标签：#Streaming

The upcoming feature expands its Transparency Tags for marking slop songs.

The adaptation of Sega's classic games hits Paramount+ on September 16.

The Crunchyroll anime streaming service has started appearing on the Appning app store.

In order to beef up its ability to accurately measure viewership data in the streaming era, Nielsen is moving forward with a plan to use more information gathered from its partners' wearable devices. Today, Nielsen announced "a number of key enhancements" that are being added to its data capturing process ahead of the upcoming fall […]

Live video looks simple until you build it. Then you discover that "low latency" means five different things, that your CDN and your latency target are fighting each other, and that the box which handled ten viewers falls over at ten thousand for reasons nobody warned you about. This is the guide I wish existed when I started. No vendor talk, just how the pieces fit. 1. Ingest and delivery are separate decisions The single most common mistake is treating "streaming protocol" as one choice. It is two. Ingest is getting video from a camera, encoder or browser into your server. Delivery is getting it from your server to viewers. They have different constraints and you almost never use the same protocol for both. A typical stack ingests over RTMP or SRT and delivers over HLS. Another ingests WebRTC and delivers WebRTC. Mixing is normal and expected. Once you separate them, most of the confusion disappears. 2. The ingest protocols RTMP is old, TCP-based, and still everywhere. Every encoder speaks it, OBS defaults to it, and it just works. Latency is typically 2 to 5 seconds. Classic RTMP is limited to H.264 and AAC, though the Enhanced RTMP spec has added HEVC and AV1. Being TCP, it degrades badly on lossy networks: packet loss becomes head-of-line blocking, and your stream stalls instead of gracefully dropping quality. SRT is the answer to that. UDP-based with its own retransmission layer (ARQ), a configurable latency buffer, and built-in AES encryption. It is designed for pushing broadcast-quality video across the public internet, which is exactly where RTMP struggles. If your source is on a flaky connection, a 4G link, or a different continent, SRT is usually the right call. # Publishing over SRT with ffmpeg ffmpeg -re -i input.mp4 -c copy -f mpegts \ "srt://your-server:4200?streamid=live/stream1" RTSP is what IP cameras speak. If you are pulling from surveillance hardware, you are pulling RTSP whether you like it or not. WHIP (WebRTC-HTTP Ingestion Protocol) is the n

Jason Statham's upcoming film Mutiny is set to hit theaters on August 21st, but it can already be streamed in full on the US version of Amazon's Prime Video service. My colleague Dominic Preston was able to verify that all 1 hour and 35 minutes of the movie is available to watch on Amazon's streaming […]

Spotify's Running Mode is now available on Android.

Peacock's quarterly profitability isn't guaranteed.

Peacock is raising subscription prices for the fourth time in as many years.

Peacock is raising prices across its streaming plans once again, with the company's cheapest ad-supported Select tier going from $7.99 to $8.99 / month, as reported earlier by Variety. The Premium plan with ads is increasing from $10.99 to $12.99 / month, while the ad-free Premium Plus plan is getting the biggest hike, jumping from […]

The typewriter effect looks simple: characters appear one by one. But behind "skip animation", "no truncation", and "no performance regression" lies a whole set of engineering decisions. The implementation in this article is Flutter/Dart based, but the core semantic decisions — "skip ≠ abort" and "buffer and batch" — are framework-agnostic : Web's EventSource, and native/RN SSE clients, face the same choices. Prologue: a "skip typewriter" button that kept breaking In an AI narrative app (where the user influences an AI-driven interactive story by entering fate instructions), I built a "⏩ skip typewriter" button — users click it to see the full AI reply immediately instead of waiting for the text to appear character by character. The button went through three stages in the dev log: V1 : clicking does nothing — the callback fires, but the user experiences no change V2 : clicking truncates the content — the animation is gone, but the reply is incomplete too Final : clicking reveals the partial text immediately, while the LLM keeps generating the full reply in the background, which appears all at once when done Behind these three versions lie the three most common pitfalls in "streaming UI". This article breaks them down. 1. From SSE to screen: the streaming rendering pipeline Why the LLM "pops" text out The LLM's reply comes back in chunks via HTTP SSE (Server-Sent Events). A typical chunk looks like this: data: { "choices" :[{ "delta" :{ "content" : "Mephistopheles appears" }}]} data: { "choices" :[{ "delta" :{ "content" : "at the study door." }}]} data: [ DONE ] The interval between chunks is determined by the model's generation speed — tens of milliseconds when fast, possibly a full second when slow. That "character-by-character appearance" is what the user perceives as the typewriter animation. Why you can't update the UI on every chunk If you trigger a state update on every chunk, a reply of a few hundred characters can cause dozens or hundreds of UI rebuilds, whi

Pets, like children, come with the responsibility to keep them happy, healthy, and mentally stimulated - obligations I readily committed to before getting my cat, Trevor. I work from home, so he's never lonely, and play with him using a mountain of toys at every opportunity outside of my working hours. But younger cats can […]

ABC News has officially introduced Searched, a livestreamed show that highlights stories people are discussing on social media and searching on the web, as reported earlier by Variety. Though the network just announced the show now, Searched began airing on the ABC News Live streaming channel in April, which appears on both Disney Plus and […]

The platform will start tracking views the same way across video formats.

If you're after the best picture quality, you'll always want to go with a disc.

YouTube will soon count a view as soon as a video starts to play, lining up with the system used by Instagram, TikTok, and its Shorts videos. The update will go into effect on August 24th, "which means creators will likely see their total view counts increase faster moving forward," the platform says. Instagram and […]

It's only available with the top-tier Premium subscription plan, for one.

The annual Disney fan event showed off the cast of Marvel’s X-Men movie, plus a new trailer for Avengers: Doomsday, and our first look at the VisionQuest TV show for Disney Plus. For Star Wars fans, there was a teaser trailer for season two of Ahsoka, plus a special look at Star Wars: Starfighter with […]

Hi, friends! Welcome to Installer No. 140, your guide to the best and Verge-iest stuff in the world. (If you're new here, welcome, don't forget to hydrate, and also you can read all the old editions at the Installer homepage.) This week, I've been reading about Will Ferrell and Fabrizio Romano and Heather Cox Richardson, […]

When you build a system that detects key moments in a live sports feed, one architectural decision shapes everything downstream: where does the inference happen? At the edge, close to where the video is produced, or in the cloud, after the stream has been ingested? There is no universally right answer, but the trade-offs are sharp and worth laying out. The case for the edge Running detection near the source, at the venue or in an on-prem encoder, minimizes the round trip. The video does not have to travel to a data center and back before a moment is flagged, which can shave critical seconds off the time to clip. For genuinely live use cases, where a clip is worthless if it lands a minute late, that latency saving is the whole game. Edge inference also reduces egress: do the heavy frame analysis locally and ship only the clips that matter, instead of streaming everything to the cloud. The cost of the edge Edge hardware is constrained. You run on whatever GPU or accelerator fits in the rack at the venue, not on an elastic fleet. That bounds model size and concurrency. Updating models across many distributed edge nodes is an ops problem in itself, and a venue that hosts one event a week is idle hardware the rest of the time. Edge is fast but inflexible. The case for the cloud The cloud gives you elastic compute, easy model updates, and the ability to run larger or ensemble models you could never fit at the edge. If you process many concurrent streams, centralizing inference pools capacity instead of overprovisioning every venue. For workflows where a few seconds of extra latency is acceptable, near-live rather than instant, the cloud is simpler to operate and cheaper to scale. The cost of the cloud You pay for it in latency and bandwidth. Every frame you want to analyze has to be ingested first, and for high-bitrate broadcast feeds that adds up. The end-to-end path, capture, encode, transport, ingest, infer, clip, deliver, has more hops, each adding delay and a potenti