cuda](), [developer-tools-techniques](), [featured](), [gpu](), [python](), [pytorch](), [simulation-modeling-design]() ### AI overview This article presents an end-to-end workflow for using AI coding agents with NVIDIA ALCHEMI Toolkit to build GPU-accelerated atomistic materials simulations. It explains how ALCHEMI agent skills and reference files provide API knowledge, describes the Python, PyTorch, CUDA and NVIDIA GPU environment, and reports validation across 45 generated pipelines. ### Source excerpt Atomistic simulation requires three things: knowledge of the science, compute-efficient implementation of simulations, and accessible interfaces to the... ## Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy DevFeed: [Run Massive-Scale UMAP in Minutes Using Multiple GPUs--Without Losing Accuracy]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-18T16:48:08Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Data analysis](), [GPU](), [Graphs](), [datasets](), [RAPIDS](), [Nvidia]() Tags: [accelerate](), [analysis](), [cuda-x](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [datasets](), [feature](), [featured](), [gpu](), [graphs](), [multi-gpu](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [training](), [vector](), [visualization]() ### AI overview This article explains how multi-GPU UMAP scales dimensionality reduction to datasets containing tens to hundreds of millions of vectors. A feature in NVIDIA cuML and cuVS 25.06 distributes all-neighbors kNN graph construction across multiple GPUs, enabling workloads of several hundred gigabytes to run in minutes while preserving nearest-neighbor relationships and accuracy. ### Source excerpt Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction. Applications... ## Want to use AI agents safely? Start with design DevFeed: [Want to use AI agents safely? Start with design]() Original publisher: [Read original article]() Author: Colin Eberhardt Published: 2026-08-18T13:12:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [AI Strategy](), [Security](), [Monitoring](), [Architecture & Design](), [Resilience]() Tags: [agentic-ai](), [agents](), [ai](), [artificial-intelligence](), [auditability](), [design](), [end-to-end-process](), [featured](), [governance](), [guardrails](), [monitoring](), [observability](), [operational-resilience](), [quality](), [risk-management](), [security](), [service-design](), [systems](), [trust]() ### AI overview This article argues that organisations adopting AI agents should begin with process and system design rather than controls alone. It explains that design should account for human and machine strengths, establish proportionate guardrails, and define how observability and monitoring evolve as the system matures. ### Source excerpt Concerns about control are one of the biggest barriers to adopting agentic AI, particularly in regulated environments. In this post, we discuss how organisations can harness AI safely by designing processes around the strengths of both humans and machines, then applying the right controls, guardrails and monitoring. ## Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer DevFeed: [Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-17T18:12:48Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nemotron](), [NVFP4](), [quantization](), [Post-training optimization](), [Nvidia](), [post-training](), [Mamba]() Tags: [agentic-ai-generative-ai](), [benchmarks](), [compute](), [developer-tools-techniques](), [developers](), [edge-computing](), [evaluation](), [featured](), [latency](), [mamba](), [megatron](), [memory](), [model](), [model-optimizer](), [models](), [nemotron](), [nvfp4](), [nvidia](), [open](), [post-training](), [quantization](), [speed](), [training](), [training-ai-models]() ### AI overview This tutorial explains how quantization-aware distillation (QAD) creates the Nemotron 3.5 Lightning NVFP4 checkpoint using NVIDIA Model Optimizer. It covers post-training quantization, teacher-student distillation, and evaluation, showing how QAD can recover accuracy while reducing memory usage and increasing throughput. ### Source excerpt Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find... ## Results from the Backblaze Generative AI Media Hackathon DevFeed: [Results from the Backblaze Generative AI Media Hackathon]() Original publisher: [Read original article]() Author: Jeronimo De Leon Published: 2026-08-14T15:52:05Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [Generative AI](), [Orchestration](), [cloud-infrastructure](), [For the Love of Code](), [Open Source](), [SDKs]() Tags: [2026](), [ai-ml](), [b2cloud](), [cloud](), [cloud-storage](), [developer](), [featured](), [featured-cloud-storage](), [generative](), [hackathon](), [object-storage](), [open-source](), [orchestration](), [sdk](), [tech-lab](), [technical]() ### AI overview The article examines the strongest projects from the Backblaze Generative AI Media Hackathon and argues that production-ready generative media applications depend on more than generating images, video, or audio. It highlights storage design, provenance, verification, correction, deletion protection, and orchestration across multiple providers. ### Source excerpt See what the strongest projects from the Backblaze Generative AI Media Hackathon had in common. These five generative media apps show how storage and orchestration become part of production-ready design. The post Results from the Backblaze Generative AI Media Hackathon appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research DevFeed: [Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research]() Original publisher: [Read original article]() Author: Stephanie Doyle Published: 2026-08-13T15:08:38Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [dataset](), [DRIVE](), [Machine learning](), [Transformer](), [Disk image]() Tags: [ai-ml](), [articles](), [b2cloud](), [cloud-storage](), [data](), [drive](), [featured](), [featured-cloud-storage](), [hard-drive-stats](), [ml](), [research](), [source](), [storage]() ### AI overview The article explains how Backblaze Drive Stats evolved from an internal hard-drive reliability tool into an open dataset used in academic and AI/ML research. It describes the dataset's real-world scale, quarterly publication, SMART attributes, labeled failures, broad manufacturer coverage, and use in hard-drive failure prediction research. ### Source excerpt Backblaze Drive Stats has been cited in more than 105 academic papers and AI/ML projects since 2018. Explore the research it powers and download the dataset. The post Backblaze Drive Stats: How an Open Dataset Powers Academic and AI/ML Research appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72 DevFeed: [Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-12T18:23:13Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Chat](), [AI Bots]() Tags: [agentic](), [agentic-ai-generative-ai](), [cache](), [data-center](), [developer-tools-techniques](), [featured](), [gb300-nvl72](), [gpu](), [inference](), [mixture-of-experts-moe](), [model](), [nvfp4](), [nvidia](), [open-source](), [performance](), [reasoning](), [routing](), [top-stories]() ### AI overview The article describes serving Alibaba's open-weight Qwen3.8-2.4T-A95B model on NVIDIA GB300 NVL72 systems for large-scale reasoning and agentic workloads. ### Source excerpt Alibaba released the open weights for Qwen3.8-2.4T-A95B (Qwen3.8-Max), its largest open-weight model, bringing near-frontier capabilities to the open... 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