9. Building Video AI Applications Learn to use DeepStream for annotating video streams. 🔗 learn.nvidia.com/courses/cou…

8 free NVIDIA courses worth more than most paid certifications No payment. No waitlist. No excuse. Here are the 8 worth your time: 1. Generative AI Explained How it works, where it applies, and what the real limits are. The foundation before everything else. lnkd.in/gBb3peXi 2. AI for All: From Basics to GenAI Practice The full arc from machine learning to generative AI. Real applications across healthcare and autonomous vehicles. lnkd.in/gXmmnC4G 3. Getting Started with AI on Jetson Nano You collect real image data, annotate it, train a neural network, create your own model. Hands-on from the first session. lnkd.in/gnmrhBJm 4. Building a Brain in 10 Minutes How neural networks actually learn. The math behind a single neuron. Shorter than most meetings and more useful than most of them. lnkd.in/gCaA-XKp 5. Building Video AI Applications on Jetson Nano DeepStream pipelines. Multiple video streams. YOLO inference. For practitioners building production-grade video AI systems. lnkd.in/gNffgw5C 6. Building RAG Agents with LLMs Scalable deployment. Microservices. LangChain for dialog management. RAG is the architecture that makes AI reliable at scale. The most important course on this list for enterprise AI work. lnkd.in/gcK2ZJ4a 7. Accelerate Data Science Workflows with Zero Code Changes GPU-accelerated data processing and machine learning. Faster output. Same codebase. No rewrites. lnkd.in/gF7eVk2V 8. Introduction to AI in the Data Center GPU architecture. Deep learning frameworks. Multi-system cluster planning. If you build or advise on enterprise AI infrastructure, this is not optional. lnkd.in/gKTS6uMS Here is what separates this list from every other free AI resource. Most free AI training teaches you how to use the tools sitting on top. This teaches you what sits underneath them. Every AI model you use runs on NVIDIA hardware. ChatGPT. Claude. Gemini. All of it. The professionals who understand both layers are not interchangeable. They are the ones organizations call when the demo works but the system doesn't. They are the ones who know why it's failing before anyone else does. That knowledge does not come from prompting faster. It comes from understanding the infrastructure the prompts run on. That's what NVIDIA is giving away for free. Use it. 💾 Save this list before you need it. ♻️ Repost to give someone in your network a real technical edge in AI.

NVIDIA/skills Official NVIDIA-verified agent skills that enhance AI coding assistants with trusted guidance for CUDA-X, NeMo, DeepStream, distributed training, and GPU-accelerated development workflows. ⭐ Stars: ~1.8k 🧾 License: Apache-2.0 & CC BY 4.0 🔔 Discover Open Source.

Najpierw niech te setki tysięcy agentów nauczą się odpowiadać na pytania o błędy w CUDA, TensorRT i DeepStream lepiej niż forum z 2021 roku. Dopiero potem pogadamy o internecie 100 miliardów agentów.

*2026 starter pack for building an edge AI product: Trained model ✓ Jetson dev kit ✓ DeepStream installed ✓ First camera stream working ✓ Now add 7 more cameras. Need someone who understands GStreamer plugin architecture. Need someone who knows GPU memory management. Need someone who has debugged NvBufSurface memory leaks at 2am. Need someone who can explain batched-push-timeout to a non-technical PM without crying. ₹50-70 LPA. 3-month onboarding. Already has 4 other offers. Everything in the edge AI stack democratized except the perception pipeline layer. The model? Solved. The hardware? Solved. The pipeline between them? Still 2015. PipeGen by @craftifai .

GStreamer DeepStream perception pipeline tip: If your pipeline freezes intermittently with 5 RTSP sources, check your batched-push-timeout in streammux config. Default is often too low for high-stream counts. The muxer gives up waiting for slow sources and ships partial batches. (1/3)

8 free NVIDIA courses worth more than most paid certifications No payment. No waitlist. No excuse. Here are the 8 worth your time: 1. Generative AI Explained How it works, where it applies, and what the real limits are. The foundation before everything else. lnkd.in/gBb3peXi 2. AI for All: From Basics to GenAI Practice The full arc from machine learning to generative AI. Real applications across healthcare and autonomous vehicles. lnkd.in/gXmmnC4G 3. Getting Started with AI on Jetson Nano You collect real image data, annotate it, train a neural network, create your own model. Hands-on from the first session. lnkd.in/gnmrhBJm 4. Building a Brain in 10 Minutes How neural networks actually learn. The math behind a single neuron. Shorter than most meetings and more useful than most of them. lnkd.in/gCaA-XKp 5. Building Video AI Applications on Jetson Nano DeepStream pipelines. Multiple video streams. YOLO inference. For practitioners building production-grade video AI systems. lnkd.in/gNffgw5C 6. Building RAG Agents with LLMs Scalable deployment. Microservices. LangChain for dialog management. RAG is the architecture that makes AI reliable at scale. The most important course on this list for enterprise AI work. lnkd.in/gcK2ZJ4a 7. Accelerate Data Science Workflows with Zero Code Changes GPU-accelerated data processing and machine learning. Faster output. Same codebase. No rewrites. lnkd.in/gF7eVk2V 8. Introduction to AI in the Data Center GPU architecture. Deep learning frameworks. Multi-system cluster planning. If you build or advise on enterprise AI infrastructure, this is not optional. lnkd.in/gKTS6uMS Here is what separates this list from every other free AI resource. Most free AI training teaches you how to use the tools sitting on top. This teaches you what sits underneath them. Every AI model you use runs on NVIDIA hardware. ChatGPT. Claude. Gemini. All of it. The professionals who understand both layers are not interchangeable. They are the ones organizations call when the demo works but the system doesn't. They are the ones who know why it's failing before anyone else does. That knowledge does not come from prompting faster. It comes from understanding the infrastructure the prompts run on. That's what NVIDIA is giving away for free. Use it. 💾 Save this list before you need it. ♻️ Repost to give someone in your network a real technical edge in AI.

Using AI agents just for normal coding is using them to their full extend. The real money is chaining them into complex workflows, but the bottleneck is always trust. If you run random instructions from the internet, you expose your system to trigger abuse and mismatched behaviors. NVIDIA bypassed this completely. NVIDIA has an under-the-radar GitHub repo that acts as a trusted skill layer. It is a catalog of official, verified AI agent skills. Instead of rolling the dice on third-party junk, you plug production-ready NVIDIA expertise directly into your agents. RAG, DeepStream video analytics, Omniverse and Physical AI workflows, ASR, TTS you name it. This repo will teach your coding agent how to use NVIDIA tools efficiently. You can install the entire catalog or just the specific skill you need to execute a highly specialized task without the security risks. They already built the infrastructure for you. Stop playing small and start delegating the real work. github.com/NVIDIA/skills

🚀Your First Physical AI Agent Dev Kit The reComputer J401 Dev Kit with @NVIDIARobotics #Jetson Orin Super gives you the hardware open-source tutorials to start building on the edge. 🟢 For Learning: J401 Nano Bundle (#OrinNano 8GB, 67 TOPS): seeedstudio.com/reComputer-J… 🧠For Performance: Super J401 NX Bundle (#OrinNX 16GB, 157 TOPS): seeedstudio.com/reComputer-S… 📘Includes step-by-step guides on #Linux, #YOLO, #DeepStream, Local #LLMs/#VLMs, and more: github.com/Seeed-Projects/re…

375edge turns highways and street corners into structured data. NVIDIA Jetson DeepStream run computer vision on-device, distilling terabytes of raw video into megabytes of insight daily. No round trip to the cloud or personally identifiable information. Just the physical world, made machine-readable in real time.

🔹A real-time object detection system is more than an AI model. It is a complete embedded video pipeline: Camera → GStreamer → DeepStream → TensorRT → Deployment RidgeRun helps teams optimize Jetson-based AI pipelines for FPS, latency, and production readiness. Contact us! ridgerun.com/contact #EdgeAI #GStreamer #DeepStream #TensorRT #NVIDIAJetson