Daytona 解决了智能体大规模并发运行时的基础设施瓶颈，做 AI 智能体开发、RL 训练或大规模评估的团队可以直接用这个平台替代传统云方案，值得关注。
Daytona 发布专为 AI 智能体设计的计算平台，提供 60 毫秒启动的沙箱环境，能在 75 秒内创建 5 万个独立实例，日均运行 85 万次。该平台从人类开发环境转向智能体沙箱，支持裸金属、有状态快照，并针对强化学习（RL）和评估（evals）工作负载优化。Daytona 认为 Kubernetes 在智能体规模下失效，AI 云应更像 Stripe 而非 AWS。CEO Ivan Burazin 解释了为何智能体需要可组合计算机，以及 RL 工作负载占比从 0% 增长到约 50% 的趋势。
🆕Daytona’s Agent-Native Compute: 60ms sandboxes, 50K startups in 75 sec, 850K daily runs, RL/evals,...
🆕Daytona’s Agent-Native Compute: 60ms sandboxes, 50K startups in 75 sec, 850K daily runs, RL/evals, CLI > MCP, & the end of localhost latent.space/p/daytona e @daytonaio CEO @ivanburazin explains why AI agents need composable computers, how Daytona pivoted from human dev environments to agent sandboxes, why bare metal and stateful snapshots matter, how RL workloads went from 0% to ~50% of usage, why Kubernetes breaks down at agent scale, and why the AI cloud may look more like Stripe than AWS. Your browser does not support the video tag. 🔗 View on Twitter 💬 2 🔄 1 ❤️ 18 👀 3370 📊 6 ⚡