Mixpanel

Quadratic: The AI Spreadsheet

Jun 10, 2025 · 28:15

David Kircos, co-founder and CEO of Quadratic, joins hosts Alessio and Swyx to demonstrate Quadratic, an AI-powered spreadsheet that natively executes Python, JavaScript, and database queries in WebAssembly for real-time performance. The product's AI assistant uses a context-aware agent loop that summarizes sheet structure rather than dumping full data, then tool-calls to read columns or peek at values only when needed, keeping costs low on large datasets. Kircos explains Quadratic's three-year technical groundwork—WebGL rendering and WebAssembly runtimes—enabled them to capitalize on AI, turning a 'technical spreadsheet' into one where users simply drop a CSV and say 'help me analyze this.' He details how they route chart images back to the model for visual feedback and predicts that formulas will yield to Python for complex analysis, though spreadsheets will remain the best interface for human–AI data collaboration. Quadratic is source-available on GitHub, supports real-time multiplayer, and has grown to nine people. The episode also covers future directions like proactive agents and form controls (sliders) for interactive models.

Tiny Teams: $6m ARR, 5m users with 4 employees — Sid Bendre, Oleve (Quizard AI/Unstuck AI)

Apr 23, 2025 · 42:59

Sid Bendre of Oleve (formerly Quizzer AI and Unstuck AI) explains how his four-person team built $6M ARR and 5M users across multiple consumer apps. Starting with Quizzer AI in January 2023, a viral TikTok gained 10K users in 30 hours; by August 2024, Unstuck AI hit 1M users in nine weeks. The company has been profitable since its first nine months. Bendre describes their platform engineering strategy: product engineers own individual apps while a platform team builds reusable systems, including internal shadow-org agents for growth and marketing. He shares technical hacks like using LaunchDarkly feature flags to load-balance across Azure OpenAI endpoints and a de-indexer for Azure AI Search to manage costs. The key engineering principle is deterministic LLM workflows—classifying user intent then routing to pre-built deterministic flows—rather than open-ended agent tool use.