Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

🚀Introducing Voice + Chat AI Agents on WhatsApp with Sarvam Samvaad Now create rich customer interactions within one seamless thread that includes voice calling + real-time chat. This is powered by: ▪️11 Indian languages across text, voice and widgets ▪️Context-aware, multimodal and hyper-personalized AI Agents ▪️Guided flows and catalogues...

33,347 Aufrufe • vor 11 Monaten •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,149 Aufrufe • vor 1 Jahr

MESSIER | In Short For those new here, welcome to #M87! Here is a quick overview to get you informed. We are developing a complete #utility ecosystem centered around our #DAO. Our ecosystem offers AI-powered solutions and a wide range of decentralized products and services. Our goal is to generate as much revenue as possible to support new investments, staking rewards, and buybacks and burns that benefit our community holders. Our ecosystem includes: ▪️ DAO investment and governance ▪️ Profit-distributing utility NFTs ▪️ Community-driven PFP NFTs ▪️ NFT bidding platform ▪️ Staking platform ▪️ Buyback and burn mechanism ▪️ P2P Token Swap Exchange ▪️ Eliminating token tax and slippage ▪️ P2P vesting token contracts ▪️ P2P escrow to buy and sell RWAs ▪️ Multi-platform & multi-chain dApps ▪️ zk-SNARK anonymization service ▪️ GPU Lend and Rent Marketplace ▪️ AI-powered solutions ▪️ Token and dApp revenue streams ▪️ Real-life crypto payment app ▪️ Non-custodial wallet ▪️ Payment request solution ▪️ Cross-chain swaps ▪️ Anonymous swaps ▪️ Listed in the Android & Apple store ▪️ Crypto debit cards ▪️ Popular investment talk show on 𝕏 ▪️ Active YouTube channel ▪️ Fully packed roadmap ▪️ 3.5 years of growth and development ▪️ Experienced development team ▪️ Company registration in the UAE ▪️ Doxxed team ▪️ 250+ strategic partnerships ▪️ A 10-year liquidity lock ▪️ Real and growing utility income streams ▪️ Deflationary token supply ▪️ 7 audits with leading auditors ▪️ 6 CEX listings, more to come 📃 Learn more: 📊 DexTools:

MESSIER | M87

28,473 Aufrufe • vor 5 Monaten

Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by dsa (Co-founder & CEO of LiveKit), Shayne (Developer Advocate, LiveKit), and Nedelina Teneva (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from , a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here:

Andrew Ng

87,711 Aufrufe • vor 1 Jahr