Using ADB via the command line can often feel like a cumbersome task. The process of locating the executable, launching a terminal window, connecting a device, and pasting in the APK file path can quickly become tedious. A streamlined application to handle these tasks would undoubtedly enhance efficiency. While existing solutions like ADB AppControl, EasyADB, and QuickADB are available, the desire to create a tailored application that zeroes in on the APK installation workflow—encompassing wireless debugging pairing, file browsing, and bundle unpacking—sparked a personal project.
Have you ever used Google’s ADB tool?
In a recent poll, responses varied widely:
- I use it fairly often: 48%
- I’ve used it once or twice: 35%
- Never used it: 11%
- I have no idea what ADB is: 7%
As a moderately skilled programmer balancing work and family life, the prospect of developing an app from scratch felt daunting. This led to the exploration of AI-assisted “vibe coding,” allowing for the integration of a new programming language, Go. The choice of Go was strategic; its simplicity makes it ideal for compact projects, and it compiles into a small, cross-platform executable. Additionally, the aim was to craft a visually appealing terminal app using the Bubble Tea TUI framework, which had recently caught my eye. With a design vision in mind, I sought an AI partner to assist in the development process.
The trials and tribulations of vibe coding
Having experimented with AI tools, I recognized that relying solely on them to build an application can be unpredictable. High-end models from Anthropic and OpenAI may handle straightforward tasks efficiently, but complex projects often lead to frustrating back-and-forth exchanges to iron out bugs. For this endeavor, I preferred a more hands-on approach to interact with ADB and construct a terminal-based UI.
Rather than investing heavily in costly AI models, I opted for a practical solution: pairing my usual IDE with OpenCode’s affordable Go subscription. This provided access to MiniMax M2.7 and the impressive GLM 5, allowing me to collaborate with AI while leveraging my own coding skills.
I initiated the project by outlining its structure—creating folders, placeholder functions, and a .gitignore file. I also drafted an AGENTS.md document detailing the project’s requirements and goals. Instead of delegating the entire project to AI, I methodically progressed through planning, building, reviewing, and refining each phase. Utilizing version control, such as Git, proved invaluable, enabling me to revert major AI errors and navigate through inevitable challenges.
The first milestone was developing a basic ADB wrapper in Go, enabling commands like adb devices to identify connected devices and adb install to push APKs. This foundational step evolved into a basic terminal interface, though it lacked user-friendliness. The most challenging aspect was designing the UI flow. Learning a new language and framework simultaneously added complexity, but by instructing the AI to allow theming through a configuration file, I could make manual adjustments, saving both time and resources.
Does my app actually work?
Ultimately, the collaboration of human planning, AI assistance, and iterative tweaking culminated in a functional application within just three days of part-time effort. The result was a compact 7MB executable that requires no installation. It can automatically download ADB from Google if necessary or utilize an existing installation. The app efficiently manages ADB sessions, allowing users to pair with debuggable Android devices over USB or Wi-Fi, browse files with a built-in explorer, and install APKs directly. It also supports unpacking and installing various app bundles, including APKM, XAPK, and APKS files.
While the app is genuinely useful, a drawback emerged: my understanding of Go did not deepen as I had hoped. Relying on AI for problem-solving limited my grasp of the language’s syntax and patterns. Although I gained some insights from reviewing the code, I still wouldn’t consider myself proficient in Go.
This highlights a key risk of vibe coding: the potential for superficial understanding of the code. During my review of AI-generated changes, I identified a few minor bugs and even a zip slip vulnerability. The stakes are higher when dealing with sensitive elements like encryption or API keys. My advice for those venturing into vibe coding is to invest time in planning and comprehending not only how your application should function but also the necessary tools, project management strategies, and common pitfalls. This preparation positions you to effectively evaluate AI outputs when they inevitably fall short.
For those interested in trying my app, releases and the source code are available on the Tiny APK Installer GitHub repository. While I have yet to test the Mac or Linux builds, I trust that Go has compiled everything correctly. Should you encounter any bugs, please feel free to report them—perhaps with a nod to the AI that made this project both easier and more complex.
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