# real-time A real-time system is an information-processing system that performs application functions within predictable, specific time constraints. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## Mercure Broadcasting in Laravel 13.32 DevFeed: [Mercure Broadcasting in Laravel 13.32]() Original publisher: [Read original article]() Author: Paul Redmond Published: 2026-09-16T02:44:52Z Content type: release Language: en Sources: [Laravel]() Topics: [Laravel](), [releases](), [Release notes](), [real-time](), [enum](), [WebSocket]() Tags: [enums](), [features](), [github](), [laravel](), [laravel-releases](), [news](), [queue](), [real-time](), [release](), [release-notes](), [storage](), [update](), [websockets]() ### AI overview Laravel 13.32 adds a Mercure broadcast driver, filesystem methods for copying and moving files between disks, and enum support for queue pause and resume methods. The release also includes additional fixes and updates. ### Source excerpt Laravel 13.32 adds a Mercure broadcast driver, Storage::copyToDisk() and moveToDisk(), enum support in queue pause/resume, and more. The post Mercure Broadcasting in Laravel 13.32 appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## pgAssistant 3.8.0 : continuous improvement loop for Postgres DevFeed: [pgAssistant 3.8.0 : continuous improvement loop for Postgres]() Original publisher: [Read original article]() Author: Pgassistant Dev Team Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [PostgreSQL news]() Topics: [PostgreSQL](), [Monitoring](), [recommendations](), [configuration](), [real-time]() Tags: [configuration](), [measurements](), [monitoring](), [postgres](), [postgresql](), [real-time](), [recommendations](), [release]() ### AI overview pgAssistant 3.8.0 expands the PostgreSQL analysis and tuning tool into a continuous improvement platform. It adds historical workload and environment measurements, compares consecutive collections, tracks recommendations and configuration changes, and helps teams measure changes while distinguishing correlation from causation. ### Source excerpt With this release, pgAssistant is evolving beyond PostgreSQL analysis and tuning to become a continuous PostgreSQL improvement platform. The new positioning is built around a continuous improvement loop: Observe -> Diagnose -> Prioritize -> Plan -> Implement -> Collect again -> Measure pgAssistant already helped identify what should be improved and turn recommendations into a prioritized Executive Plan with clear DEV and OPS ownership. Combined with pgAssistant Collector, version 3.8.0 goes further by adding historical workload and environment measurements. The objective is to answer four essential questions: What should we improve? What did we decide to do? What did we actually change? What was the result? Workload Insights compares consecutive collections and highlights: new and no-longer-detected recommendations; changes to the PostgreSQL version and configuration; workload evolution by statement type; changes in execution time and call volume; the queries with the greatest impact on the overall workload. The ambition is to correlate the application of pgAssistant recommendations and the Executive Plan with observed performance changes. Correlation is not causation, and a recommendation that is no longer detected does not necessarily prove that it was implemented. pgAssistant keeps these distinctions explicit while bringing the relevant evidence together in one place. pgAssistant is not intended to replace real-time monitoring. Monitoring shows what is happening now; pgAssistant helps teams decide what to improve next, organize the remediation work, and measure what changed afterwards. From recommendations to action--and from action to measurable evidence. pgAssistant 3.8.0: https://github.com/beh74/pgassistant-community pgAssistant Collector: https://github.com/beh74/pgassistant-collector pgAssistant Grafana : https://github.com/beh74/pgassistant-grafana ## Google and OpenAI take different approaches to reducing voice-agent latency DevFeed: [Google and OpenAI take different approaches to reducing voice-agent latency]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T21:50:15Z Content type: comparison Language: en Sources: [The New Stack]() Topics: [Latency](), [Google AI](), [OpenAI](), [API](), [Orchestration](), [real-time](), [Artificial Intelligence]() Tags: [ai](), [ai-agents](), [ai-engineering](), [api](), [comparison](), [google-ai](), [large-language-models](), [latency](), [openai](), [orchestration](), [real-time]() ### AI overview The article compares Google's Gemini 3.8 Live Extended Thinking with OpenAI's GPT-Live-1 for reducing latency in voice agents. Google keeps speech, reasoning, and asynchronous tool execution in one stateful session, while OpenAI uses a real-time conversation model alongside a backend reasoning model, shifting more orchestration to the application. ### Source excerpt Voice agents have a latency problem that shows up as soon as they have to do real work. Within five The post OpenAI's voice model doesn't think. That's the point. appeared first on The New Stack. ## Architecting resilient authentication with Amazon Cognito multi-Region replication DevFeed: [Architecting resilient authentication with Amazon Cognito multi-Region replication]() Original publisher: [Read original article]() Author: Abrom Douglas Published: 2026-09-15T19:00:52Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [Amazon Cognito](), [Replication](), [Authentication](), [identity and access management](), [Amazon Web Services](), [real-time](), [JSON Web Tokens]() Tags: [advanced-300](), [amazon-cognito](), [authentication](), [aws](), [failover](), [identity-and-access-management](), [real-time](), [replication](), [security-blog](), [security-identity-compliance](), [technical-how-to](), [token]() ### AI overview This AWS article explains how Amazon Cognito multi-Region replication supports resilient authentication by replicating user pools across AWS Regions, with eventual consistency, failover, and interoperable sessions and JSON web tokens. It also covers preparation, architecture decisions, and failover strategies for B2C, B2B, and M2M use cases. ### Source excerpt Your consumer identity and access management (CIAM) system is the foundation of your customer experience. It's how users sign in, access services, and engage with your applications. As your business scales across geographies, ensuring authentication is always available becomes a core architectural requirement. However, building multi-Region authentication has traditionally required complex custom replication solutions that [...] ## Introducing Gemini 3.8 Live and 3.8 Live Extended Thinking DevFeed: [Introducing Gemini 3.8 Live and 3.8 Live Extended Thinking]() Original publisher: [Read original article]() Author: Tom Ouyang Published: 2026-09-15T17:05:57Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [Artificial Intelligence](), [real-time](), [speech-to-speech](), [Google](), [API]() Tags: [ai](), [api](), [audio](), [cost](), [efficiency](), [gemini](), [google](), [none](), [real-time](), [speech-to-speech](), [tools]() ### AI overview Google introduces Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, two models designed for near-real-time voice interaction and reasoning. The release describes visual grounding, multilingual conversation, background tool and API execution, and deeper reasoning for complex workflows. ### Source excerpt Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet, built for natural conversation. ## How WebRTC Scales: Signaling, NAT Traversal, and the Mesh/SFU/MCU Tradeoff DevFeed: [How WebRTC Scales: Signaling, NAT Traversal, and the Mesh/SFU/MCU Tradeoff]() Original publisher: [Read original article]() Author: Karan Pratap Singh Published: 2026-09-15T15:57:39Z Content type: article Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [WebRTC](), [real-time](), [browsers](), [API](), [JavaScript](), [WebSocket](), [Firewall](), [Network](), [servers]() Tags: [api](), [browsers](), [decoding](), [distributed-system](), [firewall](), [http](), [javascript](), [network](), [networking](), [real-time](), [server](), [webrtc]() ### AI overview This article explains how WebRTC enables browsers to exchange audio, video, and data directly. It covers the three WebRTC APIs, signaling through WebSockets or HTTP, NAT traversal using ICE, STUN, and TURN, and the mesh, SFU, and MCU approaches to scaling media delivery. ### Source excerpt Web Real-Time Communication (or WebRTC) is the open standard browsers use to send audio, video, and data straight to each other. There's no plugin or native app, nothing beyond an API that every brows ## Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group DevFeed: [Aiven, Confluent, Redpanda, StreamNative and Ververica Form Streamhouse Working Group]() Original publisher: [Read original article]() Author: Streamhouse Working Group Published: 2026-09-15T07:00:00Z Content type: release Language: en Sources: [Confluent: Data in motion]() Topics: [data-architecture](), [real-time](), [data](), [Streaming](), [Artificial Intelligence]() Tags: [agents](), [ai](), [architectures](), [confluent](), [data](), [distributed](), [news](), [real-time](), [streaming]() ### AI overview Aiven, Confluent, Redpanda, StreamNative, and Ververica announced the Streamhouse Working Group and published a vendor-neutral definition of Streamhouse. The proposed data architecture is designed to keep business context continuously available to production applications, analytics, and AI agents, with real-time, production-native, and decentralized attributes. ### Source excerpt New industry initiative establishes an open category for data architectures that power real-time applications and AI agents ## Gemini 3.8 Live models now available on AI Gateway DevFeed: [Gemini 3.8 Live models now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [real-time](), [Artificial Intelligence](), [API](), [Google](), [WebSocket](), [SDKs]() Tags: [ai](), [api](), [cost](), [failover](), [gemini](), [google](), [real-time](), [realtime](), [sdk](), [websocket]() ### AI overview Vercel announces that Google's Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking models are available on AI Gateway. The models support real-time spoken interactions, audio and visual grounding, multilingual switching, background tool calls, and parallel reasoning through the AI SDK's realtime API. ### Source excerpt Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking from Google are now available on AI Gateway. Both models support real-time spoken interactions for voice assistants, conversational experiences, and applications that respond through audio. google/gemini-3.8-live supports real-time audio, visual grounding, automatic switching across 97 languages, and background tool calls while the conversation continues. google/gemini-3.8-live-extended-thinking adds multi-step reasoning that runs in parallel with speech, allowing it to acknowledge requests and narrate progress without interrupting the conversation. Use either model through the AI SDK's realtime API. Install the Gateway provider and a WebSocket client: Mint a short-lived token, open the WebSocket, and use the model adapter to serialize and parse realtime events: See the realtime quickstart for more details on realtime events and WebSocket connections. Try Gemini 3.8 Live or Gemini 3.8 Live Extended Thinking in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. Read more ## Streamhouse Working Group proposes a vendor-neutral architecture for real-time data infrastructure DevFeed: [Streamhouse Working Group proposes a vendor-neutral architecture for real-time data infrastructure]() Original publisher: [Read original article]() Author: Alexander Gallego Published: 2026-09-15T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Data Infrastructure](), [real-time](), [Streaming](), [Artificial Intelligence](), [Redpanda-Connect]() Tags: [ai](), [announcement](), [data-infrastructure](), [real-time](), [redpanda-connect](), [streaming]() ### AI overview Redpanda says it has joined Aiven, Confluent, StreamNative, and Ververica to form the Streamhouse Working Group, which proposes a vendor-neutral architecture for real-time operational data infrastructure. The article distinguishes Streamhouse from the lakehouse by focusing on continuously processing current data for production applications and AI agents. ### Source excerpt Redpanda has joined Aiven, Confluent, StreamNative, and Ververica to form the Streamhouse Working Group. Here's what that means for the future of real-time data infrastructure. ## Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks DevFeed: [Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks]() Original publisher: [Read original article]() Author: Thangam Vaiyapuri; Jay Palaniappan; B. Micheal Okutubo; Zifei Feng Published: 2026-09-14T21:04:30Z Content type: release Language: en Sources: [Databricks]() Topics: [Streaming](), [databricks](), [real-time](), [Amazon S3](), [API]() Tags: [amazon-s3](), [api](), [data-engineering](), [databricks](), [engineering](), [fraud-detection](), [infrastructure](), [monitoring](), [net-11-preview-7](), [production](), [real-time](), [streaming]() ### AI overview Databricks announces on-demand state repartitioning for Apache Spark Structured Streaming in Public Preview, available in Databricks Runtime 18 and later. The capability lets production stateful streaming queries resize their partition count while preserving checkpoint state, supporting workloads such as aggregations, stream-stream joins, deduplication, sessionization, and transformWithState. Coveo reports reducing related Amazon S3 API costs by 40%. ### Source excerpt Anyone running stateful Apache Spark™ Structured Streaming queries in production... ## The Death of the Static UI: Building Context-Aware Mobile Apps in 2026 DevFeed: [The Death of the Static UI: Building Context-Aware Mobile Apps in 2026]() Original publisher: [Read original article]() Author: Rudraksh Shukla Published: 2026-09-14T17:02:27Z Content type: tutorial Language: en Sources: [Flutter Community - Medium]() Topics: [Mobile](), [ui](), [User experience (UX)](), [personalization](), [Flutter](), [real-time]() Tags: [agentic-ai](), [artificial-intelligence](), [dark-mode](), [flutter](), [machine-learning](), [mobile](), [mobile-development](), [personalization](), [real-time](), [recommendations](), [ui](), [ux]() ### AI overview This developer article argues that mobile interfaces are evolving from fixed layouts into context-aware surfaces that adapt navigation, touch targets, color, density, and surfaced actions using on-device signals. It discusses motion, location, time, usage history, and device or network state, with Flutter examples and references to patterns associated with Spotify and Netflix. ### Source excerpt Every app you've ever shipped made the same quiet assumption: the interface is a fixed thing. You design a screen, you lay out the widgets, and every user sees the same arrangement in the same order -- a 22-year-old on a commuter train at 8am and a 60-year-old at home on a Sunday get pixel-identical layouts. For thirty years that was simply what a UI was. That assumption is dying. In 2026 the leading mobile apps treat the interface as a live surface that reshapes itself in real time -- reordering navigation, resizing touch targets, shifting color and density, surfacing the one action you're most likely to want next -- driven by on-device signals about who you are, where you are, and what you're doing right now. The static screen is becoming the exception, not the default. Here's what's actually driving it, what it takes to build, and what it looks like in code -- with Flutter examples throughout. From static layout to living surface The old personalization playbook was recommendation, not adaptation. Netflix reordered a content row; Spotify built you a playlist. The chrome around those recommendations -- the navigation, the layout, the visual system -- stayed frozen. Context-aware UX pushes personalization down into the interface itself. Concretely, an adaptive UI reacts to signals like these: Motion and activity -- accelerometer and gyroscope tell you the user is walking, driving, or still. A UI can enlarge touch targets and simplify layout when it detects movement, cutting mis-taps. Location and environment -- outdoors in bright light, boost contrast and switch to a high-legibility mode; on a known Wi-Fi network at home, load richer media. Time and calendar -- automatic dark mode at night, a leaving-for-a-meeting layout when the next calendar event is 15 minutes out. Usage history -- promote the three features this user actually touches, demote the ones they never open. A finance app foregrounds transfer for a power user and check balance for a casual one. Device and networ ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## Infineon RISC-V for Automotive at Hot Chips 2026 DevFeed: [Infineon RISC-V for Automotive at Hot Chips 2026]() Original publisher: [Read original article]() Author: Vic A Published: 2026-09-13T21:58:44Z Content type: article Language: en Sources: [ServeTheHome]() Topics: [RISC-V](), [Microcontroller](), [real-time](), [Low Latency](), [Architecture & Design](), [Security](), [IO](), [Inference Performance]() Tags: [2026](), [architecture](), [automotive](), [compute](), [cost](), [efficiency](), [infineon](), [latency](), [low-latency](), [microcontrollers](), [mobile](), [performance](), [posix](), [processors](), [real-time](), [risc-v](), [security]() ### AI overview Infineon presents RISC-V automotive processors for next-generation vehicle architectures. The article covers real-time control, low latency, power efficiency, security, zone controllers, central car computers, and heterogeneous workloads including DSP, AI inference, audio, and POSIX-based services. ### Source excerpt At Hot Chips 2026, Infineon presented a case for using RISC-V in various automotive processors in next-generation cars The post Infineon RISC-V for Automotive at Hot Chips 2026 appeared first on ServeTheHome. ## Quiz: Traditional Face Detection With Python DevFeed: [Quiz: Traditional Face Detection With Python]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-13T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Python](), [object-detection](), [Algorithm](), [real-time]() Tags: [article](), [object-detection](), [python](), [real-time](), [tutorial]() ### AI overview An interactive 10-question quiz that tests understanding of traditional face detection with Python, including image representation, Haar-like features, integral images, AdaBoost, and cascading classifiers. ### Source excerpt Test your understanding of face detection with Python. Review Haar-like features, integral images, AdaBoost, and cascading classifiers. ## CrowdStrike Delivers the Next Evolution of the Agentic SOC DevFeed: [CrowdStrike Delivers the Next Evolution of the Agentic SOC]() Original publisher: [Read original article]() Author: Brandon Benke Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Agentic SOC](), [SOC](), [Security](), [Artificial Intelligence](), [Automation](), [data](), [real-time](), [Reconnaissance](), [Cloud](), [Network]() Tags: [2026](), [agentic](), [agentic-soc](), [agents](), [ai](), [automation](), [cloud](), [data](), [identity](), [network](), [operations](), [platform](), [real-time](), [saas](), [security](), [soc]() ### AI overview CrowdStrike describes the next evolution of its agentic SOC, where analysts and AI agents work together in a unified system to investigate and respond to threats in real time. The Falcon platform combines data generation, enrichment, investigation, orchestration, and governance, with capabilities for detection-ready third-party data and coordinated specialist agents. ### Source excerpt Expert agents that reason together, learn your environment, and run on data CrowdStrike owns. See how we deliver the agentic SOC. Learn more! ## HeyGen x Google Cloud: Bringing Avatar IV to TPUs DevFeed: [HeyGen x Google Cloud: Bringing Avatar IV to TPUs]() Original publisher: [Read original article]() Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [Google](), [Cloud](), [Artificial Intelligence](), [Optimization](), [AI Infrastructure](), [Transformer](), [PyTorch](), [Compiler](), [Streaming](), [real-time](), [Hardware](), [API]() Tags: [ai](), [ai-infrastructure](), [api](), [cloud](), [code](), [compiler](), [engineering](), [generation](), [google](), [hardware](), [model](), [optimization](), [performance](), [pytorch](), [real-time](), [streaming](), [time](), [tpu](), [transformers](), [video]() ### AI overview HeyGen and Google Cloud describe porting the 18B+ parameter Avatar IV talking-head video generation pipeline to an eight-chip Trillium TPU host. Using torchax, JAX, XLA, FSDP sharding, Ulysses sequence parallelism, and custom Pallas kernels, the team improved performance by 1.86x for real-time chunked streaming while preserving output quality through strict quality gates. ### Source excerpt HeyGen ported their 18B+ parameter Avatar IV video generation model to Google Cloud's Trillium (v6e) TPUs via torchax and XLA, utilizing FSDP and Ulysses sequence parallelism across an eight-chip mesh. To achieve a 1.86x speedup for real-time streaming, the engineering team pipelined exposed all-to-all collectives, aligned sparse attention block sizes to eliminate mask padding, and bypassed softmax serial dependencies using a precomputed Cauchy-Schwarz upper bound. These custom Pallas kernel and compiler optimizations were deployed only after passing rigorous two-tier quality gates to guarantee byte-identical or mathematically equivalent pixel outputs. ## Scaling real-time AI agents with session-aware load balancing DevFeed: [Scaling real-time AI agents with session-aware load balancing]() Original publisher: [Read original article]() Author: Simerus Mahesh Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [real-time](), [Artificial Intelligence](), [Streaming](), [Server](), [gRPC](), [WebSocket](), [cpu](), [Concurrent Programming]() Tags: [ai-agents](), [concurrent](), [cpu](), [real-time](), [routing](), [server](), [streaming]() ### AI overview This article explains why real-time AI agents require session-aware load balancing. Long-lived, stateful bidirectional streams make request rates and CPU utilization insufficient measures of backend capacity. The proposed approach tracks active sessions at the application level and combines session counts with CPU metrics to distribute traffic and avoid bottlenecks. ### Source excerpt Real-time AI agents break traditional request-response load balancing paradigms because they rely on long-lived, stateful bidirectional streams that obscure true server capacity. To solve this, developers must implement application-level session tracking directly within the runtime to accurately measure the committed concurrent workload of active conversations. By feeding these precise session counts alongside standard CPU utilization metrics into a hybrid routing algorithm, infrastructure can effectively distribute stateful AI traffic and prevent individual backend bottlenecks. ## 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. ## Unpacking My Bags: How I Found Belonging, Scale, and Real-Time Networking at Cisco DevFeed: [Unpacking My Bags: How I Found Belonging, Scale, and Real-Time Networking at Cisco]() Original publisher: [Read original article]() Author: Altanai Bisht Published: 2026-09-10T12:00:13Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [WebRTC](), [networking](), [real-time](), [Open Source](), [Software](), [Machine learning]() Tags: [be-you-with-us](), [career-growth](), [engineering](), [lovewhereyouwork](), [networking](), [open-source](), [real-time](), [software](), [streaming](), [voice](), [we-are-cisco](), [wearecisco](), [webrtc](), [women-in-tech](), [writing]() ### AI overview Software engineer Altanai B. describes a non-linear career spanning startups, research and development, product engineering, streaming, telecoms, and Cisco. She reflects on her long-standing curiosity about how real-time networked communications work, including writing a WebRTC guide and building an open-source robot using edge machine learning. ### Source excerpt Software Engineer Altanai B. shares her non-linear career journey and how she finally found a place to unpack her bags and belong at Cisco. ## Build Voice AI Experiences with Twilio and GPT-Live-1 in the OpenAI API DevFeed: [Build Voice AI Experiences with Twilio and GPT-Live-1 in the OpenAI API]() Original publisher: [Read original article]() Author: Lenore Files Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [voice ai](), [real-time](), [API](), [SDKs](), [OpenAI](), [Node.js](), [Python]() Tags: [ai](), [api](), [developer-insights](), [openai](), [real-time](), [tutorials](), [voice](), [voice-ai](), [websocket]() ### AI overview This tutorial article explains how to build real-time voice AI applications by connecting Twilio Agent Connect with OpenAI's GPT-Live-1 through the OpenAI API. It provides tutorials, sample applications, SDK support, and documentation for voice assistants and outbound voice agents using Node.js and Python. ### Source excerpt Build real-time voice AI Agents with Twilio and GPT-Live-1 in the OpenAI API with these tutorials, sample apps, and more. ## HPE Alletra Storage MP B10000 10.6.0 Arrives With Six-Node Scale-Out and Agentic Support Automation DevFeed: [HPE Alletra Storage MP B10000 10.6.0 Arrives With Six-Node Scale-Out and Agentic Support Automation]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-09T16:17:13Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [releases](), [Software](), [Automation](), [ransomware](), [real-time](), [data]() Tags: [agentic](), [architecture](), [automation](), [data](), [energy](), [enterprise](), [enterprise-storage](), [hpe](), [performance](), [products](), [ransomware](), [real-time](), [release](), [scale](), [software](), [storage](), [update]() ### AI overview HPE has generally released version 10.6.0, also called Release 6, for the Alletra Storage MP B10000. The update expands disaggregated block-and-file storage from four to six controller nodes, adds agent-based support automation and built-in real-time ransomware detection, and increases the StoreMore Guarantee to a 5:1 effective capacity ratio. ### Source excerpt HPE has made the 10.6.0 software release for the Alletra Storage MP B10000 generally available, landing inside the Q3 2026 window the company set when it previewed the release in May. HPE is also calling it Release 6 in its channel materials. The update takes the B10000's disaggregated block-and-file architecture from four controller nodes to The post HPE Alletra Storage MP B10000 10.6.0 Arrives With Six-Node Scale-Out and Agentic Support Automation appeared first on StorageReview.com. ## This smart boxing band takes advantage of the new Arduino Nesso N1 DevFeed: [This smart boxing band takes advantage of the new Arduino Nesso N1]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-09T15:36:33Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [Hardware](), [data](), [real-time](), [Artificial Intelligence](), [post-training]() Tags: [ai](), [arduino](), [boxing](), [boxing-training](), [data](), [hardware](), [imu](), [nesso-n1](), [performance](), [post-training](), [real-time](), [smart-boxing-band](), [training]() ### AI overview An Arduino Nesso N1-based smart boxing band combines punch recognition, biometric tracking, and real-time feedback. It uses a pulse sensor and the board's IMU to relate punch types and movement to heart rate, while logging data for post-training analysis. ### Source excerpt Data is now a huge part of many sports, from F1 racing to football. Presenting that data to fans is secondary to the real purpose: helping teams and athletes maximize performance. No matter what sport you enjoy, you can benefit from that kind of quantified training. Manivannan proved that by building his Smart AI Boxing [...] The post This smart boxing band takes advantage of the new Arduino Nesso N1 appeared first on Arduino Blog. ## The Reflexes Machine turns a reaction game into an interactive experience DevFeed: [The Reflexes Machine turns a reaction game into an interactive experience]() Original publisher: [Read original article]() Author: Arduino Team Published: 2026-09-09T12:37:27Z Content type: article Language: en Sources: [Arduino Blog]() Topics: [Arduino](), [UNO Q](), [Computer vision](), [webcam](), [real-time](), [Hardware]() Tags: [architecture](), [arduino](), [building](), [camera](), [computer-vision](), [hardware](), [i2c](), [led](), [modulino-nodes](), [project](), [reaction-game](), [real-time](), [reflex-game](), [reflexes-machine](), [sensors](), [uno-q]() ### AI overview An Arduino community member built Reflexes Machine, an arcade-style reaction game using an Arduino UNO Q board, Modulino nodes, a USB camera, illuminated buttons, LED Matrix displays, and sound effects. The camera detects a player's face and automatically starts the countdown. ### Source excerpt What happens when you combine a little imagination with a powerful dual-brain board and a handful of building-block sensors? In the case of Arduino community member LucaDilo, you get a reaction game that doesn't simply wait for someone to press a button... it actually notices when you walk up and invites you to play. Reflexes [...] The post The Reflexes Machine turns a reaction game into an interactive experience appeared first on Arduino Blog. [Next page]()