Versions:

  • 0.2.13
  • 0.2.12
  • 0.2.11
  • 0.2.10
  • 0.2.9
  • 0.2.5

gw is a Gradle output filter developed by AndVl1, designed specifically to support AI coding agents that interact with Gradle-based build systems. The tool functions as a wrapper around ./gradlew, intercepting the build tool's standard output and processing it into a cleaner, more compact stream. Its core purpose is noise reduction: gw strips out elements that are irrelevant to automated consumers, such as daemon banners, download progress messages, task lifecycle output, and deprecation notices. At the same time, it preserves the information that matters most for diagnosing build behavior by forwarding errors, warnings, and status messages through to the caller. This selective filtering makes gw particularly relevant in the category of developer tooling for AI-assisted software engineering, where large volumes of verbose build output can overwhelm an agent's context window or obscure actionable failures. To signal that a build is still in progress despite the reduced output, gw prints a heartbeat, providing ongoing confirmation of activity without reintroducing clutter. Recognizing that filtered output may occasionally be insufficient for debugging, the tool also saves the complete, unfiltered log to disk, ensuring that full build details remain available for inspection whenever they are needed. This dual approach—concise live output paired with persistent full logs—makes gw a practical fit for use cases such as continuous integration pipelines driven by AI agents, automated code modification workflows, and any environment where an agent must monitor, interpret, and react to Gradle build results. The current release is version 0.2.13, and the project has published six versions to date, reflecting ongoing iterative development. By bridging verbose build tooling and automated consumers, gw addresses a specific gap in the emerging ecosystem of agent-oriented developer utilities, keeping builds observable and debuggable while maintaining the lean output that AI-driven workflows require.

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