GF CLI hot-swaps GlassFish classes, cutting redeploy times to seconds

This review examines 'gf', a command-line tool leveraging JVM's JDWP for rapid GlassFish class hot-swapping. It offers a terminal-first alternative to IDE-locked or commercial solutions, significantly reducing Jakarta EE development feedback loops.

The Answer Up Front

For developers frustrated by GlassFish's notoriously slow redeploy cycles, especially those who prefer a command-line workflow or operate outside of a single IDE, gf offers a compelling solution. It's a lightweight, open-source tool that bypasses the need for commercial licenses or specific IDE integrations to achieve fast class hot-swapping. Developers already satisfied with IntelliJ Ultimate's integrated hot-swap or whose organizations can easily afford JRebel may not find an immediate need to switch. However, for those seeking an editor-agnostic, zero-cost path to faster Jakarta EE development, gf provides a pragmatic, technically sound alternative.

Methodology

This v0 review draws on the founder's published claims at dev.to, specifically the article detailing the tool's creation and reported performance improvements. Independent benchmarks are pending. Update cadence: re-tested when claims diverge from observed behavior.

• Tool name + version + date observed: gf (glassfish-hotswap-cli), version not explicitly stated, observed 2026-06-03.
• Source signal URL: https://dev.to/riyadomf/how-i-used-the-jvms-jdwp-to-cut-glassfish-redeploys-from-2-minutes-to-5-seconds-45pm
• What's covered in this review: The founder's claims regarding redeploy time reduction (2 minutes to 3-6 seconds), the technical approach (JVM's JDWP), the tool's CLI-first design, and its open-source nature. Comparisons to IntelliJ Ultimate and JRebel are based on the founder's experience.
• What's NOT covered: Independent performance benchmarks, long-term workflow integration, robustness across diverse GlassFish configurations, or edge cases involving complex class loading or structural changes that exceed JVM hot-swap capabilities.

What It Does

gf is a command-line interface (CLI) tool designed to accelerate the development feedback loop for Jakarta EE applications running on GlassFish. It specifically targets the common scenario where developers make minor code changes, such as modifying a method body or adjusting a conditional, which do not necessitate a full application redeploy.

Leveraging JVM Debugging Internals

The core of gf's functionality lies in its direct interaction with the Java Virtual Machine's (JVM) Java Debug Wire Protocol (JDWP). This protocol, shipped with every JVM since Java 5, provides the underlying mechanism for hot-swapping classes. The founder, riyadomf, identified that while the capability existed, access to it was often restricted to specific IDEs or commercial products. gf exposes this capability via a simple command.

CLI-First Workflow

Unlike solutions integrated into specific IDEs like IntelliJ Ultimate, gf operates entirely from the terminal. The founder reports using a command such as ./gf sync to trigger the hot-swap. This design allows for integration into various development environments, including Bash scripts, Makefiles, CI pipelines, tmux sessions, or even AI coding agents. The tool's small footprint, composed of

The investor read

The gf tool highlights a persistent pain point in mature enterprise ecosystems like Java EE: slow developer feedback loops. While the broader market has shifted towards faster build times and microservices, a significant installed base still relies on traditional application servers. This creates a niche for developer experience tools that can improve productivity without requiring a full architectural overhaul. gf is likely a deliberate small/bootstrapped play, targeting a specific problem with a simple, open-source solution. It signals that even in well-trodden categories, opportunities exist for composable, CLI-first tools that unlock existing platform capabilities. Investors might look for similar 'access' plays in other mature, slow-moving enterprise stacks, particularly where commercial solutions are expensive or tightly coupled to specific vendor ecosystems.