July 2026 Summaries

Secrets Manager is a tool designed for securely managing credentials needed for AI-generated code execution within Daytona sandboxes, ensuring that sensitive information like API keys and access tokens remain hidden from the agents using them. By injecting secrets at runtime and masking their real values, Secrets Manager allows agents to authenticate to external systems without direct access to the underlying credentials, thereby reducing the risk of exposure when running untrusted or AI-generated code. This system separates access from visibility, with secrets scoped and managed per organization, available only to the specific sandboxes that require them. The tool is available to all Daytona organizations, and users can utilize the provided documentation to implement secret management within their workflows. Juraj Štefanić, a Technical Content Engineer at Daytona, plays a crucial role in helping developers adopt this infrastructure, creating content that bridges the gap between sandboxing technology and practical developer workflows.

Daytona's Windows Sandboxes offer real Windows environments with instant virtual machine (VM) provisioning, enabling automation and testing to run against genuine Windows software rather than Linux approximations. This ensures that desktop applications, enterprise tools, and automation targets that require Windows operate as they would in production. The sandboxes provide a fully isolated VM running the native Windows operating system, complete with registry and APIs, and they can be provisioned in seconds, unlike traditional VMs that take minutes to boot. These sandboxes are particularly useful for desktop application testing, robotic process automation (RPA) workflows, and Windows-specific automation, as they allow users to immediately run regression suites, automate business processes, and drive workflows requiring Windows-specific features. Daytona offers snapshots for creating sandboxes in various sizes to meet different computational needs, ensuring flexibility and efficiency for all users.

GPU Sandboxes offer dedicated GPU access to ensure consistent performance and hardware-level isolation for GPU-bound workloads, such as reinforcement learning, model training, and inference. By using VFIO passthrough, each sandbox receives a direct connection to a physical GPU, eliminating the performance variability and isolation issues associated with shared or virtualized GPU resources. Supported GPU models include NVIDIA H100, H200, RTX Pro 6000, RTX 4090, and RTX 5090. This setup allows for predictable and uncontested GPU access, making it ideal for applications requiring reliable and direct hardware interaction. GPU Sandboxes are available to Daytona organizations, who can access detailed documentation to facilitate their implementation.