Resource-based worker auto-tuning is now in public preview for several programming languages, including Go, Java, Python, .NET, and TypeScript, offering new capabilities to improve Temporal Worker performance and management. This feature allows Workers to automatically adjust available task slots based on CPU and memory usage, thereby simplifying configuration and potentially preventing out-of-memory issues and efficiently handling large bursts of low-resource-usage activities. Users can set targets for CPU and memory utilization, enabling the system to dynamically allocate resources up to the specified limits, reducing the need for constant monitoring and manual adjustments. Additionally, options for more granular control over task slot allocation are available, allowing customization of minimum and maximum slots alongside throttle timings. This auto-tuning capability aims to optimize Worker operations, although it remains in experimental status and is subject to adjustments based on user feedback and system requirements.