Docker Compose

AI Agents Need Computers: 74% MoM Growth, 850K/Day Runs, & New Agent Cloud — Ivan Burazin, Daytona

May 21, 2026 · 1:11:40

In this episode, Ivan Burazin, CEO of Daytona, explains why AI agents need composable computers rather than disposable code execution boxes, and how Daytona pivoted from human dev environments to building a bare-metal sandbox platform that spins up one sandbox in 60ms and 50,000 in 75 seconds. Daytona's biggest customer runs 850,000 sandboxes daily, and RL/eval workloads have surged from 0% to 50% of usage. The platform competes against managed Kubernetes (EKS/GKS) by offering stateful snapshots, dynamic resizing, and a scheduler purpose-built for agent workloads. Burazin also discusses the company's 74% month-over-month growth, the addition of Windows sandboxes, and the licensing challenges of macOS. He argues the future AI cloud will look more like Stripe than AWS, and that agents will need every kind of computer—including legacy Windows environments—to automate knowledge work.

A Brief History of the Open Source AI Hacker - with Ben Firshman of Replicate

Feb 28, 2024 · 1:21:09

Ben Firshman, CEO of Replicate, explains how the inference platform grew from a research reproducibility tool into a 2M-user API business by embracing the generative image community and treating open-source AI as a hacker-friendly ecosystem. They accidentally discovered their API when a user reverse-engineered their web form, leading to their first $1k/month customer. Cog, their container standard for ML models, was born from lessons at Docker and the need to make models tinkerable. Ben argues that fine-tuning's low cost makes open-source models sustainable, and that AI engineers (orders of magnitude more than ML engineers) just need to start playing with models. He also discusses GPU scarcity, preferring sustainable pricing over price wars, and reveals that demand is not outpacing supply thanks to aggregating demand.