MySQL

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.

Fullstack-Bench: The Eval for Coding Agents — with Sujay Jayakar, Chief Scientist, Convex

Mar 19, 2025 · 31:13

Sujay Jayakar, Convex chief scientist, presents Fullstack-Bench, a benchmark evaluating how well AI coding agents build full-stack apps on Convex, Supabase, and FastAPI+Redis, finding Convex’s tight feedback loops and strong abstractions let agents autonomously complete tasks within time limits. The benchmark tests three tasks (chat, to-do, files) across three backends with 30-60 minute limits and human hints. Convex succeeded autonomously on chat and to-do tasks, while Supabase agents struggled with RLS policy debugging and FastAPI agents with manual SSE wiring. Sujay discusses how platform design impacts AI code generation: simple procedural code, strong abstractions, and guardrails like end-to-end type safety improve LLM performance. He notes that API choices matter—things too similar to Firebase but not exact cause hallucinations—and that models like Claude 3.7 sometimes regress on platform-specific evals. The episode concludes with Convex’s focus on enabling AI-generated backends and anticipation of new agent workflow patterns.

Agents @ Work: Lindy.ai (with live demo!)

Nov 15, 2024 · 1:08:01

Florent Crivello, founder of Lindy.ai, explains how his no-code agent platform evolved from an overly LLM-dependent design to a deterministic 'on rails' system that puts AI in a box, making agents more reliable and easier to use. The product was rebuilt from scratch as Lindy 2.0, featuring step-by-step workflows with structured outputs and memory management that still requires pruning. Crivello reveals that Claude 3.5 Sonnet ended model bottlenecks but warns against over-engineering cognitive architectures, advocating instead for letting models scale. He discusses building their own eval tools (now considering Braintrust), hiring vertical GMs for go-to-market, and the decision to return to in-person work, arguing remote work hampers creativity. He also shares his contrarian views on Europe vs. US tech, testing the Overton window with spicy tweets, and balancing AI safety concerns (10% p(doom)) with building toward a potential utopia.