Deepgram

Full Workshop: Realtime Voice AI — Mark Backman, Daily

Aug 3, 2025 · 1:09:41

In this hands-on workshop, Mark Backman and Aleś from Daily demonstrate how to build real-time voice AI agents using Pipecat, their open-source Python framework, and Google's Gemini Multimodal Live API. They explain that Pipecat's modular pipeline orchestrates audio transport, speech-to-text, LLM, and text-to-speech services, allowing developers to plug and play vendors like Deepgram, OpenAI, or Cartesia. The session highlights how speech-to-speech models like Gemini Live simplify architecture by handling transcription, LLM logic, and voice generation in one step, reducing latency to under 800 milliseconds. Key topics include the critical role of voice activity detection (VAD) for natural turn-taking, strategies for managing context windows to maintain accuracy, and the trade-offs between speed and reliability when using tool calls in real-time. Despite conference Wi-Fi issues, the team live-codes a functional bot and demonstrates interruption handling and the Word Wrangler game, showcasing Pipecat's production readiness with hundreds of thousands of daily calls.

Survive the AI Knife Fight: Building Products That Win — Brian Balfour, Reforge

Jul 14, 2025 · 14:10

Brian Balfour, CEO of Reforge, argues that winning in today's AI knife-fight requires answering 'What do I build and why will it win?' by focusing on proprietary data, unique functionality, and unmet customer needs rather than building custom AI. He illustrates with Granola, which entered a crowded AI note-taker market by understanding users wanted help taking better notes, not full automation, and assembled off-the-shelf AI (DeepGram, Anthropic, OpenAI) with unique data (user notes plus transcription) and functionality (Mac app, calendar integration) to create a competitive edge. Balfour warns competitive advantages now last only 2-3 weeks, so teams must sequence smaller moats continuously, each buying time to execute faster. The talk emphasizes treating AI as Lego blocks—assembling pre-trained models, data, and product superpowers into a system that spins a data flywheel.