Stream

Build a real-time voice agent with Gemini 3.1 Flash Live and Stream's Vision Agents SDK using Stefan Blos’s walkthrough to move from early access to a fully orchestrated multi-step workflow. What’s covered: ✨ Setting up the Vision Agents SDK with the Gemini plugin ✨ Defining tools for image generation and product search ✨ Building a video processor to analyze live frames via Next.js and WebSockets Grab Gemini API keys at Google AI Studio and explore the Vision Agents SDK from Stream to start building. Watch the full video: https://goo.gle/4m4GpgH

Update on Cognivise: Building the English AI Coach A short time ago, I was dealing with personal issues and depression that completely blocked me from writing code. I had stopped developing entirely. But thankfully, being introduced to the Stream(Vision_AI_SDK(https://lnkd.in/gwdp7yjz)) by Kunal Kushwaha during the WeMakeDevs hackathon reignited my spark. I am finally building again. My ultimate goal remains the same: to create the most accurate real-time cognitive monitoring system for students and learners. Today, I’m excited to share my latest major addition to Cognivise. 🗣 New Feature: The English AI Coach I wanted to see if I could build a conversational AI that doesn't just listen to your pronunciation, but literally watches your face as you speak. Here is what the English Coach is doing under the hood: 🔹 Dual-AI Architecture: Combining local browser processing (MediaPipe FaceMesh) for 30fps physical tracking, synced with a cloud LLM (Gemini/Groq) for speech parsing and reasoning. 🔹 Canvas Layer Multiplexing: Built 4 distinct visual feedback modes (Composite, Landmarks-only, Full HUD Analysis, Raw Frame) by manipulating HTML5 canvas layers behind the live WebRTC stream. 🔹 Real-time Synchronicity: Overcame strict browser autoplay policies and synchronous call-stack limits to ensure the AI's Text-To-Speech (TTS) responds naturally to the user's analyzed audio. ___________ 🛠 What Was Actually Hard This Time Getting the Vision SDK to accurately track full movement is incredible. But translating that raw physical movement—every micro-expression and blink—into an accurate emotional state is intensely difficult. Right now, the system catches the physical layout perfectly, but the semantic "emotion tracking" is still not exactly where I expect it to be. Sending frame-by-frame snapshot analysis over WebSockets without colliding with the synchronous audio pipelines required deep debugging of React state refs and Canvas requestAnimationFrame loops. Testing this in a remote location with an unstable internet connection makes debugging real-time video+WebSocket systems extremely painful. I am learning exactly why tracking every single frame matters, and how quickly it scales in complexity. _________________________________________________________________ ⚠️ Honest Status & What's Next Cognivise is not perfect yet. Capturing true "emotion" is a work in progress. But getting out of my slump was step one. I’m heading back to my hometown in Delhi soon, and I now have 3 brand new AI project ideas I plan to start sprinting on this month. ----------------------------------------------------------------------------- Massive thank you to Kunal Kushwaha and the WeMakeDevs community for creating environments that push developers back into motion. The journey continues! 🔗 GitHub: https://lnkd.in/gZvJnCfa #AI #VisionAgents #VisionPossible #MachineLearning #WebRTC #FastAPI #ReactJS #BuildInPublic #OpenSource #algsoch #MentalHealthInTech