How to talk to statues — Joe Reeve, ElevenLabs
TL;DR
Joe Reeve from ElevenLabs discusses building a viral AI app that lets users talk to statues via phone calls, exploring how vibe coding with existing APIs enables rapid prototyping, the unique challenges of voice interface design, and the cultural implications of giving physical objects AI-generated voices.
🏛️ The Viral Statue App 3 insights
Two-hour vibe coding prototype
Joe Reeve built the app in Cursor during a single Sunday, combining OpenAI vision/research, ElevenLabs voice design API, and agents to let users photograph statues and receive contextual phone calls from historically-voiced characters within 30 seconds.
Unexpected enterprise demand
The demo accumulated 1.5 million impressions and attracted inquiries from major museums, auction houses (Bonhams, Christie's), and tourism platforms seeking commercial deployments.
Scalable architecture via APIs
The application relies entirely on managed APIs for heavy lifting, meaning technical scaling requires minimal engineering effort compared to traditional infrastructure.
🎙️ Voice Interface Design 3 insights
Multimodal conversation requirements
Effective voice agents require concurrent visual interfaces displaying extracted conversation data, moving beyond binary voice-only interactions to show what the agent is thinking.
The interruption permission problem
Users hesitate to interrupt speaking agents due to politeness, indicating voice UIs must explicitly design for and encourage aggressive interruption to improve conversational flow.
Indirect interaction patterns
Complex workflows benefit from intermediary 'product manager' agents that translate human speech into tool-specific actions rather than forcing direct voice control of coding agents.
⚡ Culture & Physical AI 3 insights
Embedded experiential technology
Future museum installations will hide microphones and speakers directly within statues and historical phone booths (like the K6 booth featuring Sir Michael Caine's voice) to create seamless physical interactions without screens.
Philosophical voice casting
Voice design should reflect an object's material provenance and history, such as a statue carved in Vietnam from Chinese stone speaking with blended accents reflecting both origins plus its British Museum context.
Democratization of creation
Non-coders are already building functional applications without understanding technical jargon like 'hamburger menus,' suggesting an impending 'Instagram filters moment' for consumer app generation.
Bottom Line
Technical barriers to sophisticated AI applications have collapsed—success now depends on storytelling, curatorial content design, and choosing the right combination of scalable APIs rather than solving hard engineering problems.
More from AI Engineer
View all
Think You Can Build a Game with AI? Think Again! - Danielle An & David Hoe, Meta
Meta engineers Danielle An and David Hoe argue that while AI has democratized basic game creation, true differentiation requires human taste, cohesive aesthetics powered by key art anchoring, and innovative runtime LLMs that enable unscripted, dynamically personalized gameplay experiences previously impossible in traditional development.
Beyond the Harness: A Journey Towards Adaptative Engineering - Rajiv Chandegra, Annicha Labs
Rajiv Chandegra introduces 'adaptive engineering,' a paradigm shift from fixed AI harnesses (like Cursor or Claude Code) to dynamic, self-organizing systems that emerge during runtime, enabling AI to handle complex, real-world messes beyond deterministic software environments.
What if the harness mattered more than the model? - Aditya Bhargava, Etsy
Aditya Bhargava argues that sophisticated agent harnesses can compensate for weaker open-source models, enabling local AI to match proprietary performance while reducing vendor dependency.
Frontier results, on device - RL Nabors, Arize
Rachel Lee Neighbors introduces a framework for replacing expensive cloud-based frontier models with Small Language Models (SLMs) running on-device, demonstrating how a systematic 'prototype big, deploy small' approach using evaluation tools like Phoenix can cut inference costs to zero while maintaining 90% accuracy and enabling offline functionality.