Mimir Query Engine: Streaming Queries for Speed and Efficiency

Kinetica v7.2 release boasts expanded support for graph querying through PGQ/GQL, and native Kafka integration for high availability. It's has a distributed graph query engine for streaming analytics, GPU-accelerated library of geospatial functions and is a lot of fun to use. https://www.kinetica.com/ #Graphs #GraphDatabase

In the next version of #libmodulor, we are getting streaming support in LLMManager and use case server output. Available on the #express and #hono server targets. Here is a demo of a Mistral AI response displayed the "standard" way vs the "stream" way. Although streaming is heavily used for videos and LLM responses, it can be applied to a lot of other use cases, especially in professional applications. Instead of letting the user wait for something to process, start sending the data as soon as it starts being ready. This way, they can start working on it instead of waiting for 5-6s for the whole thing to complete.

🚀 Unlock the Power of Multi-Channel HD Streaming with MYIR’s RK3576 SOM In modern intelligent vision applications, handling multi-camera data efficiently and with ultra-low latency is critical. With our MYC-LR3576 System-on-Module, MYIR has successfully achieved: ✅ 12-channel 1080p H.264 encoding ✅ Low-latency RTSP streaming (~140ms end-to-end) ✅ Optimized performance for AI-powered security, industrial vision, and vehicle surround-view systems This breakthrough delivers the efficiency, scalability, and real-time response needed for next-generation vision solutions. 👉 Read article here: https://lnkd.in/eQB7nVih

🛠️ Hive Control, your safety net for live streaming, is now enhanced with Automated Quality Level Filtering (AutoQLF) to deliver smarter, more reliable performance at scale. With AutoQLF, Hive Control automatically adjusts video quality when specific thresholds are reached. This helps teams maintain a stable, high-quality viewing experience across every event without needing to step in manually. It’s the latest addition to Hive Control, designed to make enterprise video delivery more resilient, adaptive, and efficient. 🎥 In this video, Nick Morolda explains how AutoQLF works and how it fits into your enterprise video delivery setup. 👉 Learn more about Hive Control: https://lnkd.in/dhFbGHRj

YOLOv8-Based Odometer Detection System Developed a custom YOLOv8 model to detect analog and digital odometers in videos with high accuracy and real-time performance. The system also supports post-processing with OCR for automatic mileage extraction. Key Highlights: Trained on 13 classes including odometer with mAP50 = 0.99 for odometers Real-time video inference with memory-efficient streaming Supports automatic extraction of odometer readings via OCR Designed for accuracy, scalability, and practical deployment 📂 Check out the code & try it: https://lnkd.in/gFhynGVM

Our first step after launching Flux at yesterday’s VapiCon? Immediately putting it to the test. Flux is our new conversational speech recognition model, purpose-built for voice agent development. We had Coval benchmark Flux and quantify what it can do: ✅ Super low latency to first token (50% lower latency than Nova-3) ✅ Faster, more accurate turn detection ✅ Seamless streaming, validated by Coval benchmarks 👉 Check the comments for links to our reaction blog post, demo, and the full Scott–Brooke conversation.

FastHTML dashboard graphs are working. Streaming IMU data from VESC through to ZMQ and then to a SSE (Server Side Events) to a Plotly graph. Next up is to add some sliders and vision stream to tune parameters without coding... If you watch till the end you will see the start of the mapping using isaac-ros-common and nvblox. All this is running on the Jetson Orin 8gb! I was worried in the beginning all this wouldn't fit. #nvidia #jetson #edgeai

India adds 50 million internet users every year. That's essentially equivalent to adding an entire South Korea to the digital world every year. Think about this: - A single IPL match pulls more concurrent streams than the Super Bowl. Now imagine what happens when that audience doubles in 2 years. Your infrastructure isn't just supporting growth, it's deciding who survives it. India already has a billion users online. The next billion won't just browse, they'll stream, game, and demand real-time everything. And that's where Techno Digital high-density colocation isn't just a service, it's the foundation for scale without compromise. The infrastructure you choose today determines the market you own tomorrow. Are you building for now, or for what's coming? #DigitalIndia #Infrastructure #Streaming #CloudInfrastructure #Scale

Everyone’s streaming. Few can scale. The next billion viewers won’t wait for buffering wheels, they’ll go where the stream never blinks. That’s where Techno Digital’s high-density colocation comes in: • 40kW+ per rack for GPU-intensive workloads • Advanced cooling technology for sustained performance • <10 ms latency for real-time experiences • N+1 uptime for nonstop reliability Discover what powers the world’s most resilient streams: https://lnkd.in/dUa7ebsN

😊 We’re releasing StreamDiffusionV2 for the live-stream community—from individual creators with one GPU to enterprise platforms with many. StreamDiffusionV2 is our follow-up to StreamDiffusion. StreamDiffusion powered many real products, but temporal consistency still bugged us. We’re fixing that for the community: 👇 🤔Efficiency ≠ live-readiness: many “real-time” video diffusion models are fast, but live streaming demands per-frame deadlines with low jitter. StreamDiffusionV2 balances temporal stability with strict per-frame deadlines, achieves fast interaction, and is ready for your AI Youtuber Stream Live 👇 📖Project page: https://lnkd.in/gCbTHayX 💻Code: https://lnkd.in/gJjBiRuC