Show HN: Load-lock, a machine-wide semaphore for parallel AI coding agents (github.com)

Load-lock, a newly released tool, introduces a machine-wide semaphore for parallel AI coding agents to enhance their collaborative efficiency. By allowing multiple agents—such as OpenCode, Codex, and Claude Code—to share a single capacity pool for resource-intensive tasks, load-lock prevents interference, reduces cache thrashing, and minimizes resource starvation. This cooperative system operates on a queue mechanism, where agents acquire permission to execute heavy operations while enabling them to continue lighter tasks, thereby optimizing workflows without disrupting ongoing processes. The significance of load-lock lies in its ability to streamline the development pipelines of AI/ML applications, especially as teams increasingly deploy multiple coding agents concurrently. Its architecture includes crucial features such as crash safety, lifecycle management, and precise notifications, ensuring that resources are allocated effectively while maintaining system integrity. It also supports various environments, from native installations to sandboxed settings in Docker or macOS. With detailed compatibility and operational documentation, load-lock not only improves efficiency but also sets a new standard for managing shared resources in parallel programming environments, benefiting developers in the AI/ML community.