First, big thanks to the ADMIN for building this community. Really appreciate the work behind it. Happy to be here, and happy to contribute where I can. Most of my work is with global clients, usually founders or companies building something with a real vision behind it. Not really looking for one-off tasks only. I prefer working with people who want to build, improve, launch, and keep growing a product over time. I’m based in Tokyo, Japan and work mainly across AI, full-stack development, SaaS, automation, mobile apps, and product development. I also have access to active development teams and individual engineers here in Japan. So depending on the project, can support either a specific technical area or a full development process. - Main areas I work in: • AI agents and multi-agent systems • LLM apps, RAG, AI search, knowledge systems • Business automation and internal AI tools • SaaS platforms • Web applications • Mobile apps • CRM and booking systems • Marketplace and matching platforms • Logistics and dispatch systems • GIS and location-based apps • AI customer support systems • Data processing and analytics • Recommendation systems • API integrations • Cloud infrastructure and scaling - Typical tech stack: Frontend React, Next.js, TypeScript, JavaScript, Tailwind Backend Python, FastAPI, Node.js, Express, Golang, REST, GraphQL, gRPC AI / ML PyTorch, TensorFlow, Hugging Face, LLMs, RAG, vector databases, AI agents, NLP, computer vision Mobile React Native, Flutter, Swift, Kotlin Database PostgreSQL, MySQL, MongoDB, Redis, Pinecone, FAISS, Elasticsearch Cloud / DevOps AWS, GCP, Azure, Docker, Kubernetes, CI/CD, GPU systems - Have worked across quite a few industries too. Healthcare, logistics, hospitality, education, finance, e-commerce, media, marketplaces, SaaS, customer support, field services, and more. - I can also help with: • Product planning • Technical architecture • MVP development • Production development • AI product strategy • Workflow design

Has anyone successfully integrated LiveKit with Asterisk?

🚨 We’re Hiring: AI Voice Agent Realism Consultant / Engineer We already have a working AI voice agent and need an expert to make it feel more natural and human in real conversations. We’re looking for hands-on experience with: • Turn-taking & interruptions • Latency & pacing • VAD / endpointing • Human-like TTS / expressivity • Handling unexpected responses 🎧 To apply, send: • A real voice-agent call recording • Your stack • What you personally improved • Your availability & rate 📧 [email protected] 📱 WhatsApp: +923460054979 📄 Job JD: https://lnkd.in/dREnSFfA Contract / Consulting · Remote

Hi skool fams! I am a software engineer. I'd love to connect with Founders, builders, CTO or PMs currently building unique ideas. Mainly use Python/Django, React/Nextjs, C#/.NET, Gofast and serveral Typescript and Python libs for developing cutting-edge AI production such as Voice AI, AI assistants, Automation and LLM projects. Also take full responsibility for enhancing visual appeal and marketing effectiveness through UI/UX, website design, and front-end improvements. Let’s develop useful projects that can actually sell well to companies or customers. Also, if you have such an opportunity, please bring me on board.

Hi Folks, I'm planning to self-host LiveKit Egress in Kubernetes and would like some guidance on infrastructure sizing and autoscaling. Our use case - Track Egress (not RoomComposite) - Supporting both 1:1 video calls and group video calls - Recording each participant's audio and video tracks separately - Maximum recording resolution: 720p (1280×720) - Maximum frame rate: 30 FPS - A separate merge service combines the recorded tracks into a final MP4 - Final recordings are uploaded to S3 - We are planning for hundreds of concurrent recordings Questions 1. Is there an official recommendation for the minimum and recommended CPU/RAM required per Egress instance for Track Egress at 720p/30 FPS? 2. Approximately how many concurrent Track Egress jobs can we expect from servers with: - 4 vCPU / 8 GB RAM - 8 vCPU / 16 GB RAM - 16 vCPU / 32 GB RAM - For 1:1 calls, if both participants have audio and video tracks, are there any specific resource considerations compared with recording a single track? - For group calls, where multiple participants' audio/video tracks are being recorded separately, how does the number of tracks per room affect Egress CPU, memory, and network usage? - For a maximum of 720p/30 FPS, what factors have the biggest impact on CPU and memory consumption for Track Egress? - Are there recommended Kubernetes CPU/memory requests and limits for Track Egress? - What is the recommended approach for autoscaling Egress workers in Kubernetes? Is CPU-based HPA sufficient, or is there a better metric/approach for determining when another Egress worker is required? - Since we expect hundreds of concurrent recordings, is there a recommended architecture where LiveKit Egress is hosted separately from the LiveKit Server and scaled independently? - Is there any managed/cloud platform or LiveKit-supported service available where Egress workers can be hosted separately with automatic scaling, rather than managing the Egress Kubernetes deployment ourselves? - Are there any production examples or recommendations for sizing a large-scale Track Egress deployment supporting both 1:1 and group calls?