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dagger/dagger v0.21.11 — Codebase Delivery Automation Engine

dagger/dagger is a platform that automates the build, test, and deployment of codebases in local, CI, or cloud environments.

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dagger/dagger is a platform that automates the build, test, and deployment of codebases in local, CI, or cloud environments.

GitHub description: Automation engine to build, test and ship any codebase. Runs locally, in CI, or directly in the cloud

stars
★ 16.5K
Forks
937
Language
Go
License
Apache-2.0
Latest release
sdk/rust/v0.21.11
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dagger/dagger is a platform that automates the build, test, and deployment of codebases. The latest release recorded in the repository is v0.21.11. It is written in Go and released under the Apache-2.0 license. It can run not only in local environments but also on CI servers and in the cloud. The information available for this round contains no specific list of changes for v0.21.11, so it is not possible to state definitively what differs in this version.

Dagger is designed so that development and deployment work is defined in code, then executed repeatedly and reused. According to the README, it provides an execution engine and system API, SDKs for multiple languages, an interactive REPL, and reusable modules. The REPL is an interactive environment where you enter commands and check the results. Work can be organized into reusable functions, and connecting build, test, and deployment with agent tasks is also presented as a use case. It also includes OpenTelemetry tracing for inspecting execution records, and a feature that uses content addressing to run only the required tasks incrementally. Content addressing is a way of identifying tasks based on their input content. The key point is that it is a tool for handling the same workflow in code, regardless of whether the execution environment is local or CI.

That said, the provided record shows only the fact that the latest release is v0.21.11, and does not include the fixes for that version or any performance and stability test results. So rather than judging the effect of this release, it is more accurate to treat it as a resource for examining the components and execution model offered by an automation platform managed in code.