Daytona provides a secure, scalable infrastructure for executing AI-generated code. Our platform delivers instant environment creation, complete isolation, and enterprise-grade security for AI workflows. With comprehensive APIs and native Git/LSP support, we enable developers to safely run AI-generated code while maintaining full programmatic control. Built for the future of AI development.
External link for Daytona
224 West 35th Street Ste 500
297
New York, New York 10001, US
🚀 Kicking off the Daytona HackSprint w/ Braintrust A full day of building, experimenting, and hacking has begun at the WorkOS office in San Francisco. Huge thanks to our partners Braintrust, ElevenLabs, Fireworks AI, WorkOS, CodeRabbit, and CopilotKit for helping make today's HackSprint possible. Let's build something awesome. 🔥
We had a great morning full of conversations with fellow GTM leaders and founders at our Daytona AI GTM Breakfast with ZeroClick at Corgi Cafe in San Francisco. ☕️ Thanks to everyone who joined us, we loved the discussions, the connections, and the energy. We'll definitely be hosting more gatherings like this soon. 🚀
How to run OSWorld on Daytona Windows Sandboxes. Full guide here: https://lnkd.in/eFh68MXy
🇬🇧 Our first Daytona AI Builders London, together with Encode Club! A great evening of talks and demos on AI engineering, agent infrastructure, observability, agentic frontends, and efficient AI models. Thanks to our speakers: 🎤 RL Nabors (Arize AI) 🎤 Sofía Sánchez-Zárate (CopilotKit) 🎤 Tyler Edwards (Overmind) 🎤 Mislav Ivanda (Daytona) Thanks to everyone who joined us yesterday. See you at the next AI Builders! 🚀
Production AI agents need both trusted compute and trusted browsers. Daytona & Anchor Browser deliver the complete stack. Read more in the article by Idan Raman ⚓️ on this link: https://lnkd.in/eS4mG2vb
How to get Microsoft Office in Daytona Windows Sandboxes. Full guide below here: https://lnkd.in/ehGh77fF
Thank you to everyone who joined us yesterday for Daytona AI Researchers at Stanford University. Together with FounderCoHo, we hosted a packed event focused on long-horizon AI agents, reinforcement learning, evaluation, and the infrastructure behind modern agent systems. Many thanks to our speakers for sharing their work: • Jun Park (hillclimb) • Sijun Tan (University of California, Berkeley) • Hanchen Li (University of California, Berkeley) • Ying Liu (Fireworks AI) • Muhammad Annas Hashmi (Daytona) We look forward to continuing the series with more discussions on the future of AI research.
Last Saturday, Daytona hosted HackSprint in Singapore in partnership with AI Builders alongside Oxylabs.io, Nosana, Doubleword, and ai&. 120+ builders joined us for a full day of building, resulting in 47 impressive project submissions. Thanks to everyone who took part. Looking forward to many more events across Singapore and Asia in the months ahead.
"An agent is only as capable as the environment it can safely act in." NeoSigma published a great deep dive on building agent workspaces on Daytona. It covers how they give AI agents a real, isolated, reproducible machine to work in: warm pools for instant startup, full Docker + filesystem fidelity, and disposable, snapshot-backed sandboxes. Check it out 👇
How We Built Sandbox Infrastructure for Autonomous Agents How we built the workspaces where AI agents execute, explore, and learn from experience. A deep dive into the architecture behind realistic, reproducible, fast, and isolated agent execution using Daytona Sandboxes. An agent is only as capable as the environment it can safely act in. You can have a strong model and a perfect reward signal, but without a real place to execute, none of it is verifiable. Capability you cannot verify is just a claim. So we built workspaces that resemble a developer's laptop rather than a locked-down shell. Real filesystem, a running Docker daemon, cloned repositories, MCP servers, and databases with real data. Rather than reasoning about whether a change should work, the agent builds the app and runs it. We rely on Daytona as a sandbox provider to make this practical. Our warm sandbox pools let us keep environments pre-provisioned and ready, so agents attach almost instantly instead of waiting on a cold boot. Daytona's kernel and hardware level isolation is also what lets us hand agents this much execution power without the risk of one sandbox bleeding into another. Handing an agent that much power sounds reckless until you move the dangerous parts out of reach. Secrets never enter the sandbox at all. A proxy attaches them on the way out, and a strict egress allowlist means a prompt-injected request to an unapproved host never leaves the boundary. Every run also starts from an identical database, branched copies of data from a shared reference, so determinism costs almost nothing. That turns a sandbox run into the closest version to a real production run. The future of reliable agents will not come from limiting what they can do. It will come from building environments where they can do more safely. Read more about our blog here: https://lnkd.in/gFCQ8swH