# Streaming Streaming is the incremental delivery of data to a client in chunks as it becomes available, rather than waiting for the complete response. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Aniimo Launches on GeForce NOW Alongside a 007 First Light Update DevFeed: [Aniimo Launches on GeForce NOW Alongside a 007 First Light Update]() Original publisher: [Read original article]() Author: GeForce NOW Community Published: 2026-09-17T13:00:55Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [GeForce](), [Cloud](), [Streaming](), [Steam Deck](), [Firefox](), [Nvidia]() Tags: [cloud-gaming](), [devices](), [firefox](), [gaming](), [geforce-now](), [launches](), [steam-deck](), [stream](), [super-resolution](), [update]() ### AI overview NVIDIA highlights Aniimo's launch on GeForce NOW, support for streaming across devices including Steam Deck and Firefox, and updates and promotions for 007 First Light. The article also mentions other new titles joining the cloud service. ### Source excerpt A new creature-catching adventure is ready to stream from the cloud this week. Pawprint Studio's Aniimo arrives on GeForce NOW at launch, inviting gamers to explore the vibrant continent of Idyll across supported devices. Also this week, 007 First Light receives a path-tracing update on GeForce NOW, alongside a smashing limited-time Deluxe Edition sale on [...] ## Steam Frame Review: Capable VR Headset for Power Users Who Will Tinker DevFeed: [Steam Frame Review: Capable VR Headset for Power Users Who Will Tinker]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-16T22:52:10Z Content type: article Language: en Sources: [Road to VR]() Topics: [pc](), [Streaming](), [Users]() Tags: [feature](), [pc-vr-news-reviews](), [performance](), [review]() ### AI overview A review of Valve's Steam Frame VR headset finds that it combines strong standalone capabilities with easy Steam game streaming. Its flexibility and performance appeal to power users, but demanding standalone games may be difficult to run and the headset is less user-friendly than Quest 3. ### Source excerpt Steam Frame is finally here. It's Valve's second-ever VR headset, and a radically different product than its first. Did Valve pull off the vision? And, if so, who is Steam Frame for? Read on in our full review to find out. Table of Contents Steam Frame Review Summary Steam Frame is a unique headset that [...] The post Steam Frame Review - Power-user's Playground 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... ## 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 ## 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. ## 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. ## 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 ## 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. ## 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. ## Fovii Showcases Immersive Videos And Their Creators On Apple Vision Pro DevFeed: [Fovii Showcases Immersive Videos And Their Creators On Apple Vision Pro]() Original publisher: [Read original article]() Author: Laura Mingail Published: 2026-09-08T14:00:00Z Content type: article Language: en Sources: [UploadVR]() Topics: [App](), [Streaming](), [ui]() Tags: [apple](), [business](), [creators](), [immersive-video](), [launch](), [subscriptions](), [ui](), [video](), [video-streaming](), [vision]() ### AI overview The Fovii app is available on Apple Vision Pro as a platform for on-demand and live-streamed 180-degree immersive videos. It also highlights the creators, studios, and production work behind each project, with additional materials such as behind-the-scenes videos and spatial photos. The app is free to download, with monthly and annual subscriptions, individual rentals, and a limited launch selection. ### Source excerpt The Fovii app on Apple Vision Pro offers a new platform to experience 180-degree immersive videos and discover the creators behind them. ## Video Streaming Technology: Protocols, Codecs, CDNs, and 2026 Trends DevFeed: [Video Streaming Technology: Protocols, Codecs, CDNs, and 2026 Trends]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-09-08T01:00:42Z Content type: tutorial Language: en Sources: [DaCast]() Topics: [Streaming](), [5G](), [Artificial Intelligence](), [Low Latency](), [real-time](), [WebRTC](), [Virtual reality]() Tags: [access-control](), [ai](), [cdn](), [encryption](), [guide](), [latency](), [low-latency](), [streaming](), [technology](), [the-video-experts-blog](), [video](), [video-streaming]() ### AI overview This guide explains how video streaming works, covering data encoding, delivery through content delivery networks, decoding by video players, streaming protocols, codecs, and security. It also discusses AI-driven tools, low-latency streaming, 5G, edge computing, virtual reality, and business applications in 2026. ### Source excerpt By Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 Video streaming technology has revolutionized the way businesses and individuals share and consume content. Whether it's live streaming a product launch, hosting virtual events, delivering training sessions, or providing entertainment through on-demand video services, streaming technology is now an [...] The post The Definitive Guide to Video Streaming Technology in 2026 appeared first on Dacast. ## Integrate Deepgram Flux with Twilio's Conversation Relay DevFeed: [Integrate Deepgram Flux with Twilio's Conversation Relay]() Original publisher: [Read original article]() Author: Dhruv Patel Published: 2026-09-08T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [flux](), [asr](), [Tutorial](), [Latency](), [Node.js](), [WebSocket](), [Streaming]() Tags: [developer-insights](), [flux](), [latency](), [node](), [speech](), [streaming](), [transcription](), [tutorial](), [websocket]() ### AI overview This tutorial shows how to integrate Deepgram Flux with Twilio's Conversation Relay in Node.js. Flux combines speech transcription and turn detection, with configurable end-of-turn confidence, and is described as reducing response latency and false interruptions. ### Source excerpt Integrate Deepgram Flux with Twilio ConversationRelay in Node.js for faster turn detectio, and tunable end-of-turn control. ## 🍔🧠 How OpenAI Built GPT-Live for Low-Latency Voice AI DevFeed: [🍔🧠 How OpenAI Built GPT-Live for Low-Latency Voice AI]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-09-07T15:31:33Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Conversational AI](), [Low Latency](), [voice ai](), [OpenAI](), [real-time](), [Streaming](), [Remote Procedure Call (RPC)](), [Go]() Tags: [go](), [low-latency](), [openai](), [real-time](), [rpc](), [software-engineering](), [streaming](), [voice-ai]() ### AI overview The article explains how OpenAI built GPT-Live as a low-latency, full-duplex voice system. It describes a fast audio path that listens and speaks simultaneously, asynchronous delegation of deeper reasoning and backend work, stateful handoffs, optimized protocol boundaries, and a Go-based media frontend. ### Source excerpt PLUS: Zero-knowledge proofs ⚡, Design.md agent automation 👨💻, System Design Docs 101 📚 ## Video Hosting in China: What You Need to Know in 2026 DevFeed: [Video Hosting in China: What You Need to Know in 2026]() Original publisher: [Read original article]() Author: Jon Whitehead Published: 2026-09-07T04:45:44Z Content type: tutorial Language: en Sources: [DaCast]() Topics: [hosting](), [Streaming](), [Internet](), [data](), [networking]() Tags: [2026](), [china](), [compliance](), [data](), [hosting](), [infrastructure](), [live-streaming](), [performance](), [the-video-experts-blog]() ### AI overview This guide explains the requirements for reliable, compliant video hosting in mainland China in 2026. It covers China-approved delivery paths, China-optimized routing and CDN infrastructure, playback performance planning, and compliance and data-handling considerations. ### Source excerpt Dacast Editorial Team | Reviewed by Jon Whitehead, COO at Dacast | Updated September 2026 Over the past decade, China has become the world's largest internet market, and video is the default way people learn, shop, and engage with brands. By June 2025, China had 1.123 billion internet users, representing a penetration rate of 79.7% [...] The post Video Hosting in China: What You Need to Know in 2026 appeared first on Dacast. ## Strengthening Camera Support in Zephyr for Advanced Vision Applications DevFeed: [Strengthening Camera Support in Zephyr for Advanced Vision Applications]() Original publisher: [Read original article]() Author: Zephyr Project Published: 2026-09-04T21:12:17Z Content type: article Language: en Sources: [Zephyr Project]() Topics: [Embedded Systems](), [Embedded Software Dev](), [Open Source](), [Framework](), [Artificial Intelligence](), [object-detection](), [real-time](), [Streaming](), [Robotics]() Tags: [ai](), [blog](), [cameras](), [development](), [embedded](), [embedded-systems](), [events](), [india](), [industry-conference](), [object-detection](), [open-source](), [open-source-summit](), [real-time](), [robotics](), [streaming](), [vision](), [zephyr](), [zero-copy]() ### AI overview This event recap examines proposed changes to Zephyr's camera and driver architecture for AI-driven vision workloads. The proposals include attaching metadata and inference results to individual video buffers and adding per-buffer callbacks to improve buffer ownership, reduce CPU wakeups, and support more manageable camera pipelines. The changes remain under exploration through prototypes and community discussions. ### Source excerpt The Zephyr community came together at Open Source Summit India 2026 in Mumbai to share knowledge and explore developments, tooling, and real-world applications across embedded systems. In this second post event blog, we recap two lightning talks from the Zephyr track focused on camera support. [Next page]()