ElevenLabs

How to talk to statues — Joe Reeve, ElevenLabs

Jun 1, 2026 · 33:28

Joe Reeve, an ElevenLabs growth engineer, built an app that lets users talk to statues by pointing their phone at one—it identifies the statue via OpenAI deep research, generates a matching voice with ElevenLabs' voice design API, and starts an ElevenLabs agent conversation—all in 30 seconds. He created it in two hours on a Sunday using Cursor and a single prompt, posted it on Tuesday, got 50,000 impressions, then reposted about vibe coding and hit 1.5 million. Museums, auction houses (Bonhams, Christie's), and travel platforms reached out; one CEO tracked down his WhatsApp, saying a team of 10 had worked on a similar project for a year. The episode explores voice interaction patterns, the challenge of interrupting agents, the need for multimodal interfaces (voice plus visual), and the potential of vibe coding to democratize software creation, with Joe noting that music and captions are key to viral videos.

Reverse engineering a Viking VOIP phone protocol with Claude Code — Boris Starkov, Eleven Labs

May 29, 2026 · 20:11

Boris Starkov from ElevenLabs used Claude Code to reverse engineer the undocumented protocol of a Viking VOIP phone that three senior engineers and ChatGPT failed to configure in a year. Claude brute-forced all 676 possible two-letter command combinations, found 80 valid ones, then set up a TCP proxy between a Windows XP virtual machine and the phone to intercept traffic from the proprietary software. The critical missing piece—a one-byte checksum in the persistence command—was deduced by Claude by running known input-output pairs through the derived formula, confirming a simple subtraction. Starkov acted as Claude's hands, physically rebooting the phone and reporting beeps. The protocol is now open-sourced as a Claude Code skill, enabling direct programming without Windows software. The project culminated in a red phone booth at AI Engineer Europe where picking up the receiver connects to a Michael Caine voice agent quizzing attendees on British AI history.

Give Your Chat Agent a Voice — Luke Harries, Head of Growth, ElevenLabs

May 9, 2026 · 8:12

Luke Harries from ElevenLabs argues that the next upgrade for chat agents is a voice layer, not smarter prompts or RAG, and introduces the company's Voice Engine to wrap existing agents via a few lines of code. He demonstrates how text-in, text-out chat agents can be converted to voice agents using a single prompt, leveraging ElevenLabs' advanced turn-taking, emotion-aware interruption detection, and Scribe for speech-to-text. The Voice Engine provides server and client SDKs, plus Shadcn-based UI components, enabling omni-channel deployment like phone calls and Zoom. Harries also addresses tool calling, noting that the wrapper proxies calls to existing agent logic without rebuilding. He predicts that chat agents will either adopt voice or become obsolete.

Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs

May 4, 2026 · 1:21:26

Angelos Perivolaropoulos from ElevenLabs walks through building a small GPT-2-like LLM from scratch on a local machine, demonstrating that the core techniques used by major labs are accessible in a few hundred lines of PyTorch code. The workshop uses character-level tokenization (65 tokens) on a Shakespeare dataset to enable fast training with limited compute. The model architecture includes multi-head self-attention, MLP layers, residual connections, and layer normalization, totaling 10 million parameters across six transformer blocks with a 256-token context window. The training loop employs next-token prediction with a warm-up cosine decay learning rate schedule and validation loss to detect overfitting. Inference uses temperature sampling (default 0.7) and top-k sampling to improve creativity. Perivolaropoulos explains that audio and multimodal models share the same transformer foundation but differ in tokenization (e.g., mel-spectrograms for audio) and use specialized losses like L2 or KL divergence, while reasoning models result from post-training base models with high-quality chain-of-thought data.

[Full Workshop] Building Conversational AI Agents - Thor Schaeff, ElevenLabs

Jul 31, 2025 · 1:01:42

Thor Schaeff of ElevenLabs demonstrates how to build multilingual conversational AI agents using ElevenLabs' platform, which combines speech-to-text (ASR) with 99-language support, a voice library of over 5,000 voices, and language detection system tools that automatically switch between 31 languages (with plans to expand). The agent pipeline transcribes user speech, feeds it to any LLM (like GPT-4 or Gemini), and streams the response back as speech for low-latency conversations. Schaeff shows real-time language switching in Mandarin, Hindi, and English, and explains how to assign per-language voices (e.g., Chennai-accented Tamil). He addresses safety tooling—voice watermarking, live moderation, and consent verification—and discusses cost (per-minute pricing), latency mitigation via Flash models and RAG, and handling multi-language mixing within a single utterance, though accuracy degrades with more than two languages intermixed.

Storyteller: Building Multi-modal Apps with TS & ModelFusion - Lars Grammel, PhD

Jan 23, 2024 · 7:30

Lars Grammel presents Storyteller, a kid's story generator built with TypeScript and his ModelFusion library, which orchestrates multiple AI models to turn voice input into narrated audio stories with images. The app transcribes speech via OpenAI Whisper in 1.5 seconds, generates a story outline with GPT-3 TurboInstruct in 4 seconds, and then runs title, image, and audio generation in parallel. For consistency, it uses GPT-4 with a low temperature to produce structured story passages, and streams partial results so narration can begin before the full story is ready—addressing the 1.5-minute full generation time. Voices are selected by embedding speaker descriptions and retrieving matching ElevenLabs voices filtered by gender. Image prompts are extracted from the story via GPT-4 and rendered with Stability AI's Stable Diffusion XL. The result is a responsive, multi-modal app that lets users listen while the server continues processing.