# Streaming Published articles for Streaming. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses DevFeed: [Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-16T23:40:00Z Content type: news Language: en Sources: [Road to VR]() Topics: [Artificial Intelligence](), [AI Models](), [iOS](), [iphone](), [Cloud](), [Security](), [Streaming]() Tags: [ai](), [ai-models](), [cloud](), [ios](), [iphone](), [on-device](), [security](), [streaming](), [xr-industry-news]() ### AI overview Snap announced Specs Intelligence, an anticipatory AI service for its upcoming consumer Specs AR glasses. The service is designed to work across Specs, iPhone, and Mac, using connected apps and tools to build context and surface relevant information. Snap also announced AR experiences, streaming features, Spotify integration, and partnerships including HBO Max, the NBA, and the WNBA. ### Source excerpt Snap today announced new experiences, services and partnerships for SPECS, the company's upcoming pair of consumer AR glasses. Snap's big Specs livestream today wasn't technically a launch event--they're still slated to arrive in the US, UK and France later this fall starting at $2,195--although the company did give a little more insight into what sort [...] The post Snap Announces New "anticipatory" AI Service & Apps for First Consumer 'Specs' AR Glasses appeared first on Road to VR. ## 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 ## 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... ## Kubernetes Changed Block Tracking API - Beta Differences DevFeed: [Kubernetes Changed Block Tracking API - Beta Differences]() Original publisher: [Read original article]() Author: Prasad Ghangal Published: 2026-09-14T18:30:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [API](), [gRPC]() Tags: [2026](), [compatibility](), [container-image-registry](), [developer](), [documentation](), [kubernetes](), [registry](), [release](), [snapshots](), [storage](), [streaming](), [update]() ### AI overview This Kubernetes developer article explains the Beta changes to Changed Block Tracking (CBT) support for CSI drivers. The SnapshotMetadataService CRD moved from v1alpha1 to v1beta1, with no automatic conversion, and CBT remains limited to block volumes. It also outlines compatibility requirements and the steps for upgrading and trying the feature. ### Source excerpt Changed Block Tracking (CBT) support for CSI drivers shipped as Alpha in September 2025. With the March 2026 v1.0.0 release of the external-snapshot-metadata project, the feature moved to Beta. If you aren't yet familiar with changed block tracking for storage in Kubernetes, the Alpha announcement covers the motivation, the three primary components (the CSI SnapshotMetadata gRPC service, the SnapshotMetadataService CRD, and the external-snapshot-metadata sidecar), and a walkthrough of how to use the API. CBT currently applies to block volumes; file-volume and network file-share changed-list tracking is not covered by this feature. This post focuses on what is different in Beta. What's new in Beta The main change in that release was the promotion of the SnapshotMetadataService CRD from v1alpha1 to v1beta1. The CRD used to advertise a driver's metadata service now serves cbt.storage.k8s.io/v1beta1. The schema itself is unchanged, but this release removed v1alpha1 (rather than serving it alongside the new version). If you are upgrading from Alpha, you need to: Re-apply the CRD definition shipped with v1.0.0. Update SnapshotMetadataService manifests to use apiVersion: cbt.storage.k8s.io/v1beta1. Update any client or controller code that talks to the CRD. This is a one-time change. There is no automatic conversion between the two versions. Compatibility Minimum Kubernetes version: 1.33 CSI spec: 1.10 or newer Container image: registry.k8s.io/sig-storage/csi-snapshot-metadata:v1.0.0 Trying it out The Getting Started section in the Alpha blog still applies. In short: Make sure your CSI driver supports volume snapshots and ships the external-snapshot-metadata sidecar. Install the SnapshotMetadataService CRD (the v1beta1 definition from the v1.0.0 release). Create a SnapshotMetadataService resource for your driver. Use a client -- snapshot-metadata-lister, or your own implementation -- to call GetMetadataAllocated and GetMetadataDelta. If you want to see the full flow end-to-e ## Steam Frame vs. Quest 3 Specs: Better PC Streaming, Power & Hackability DevFeed: [Steam Frame vs. Quest 3 Specs: Better PC Streaming, Power & Hackability]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-14T16:59:00Z Content type: comparison Language: en Sources: [Road to VR]() Topics: [Streaming](), [pc](), [Linux](), [Steam Deck](), [Windows](), [Operating system](), [Arm]() Tags: [arm](), [cameras](), [compatibility](), [devices](), [hacking](), [linux](), [meta-quest-3-news-reviews](), [news](), [os](), [pc](), [pc-vr-news-reviews](), [pcie](), [steam-deck](), [streaming](), [windows](), [x86]() ### AI overview This comparison examines Valve's Steam Frame standalone headset against Quest 3. Steam Frame is designed to run Steam's flatscreen and PC VR libraries, stream PC VR games wirelessly through a dedicated Wi-Fi 6E dongle, and support native x86 Windows and Linux content through a compatibility layer. Compared with Quest 3, it offers a more powerful Qualcomm Snapdragon processor, a more open and hackable SteamOS platform, an accessible gen4 PCIe expansion port, and broader compatibility, although its application compatibility is not yet as established. ### Source excerpt Want to know how Valve's Steam Frame standalone headset stacks up against the competition? Read on to find out. Steam Frame is a lot like Steam Deck; it can play pretty much all of Steam's game library out of the box, which includes flatscreen content based on x86 architecture--basically everything on Steam--and also now PC [...] The post Steam Frame vs. Quest 3 Specs: Better PC Streaming, Power & Hackability appeared first on Road to VR. ## You Don't Need a VR-Ready PC (or a PC at all) to Use Steam Frame DevFeed: [You Don't Need a VR-Ready PC (or a PC at all) to Use Steam Frame]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-14T16:59:00Z Content type: opinion Language: en Sources: [Road to VR]() Topics: [pc](), [Linux](), [Streaming]() Tags: [bluetooth](), [games](), [linux](), [pc](), [pc-vr-news-reviews](), [storage](), [streaming](), [wi-fi](), [xr-industry-news]() ### AI overview The article argues that Steam Frame can function as a standalone VR and gaming device, without requiring a VR-ready PC or any PC at all. It provides a self-contained setup, Steam interface, Linux desktop, access to flat and VR games, Bluetooth peripheral support, and expandable storage. ### Source excerpt Valve essentially considers Steam Frame a 'streaming-first' VR device, capable of directly connecting to your PC via a Wi-Fi 6E dongle so you can play high-quality VR and flatscreen games. But don't let the RAMpocalypse get you down: you don't even need a PC, VR-ready or otherwise. Before I ever got my hands on Frame, [...] The post You Don't Need a VR-Ready PC (or a PC at all) to Use Steam Frame appeared first on Road to VR. ## 🍔🧠 Pinterest's Fix for the Hardest Problem in ML Infra DevFeed: [🍔🧠 Pinterest's Fix for the Hardest Problem in ML Infra]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-09-14T15:31:30Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [recommendation systems](), [Feature Engineering](), [Inference](), [Streaming](), [configuration](), [Framework](), [data](), [Low Latency]() Tags: [batch](), [configuration](), [data](), [feature-engineering](), [low-latency](), [ml](), [pinterest](), [ranking](), [recommendation-systems](), [streaming]() ### AI overview Pinterest redesigned its user-sequence platform for ranking, retrieval, and recommendation systems by defining signals once and instantiating them consistently across streaming, batch, and serving workloads. The approach uses Python configuration with validated schemas, a shared execution engine, cooperating streaming and batch paths, and columnar time-partitioned storage to improve freshness, completeness, consistency, and operational efficiency. ### Source excerpt PLUS: OpenAI agents beat math 🧮, Test techniques for agents ⚡, Postgres survival guide 📖 ## Which Video Streaming Provider Is Best for Corporate Training? DevFeed: [Which Video Streaming Provider Is Best for Corporate Training?]() Original publisher: [Read original article]() Author: Max Wilbert Published: 2026-09-14T12:40:20Z Content type: comparison Language: en Sources: [DaCast]() Topics: [Streaming](), [communications]() Tags: [compliance](), [corporate](), [cost](), [distributed](), [live-streaming](), [streaming](), [the-video-experts-blog](), [training]() ### AI overview This guide compares Brightcove, Kaltura, Panopto, and Dacast as video streaming providers for corporate training. It focuses on LMS compatibility, compliance recording retention, and cost at scale, and summarizes the providers' stated features and pricing. ### Source excerpt By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 Training a distributed workforce is one of the hardest logistics problems corporate L&D teams face, and live streaming video has become one of the most effective ways to solve it. Choosing the right video streaming provider for training is [...] The post Which Video Streaming Provider Is Best for Corporate Training? appeared first on Dacast. ## On-Device AI Series (Part 5): LiteRT-LM DevFeed: [On-Device AI Series (Part 5): LiteRT-LM]() Original publisher: [Read original article]() Author: Oğuzhan Aslan Published: 2026-09-14T05:59:12Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [LiteRT](), [On-device AI](), [Large Language Model](), [Inference](), [Streaming](), [GPU](), [LLM evaluation / benchmarking](), [PyTorch]() Tags: [ai](), [android](), [android-development](), [benchmarking](), [gpu](), [inference](), [litert](), [llama](), [llm](), [multimodal](), [on-device](), [on-device-ai](), [programming](), [pytorch](), [qwen](), [software-development](), [streaming]() ### AI overview This tutorial explains LiteRT-LM for running large language models on-device. It covers the Engine/Session API, streaming output, system prompts, tool calling, multimodal inputs, thinking mode, and CPU-versus-GPU benchmarking. The article also discusses tradeoffs involving privacy, network independence, latency, memory, sampling configuration, and model capability compared with cloud APIs. ### Source excerpt Put your phone in airplane mode. Open the app, type a question, and watch the answer arrive one token at a time -- no spinner waiting on a network round-trip, no API key, no per-token bill, and nothing you typed ever leaving the device. LiteRT-LM removes the genuinely hard parts of running an LLM on-device -- KV-cache management, token streaming, backend selection -- but it doesn't remove your job so much as relocate it. What's left on your plate is a short, specific list: sizing a combined input+output token budget, owning your own sampling defaults, hand-building system prompts and tool calling out of raw text, and one native-library collision that presents as a SIGSEGV rather than a build error. Know those going in and the API itself is a clean three-step pattern. We'll get there in that order: Why you'd choose this runtime and what it costs you versus the cloud. The Engine/Session model you need to read the code at all. Real implementation samples -- streaming, system prompts and tool calling, multimodal inputs, thinking mode, and CPU-vs-GPU benchmarking. The anti-patterns to avoid. A developer-friendliness rating on the same rubric as Parts 1-4. Why Use LiteRT-LM? You reach for LiteRT-LM instead of hand-rolling generation on top of raw LiteRT when: You need multi-turn conversation, not single-shot inference -- session state and KV-cache bookkeeping are handled for you, and resetting a conversation is a session swap, not a model reload. You need streaming output -- token-by-token delivery for a responsive chat UI, instead of a blocking call that returns everything at once. You're choosing between CPU and GPU per device -- the explicit backend parameter turns that into a runtime decision instead of a build-time guess. You want a pre-converted model without doing your own PyTorch-to-LiteRT conversion work -- the Model Zoo covers Gemma, Qwen, Llama, and more out of the box. You're willing to own sampling -- the engine won't pick sane decoding defaults for you; that's on the ## Data Engineering Weekly #287 DevFeed: [Data Engineering Weekly #287]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-14T02:52:23Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [Kafka](), [data observability](), [Embeddings](), [Multi-tenancy](), [Event-Streaming](), [OpenAI](), [Library](), [Code]() Tags: [architecture](), [architectures](), [data-engineering](), [data-quality](), [embedding](), [engineering](), [kafka](), [multi-tenancy](), [object-storage](), [observability](), [openai](), [streaming](), [vector]() ### AI overview Data Engineering Weekly #287 covers building data platforms from scratch, including composable architectures, data quality, and observability. It also previews talks on governed machine-executable ontologies for marketing activation and fair, order-preserving Kafka consumption for many tenants. The issue links to OpenAI's storage platform scaling for ChatGPT and Pinterest's embedding retrieval platform. ### Source excerpt The Weekly Data Engineering Newsletter ## The Grafana AI SDK for Go: a shared foundation for building AI applications DevFeed: [The Grafana AI SDK for Go: a shared foundation for building AI applications]() Original publisher: [Read original article]() Author: Luccas Quadros Published: 2026-09-12T11:22:06.456390Z Content type: article Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [vercel ai sdk](), [SDKs](), [Go Language](), [AI Development](), [Large Language Model](), [agent observability](), [Model Context Protocol](), [React]() Tags: [agent-observability](), [ai](), [analytics](), [backend](), [building](), [go](), [grafana](), [logs](), [metrics](), [monitoring](), [observability](), [sdk](), [streaming](), [tools](), [traces]() ### AI overview Grafana Labs introduces the Grafana AI SDK for Go, an open-source shared foundation for building AI applications. The SDK standardizes model calls, streaming, tool execution, structured output, multi-step agents, workflow controls, and operational features such as retries, logging, metrics, and Agent Observability. It also supports streaming Go backends to Vercel AI SDK frontend hooks. ### Source excerpt Starting an experiment with an LLM has never been easier. Keeping a growing collection of those experiments consistent is another matter. Earlier this year, as more teams began exploring AI features here at Grafana Labs, we repeatedly encountered the same pattern: a new experiment would start, move quickly, and build its own client for whichever model provider it needed. The next experiment would do the same, with a slightly different abstraction for streaming, tools, errors, or provider configuration. This was understandable, given the circumstances. Model providers were changing quickly, our teams were learning quickly, and coding agents made it possible to turn an idea into a working integration faster than ever. But that speed also made it easier for every integration to develop its own architecture. Eventually, we were maintaining a collection of solutions to what was essentially the same problem. And since most of our backend is written in Go, we built the Grafana AI SDK for Go to give our teams a shared foundation to work from. It provides common interfaces for calling models, streaming responses, executing tools, producing structured output, and running multi-step agents. It also speaks the protocol used by Vercel AI SDK frontend hooks, so a Go backend can stream directly to useChat, useCompletion, and useObject. We built it because we needed it inside Grafana Labs, but we open sourced it last month (alongside a broader collection of tools we released for building, operating, and understanding AI systems during our first Grafana Labs AI Week) because we think other teams building AI applications in Go are likely to encounter many of the same problems. We would like to build the next part together, so in this blog I'll tell you a bit more about the project, including how you can put it to use today, as well as how you can help us improve it. What teams can build with it today The SDK supports both simple model calls and larger application workflows: Generate ## 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. ## How to Build an AI Chat App Interface With the Vercel AI SDK and Shadcn/ui DevFeed: [How to Build an AI Chat App Interface With the Vercel AI SDK and Shadcn/ui]() Original publisher: [Read original article]() Author: Vaibhav Gupta Published: 2026-09-11T16:21:29Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [vercel ai sdk](), [AI Chat](), [SDKs](), [Node.js]() Tags: [ai](), [app](), [how-to](), [llm](), [mcp](), [next-js](), [node](), [openai](), [react](), [shadcn-ai-chat-app](), [shadcn-ui](), [streaming](), [tailwind](), [typescript](), [ui](), [vercel](), [vercel-ai-sdk]() ### AI overview Tutorial for building a streaming AI chat interface in Next.js with the Vercel AI SDK and shadcn/ui. ### Source excerpt Every other AI product you open today has the same screen: a message list, a text box at the bottom, and words that stream in one token at a time. It looks simple, but it's not simple to build well. Y ## The 15 Best Chinese Live Streaming Platforms (ICP Licensed and Native) for 2026 DevFeed: [The 15 Best Chinese Live Streaming Platforms (ICP Licensed and Native) for 2026]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-09-11T07:30:31Z Content type: comparison Language: en Sources: [DaCast]() Topics: [Streaming](), [Firewall](), [Sports]() Tags: [2026](), [china](), [comparison](), [e-commerce](), [firewall](), [gaming](), [live-streaming](), [platform](), [selection](), [streaming](), [the-video-experts-blog]() ### AI overview This comparison reviews 15 live-streaming platforms for reaching audiences in China, covering international ICP-licensed services and native Chinese platforms. It discusses market scale, platform selection, the Great Firewall, and legal and regulatory considerations. ### Source excerpt Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 Live streaming in China isn't a niche channel, it's one of the most competitive, fastest-moving live-streaming markets in the world, and which platform you choose to reach it matters just as much as the decision to enter it in the [...] The post The 15 Best Chinese Live Streaming Platforms (ICP Licensed and Native) for 2026 appeared first on Dacast. ## Building an Agentic SOC on a Stream DevFeed: [Building an Agentic SOC on a Stream]() Original publisher: [Read original article]() Author: Pavel Lineitsev Published: 2026-09-10T19:19:05Z Content type: article Language: en Sources: [Confluent: Data in motion]() Topics: [Agentic SOC](), [SOC](), [Security](), [Streaming](), [Architecture & Design](), [Artificial Intelligence](), [Automation](), [data loss prevention]() Tags: [agent](), [agentic](), [agentic-soc](), [agents](), [ai](), [ai-agents](), [architecture](), [automation](), [confluent-cloud](), [data-loss-prevention](), [security](), [security-tools](), [soc](), [streaming]() ### AI overview This article describes an Agentic SOC that uses AI agents and a continuous streaming architecture to investigate security alerts. Its pipeline combines central triage, specialized evidence agents, adversarial evaluation, and a self-learning knowledge base to analyze every alert, escalate higher-value cases, and surface true positives for analyst review. The approach is intended to address the backlog of low-priority alerts, including Data Loss Prevention alerts, whose volume makes manual investigation impractical. ### Source excerpt Discover how our security team built an automated multi-agent investigation pipeline that scaled alert triage throughput using a continuous streaming architecture. ## Building resilient real-time streaming workers with Amazon DynamoDB leases DevFeed: [Building resilient real-time streaming workers with Amazon DynamoDB leases]() Original publisher: [Read original article]() Author: Siddhesh Tiwari Published: 2026-09-10T16:14:22Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [WebSocket](), [Amazon EC2](), [Amazon Elastic Kubernetes Service]() Tags: [advanced-300](), [amazon-dynamodb](), [amazon-ec2](), [amazon-eks](), [amazon-elastic-container-service](), [aws-fargate](), [how-to](), [real-time](), [streaming](), [technical-how-to](), [workers]() ### AI overview A tutorial for building resilient real-time WebSocket workers with Amazon DynamoDB leases. It covers conditional-write ownership, orphan reconciliation for automatic failover, and graceful shutdown to reduce deployment downtime on Amazon ECS and AWS Fargate. ### Source excerpt Real-time streaming workers that hold hundreds of persistent WebSocket connections lose data when a worker fails. Learn how to build a WebSocket fleet management system on Amazon ECS and AWS Fargate that uses Amazon DynamoDB conditional writes as a distributed lease to track ownership, fail over automatically, and deploy with low downtime. ## 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 ## 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. ## The 13 Best Video Training Platforms for Corporate L&D in 2026 DevFeed: [The 13 Best Video Training Platforms for Corporate L&D in 2026]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-09-10T00:01:56Z Content type: comparison Language: en Sources: [DaCast]() Topics: [hosting](), [Learning](), [Streaming](), [Monitoring]() Tags: [2026](), [article](), [comparison](), [corporate](), [hosting](), [learning](), [platforms](), [streaming](), [the-video-experts-blog](), [training]() ### AI overview A comparison of 13 video training platforms for corporate learning and development teams in 2026. It evaluates capabilities such as LMS integration, completion tracking, compliance recording, reporting, and pricing, identifying Dacast, Kaltura, and Panopto as having the clearest corporate training fit among the listed platforms. ### Source excerpt By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 For corporate L&D teams, the best video training platforms have become core infrastructure rather than a nice-to-have. They provide a reliable and interactive learning experience for onboarding, compliance, and enablement content. Leveraging educational video hosting and video-on-demand (VOD) capabilities, [...] The post The 13 Best Video Training Platforms for Corporate L&D in 2026 appeared first on Dacast. ## NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC DevFeed: [NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC]() Original publisher: [Read original article]() Author: NVIDIA Writers Published: 2026-09-09T16:00:42Z Content type: release Language: en Sources: [NVIDIA Blog]() Topics: [AI Development](), [GPU](), [SDKs](), [Microservice]() Tags: [ai](), [artificial-intelligence](), [compliance](), [events](), [gpu](), [holoscan-for-media](), [image-to-video](), [media](), [media-and-entertainment](), [microservices](), [news](), [nvidia](), [nvidia-nim](), [pro-graphics](), [real-time](), [sdks](), [streaming](), [synthetic](), [text-to-video](), [video]() ### AI overview NVIDIA announced an expansion of NVIDIA AI for Media at IBC 2026, including GPU-accelerated SDKs and NIM microservices for media workflows. The article highlights Synthetic Video Detector integrations for assessing whether video footage may be AI-generated and for compliance review. ### Source excerpt At the IBC conference, running Sept. 11-14 in Amsterdam, the creative, technology and business communities are coming together to turn ideas into action and discuss innovations across the media and entertainment industries. More than 44,000 attendees from 170+ countries are gathering to explore 1,300+ exhibitions in 14+ halls and outdoor spaces, with over 600 speakers [...] ## How to Choose a Corporate Video Streaming Solution DevFeed: [How to Choose a Corporate Video Streaming Solution]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-09-09T15:46:02Z Content type: tutorial Language: en Sources: [DaCast]() Topics: [Streaming](), [hosting](), [API](), [Website]() Tags: [2026](), [api](), [enterprise](), [hosting](), [how-to](), [streaming](), [the-video-experts-blog]() ### AI overview This tutorial presents a five-step process for choosing a corporate video streaming solution: assess needs, research platforms, verify required features, set a budget, and select a platform for integration. It highlights CDN reliability, chapter markers, captions, APIs, support, branding, player themes, and scheduled-stream countdowns, and advises accounting for bandwidth overage fees. ### Source excerpt By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 The global video streaming market was valued at $129.26 billion in 2024 and is projected to grow at a 21.5% compound annual rate through 2030, according to Grand View Research. Live streaming and interactive video make up a growing [...] The post How to Choose a Corporate Video Streaming Solution appeared first on Dacast. ## ByteDance Reportedly Developing an AI Model for Real-Time Spatial Video DevFeed: [ByteDance Reportedly Developing an AI Model for Real-Time Spatial Video]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-09T10:44:59Z Content type: news Language: en Sources: [Road to VR]() Topics: [Artificial Intelligence](), [AI Models](), [real-time](), [Google](), [Cloud](), [Streaming](), [Hardware]() Tags: [ai](), [ai-models](), [cloud](), [hardware](), [model](), [real-time](), [streaming](), [xr-industry-news]() ### AI overview ByteDance is reportedly developing an AI model for generating real-time spatial videos, based on Seedance and intended to connect the company's AI models, cloud resources, content platforms, and Pico Interactive hardware business. The report compares the model with Google's Genie, but its launch timing remains uncertain. ### Source excerpt TikTok parent ByteDance is reportedly preparing an AI model that could let you create real-time spatial videos similar to Google Genie. According to a Bloomberg report, ByteDance is readying an AI model dedicated to creating real-time spatial videos, which the company hopes will act as a "flywheel" to its various companies, including video streaming giant [...] The post TikTok Parent Reportedly Building AI Model to Rival Google's Genie for Real-Time Spatial Video appeared first on Road to VR. [Next page]()