# real-time Published articles for real-time. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## September 2026 newsletter DevFeed: [September 2026 newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-17T13:42:49Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [releases](), [Replication](), [Grafana Cloud Metrics]() Tags: [2026](), [caching](), [clickhouse](), [events](), [news](), [newsletter](), [real-time](), [release](), [replication](), [september-2026]() ### AI overview The September 2026 ClickHouse newsletter covers the 26.8 release, including custom HTTP handlers, pipelined SQL, dynamic query filtering, tokenizer support, and other features. It also summarizes replication-queue diagnostics, CostBench results, preview releases, community contributions, and upcoming events. ### Source excerpt Welcome to the September 2026 ClickHouse newsletter, featuring ClickHouse 26.8, PromQL, On-Demand Compute, CostBench results, and the latest community news and events. ## "Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE DevFeed: ["Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE]() Original publisher: [Read original article]() Author: Kent Marten; Sergei Fedorov Published: 2026-09-16T17:14:05Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [SQL](), [databricks](), [Cybersecurity](), [Security & Privacy]() Tags: [count](), [cybersecurity](), [databricks](), [failed](), [false-positives](), [functions](), [login](), [partition](), [product](), [real-time](), [regex](), [sql](), [window-functions]() ### AI overview This tutorial explains how Databricks supports SQL MATCH_RECOGNIZE for detecting ordered event patterns. It shows how the clause can simplify sequence detection, including identifying repeated login failures followed by a successful login, compared with complex SQL queries and window functions. ### Source excerpt Imagine you work in cybersecurity and you have a table that tracks login attempts... ## Native Splunk brings real-time insights to Cisco Nexus One DevFeed: [Native Splunk brings real-time insights to Cisco Nexus One]() Original publisher: [Read original article]() Author: David Keith Published: 2026-09-16T15:00:59Z Content type: release Language: en Sources: [Cisco Blogs]() Topics: [Nexus Dashboard](), [observability](), [telemetry](), [datacenter](), [Security](), [sensitive data](), [audit](), [configuration]() Tags: [analytics](), [audit](), [cisco](), [cisco-nexus-dashboard](), [configuration](), [data-center](), [data-center-networking](), [network](), [nexus-one](), [observability](), [real-time](), [sensitive-data](), [splunk](), [troubleshooting]() ### AI overview Cisco describes native Splunk embedded in Cisco Nexus Dashboard as an on-premises analytics and observability capability for data center and AI workloads. It processes telemetry locally, correlates network, security, configuration, and audit data, and provides dashboards, searches, and alerts for troubleshooting, data sovereignty, compliance, and cost efficiency. ### Source excerpt Discover how native Splunk embedded in Cisco Nexus Dashboard delivers real-time analytics, faster troubleshooting, and on-premises data sovereignty for modern data center and AI workloads. ## New AI technique could make minimally invasive surgeries safer and more precise DevFeed: [New AI technique could make minimally invasive surgeries safer and more precise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-16T15:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [3D](), [navigation]() Tags: [3d](), [ai](), [artificial-intelligence](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [computer-vision](), [electrical-engineering-and-computer-science-eecs](), [health-care](), [images](), [imaging](), [jameel-clinic](), [machine-learning](), [medical-devices](), [medical-imaging](), [minimally-invasive-surgery](), [mit-ibm-computing-research-lab](), [mit-schwarzman-college-of-computing](), [model](), [national-institutes-of-health-nih](), [navigation](), [paper](), [polina-golland](), [precision](), [real-time](), [research](), [school-of-engineering](), [vision](), [vivek-gopalakrishnan]() ### AI overview MIT researchers and collaborators developed xvr, an AI method that adapts to individual patients and rapidly aligns intraoperative X-rays with preoperative 3D medical scans. The technique is intended to improve surgical navigation for minimally invasive procedures. ### Source excerpt This patient-specific method, called xvr, helps doctors use X-rays for surgical navigation in fields such as orthopedics and neurosurgery. ## 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. ## CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding) DevFeed: [CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding)]() Original publisher: [Read original article]() Author: Debashis Das Published: 2026-09-16T00:00:55Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Artificial Intelligence](), [Embedded Systems](), [AI Inference](), [V](), [Arm](), [cpu](), [Open Source](), [Ethernet](), [Linux](), [Toit]() Tags: [ai](), [arm](), [artificial-intelligence-ai](), [camera](), [computer-vision](), [cpu](), [debug](), [edge-ai](), [embedded](), [ethernet](), [hardware](), [inference](), [kickstarter](), [linux](), [module](), [open-source](), [real-time](), [risc-v](), [robotics](), [rt-thread](), [soc](), [sophgo](), [tinyml](), [usb](), [video](), [vision]() ### AI overview The AIMORELOGY Ovis is an open-source modular AI vision camera built around the CVITEK CV1842H-P SoC. It provides full-color 1080p night vision, 1.5 TOPS edge AI inference, USB and Ethernet connectivity, and a dual-OS environment using Linux and RT-Thread. ### Source excerpt The AIMORELOGY Ovis is an open-source AI vision camera module built around the CVITEK CV1842H-P SoC, with full-color 1080p night vision and 1.5 TOPS edge AI inference in a compact modular design. It is designed for drones, robotics, security systems, smart cameras, and custom embedded vision products. The camera features a compact stacked design, with a 20 x 20 mm Core board that includes the CVITEK CV1842H-P SoC, 2 Gbit NAND flash, USB, and UART debug pads. A Sensor board with the SC235HAI image sensor connects on top and also adds Ethernet and UART interfaces. An optional CVBS board goes between the Sensor board and the Ovis Core board. AIMORELOGY Ovis specifications: Ovis Core Board SoC - CVITEK CV1842H-P CPU - 1x Arm Cortex-A53 core @ 1.1 GHz, 1x RISC-V C906 core @ 800 MHz NPU - 1.5 TOPS @ INT8 with BF16 support ISP - AI-ISP with real-time 1080p [...] The post CVITEK CV1842H-P-based edge AI camera module offers night vision and AI-ISP support (Crowdfunding) appeared first on CNX Software - Embedded Systems News. ## 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 ## From error mitigation to fault-tolerant quantum computing DevFeed: [From error mitigation to fault-tolerant quantum computing]() Original publisher: [Read original article]() Published: 2026-09-15T14:30:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [Hardware](), [ibm]() Tags: [compilation](), [deep-dive](), [errors](), [hardware](), [quantum](), [quantum-computing](), [quantum-error-correction-mitigation](), [quantum-software](), [real-time](), [reduce](), [resource]() ### AI overview The article describes a continuum from quantum error mitigation to quantum error correction as a path toward useful quantum computing. It reports emerging techniques with lower effective error rates, reduced sampling overhead, and lower resource requirements than conventional approaches. ### Source excerpt A spectrum of error-correcting techniques is enabling useful quantum computation, measured not by logical qubits but by the circuits you can run with them. ## 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 Named Strongest Overall Leader in 2026 Frost Radar™: Cloud Workload Protection Platforms DevFeed: [CrowdStrike Named Strongest Overall Leader in 2026 Frost Radar™: Cloud Workload Protection Platforms]() Original publisher: [Read original article]() Author: Brett Shaw Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [workload protection](), [Security & compliance, Cloud security](), [Security](), [telemetry](), [Containers](), [Kubernetes](), [SOC]() Tags: [2026](), [cloud](), [cloud-application-security](), [cloud-security](), [containers](), [growth](), [innovation](), [kubernetes](), [real-time](), [security](), [soc](), [telemetry](), [workload-protection]() ### AI overview CrowdStrike says Frost & Sullivan named it the strongest overall leader in the 2026 Frost Radar for Cloud Workload Protection Platforms. The article highlights Falcon Cloud Security's focus on connecting risk, adversary intelligence, and real-time protection across containers, Kubernetes, identities, cloud control planes, endpoints, and SOC operations. ### Source excerpt Falcon Cloud Security earned the highest scores in both Innovation and Growth by connecting risk, adversary intelligence, and real-time protection to stop attacks. ## 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. [Next page]()