TL;DR
The developer of AMP, a music player written in Zig, says that after using an LLM to generate a substantial part of the project, he spent four months reviewing and rewriting the code. He reports that nearly half the code is now gone while the player retains its features and is more robust. The account is a personal report, not an independent technical evaluation.
The developer of AMP, a music player written in Zig, says he spent four months rewriting a version of the project that had been partly generated with a large language model, removing nearly half its code while retaining its features. In a personal report titled “How Not to Build a Music Player,” he describes the cleanup as a response to code he found difficult to review and unnecessarily complicated; his account is not an independent audit of the software.
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The developer says he began AMP after internet shutdowns in Iran made online music services unreliable. He had been using Strawberry, an offline-capable desktop music player, but wanted lyrics without distracting timestamps and decided to build a smaller application of his own. His stated goals included a minimal feature set, no C++ dependencies, integration with the desktop environment and implementation in Zig, including its shaders.
He says another prolonged blackout interrupted work on the project. During that period, he used limited and expensive connectivity to download software repositories and documentation. After internet access was mostly restored, he experimented with an LLM using a Claude Max 20x subscription, prompting it for several days and producing what he describes as a substantial portion of AMP.
The developer says he initially expected he could clean up the generated code later, but found that reviewing tens of thousands of lines was beyond what he could manage. He also says his Anthropic account was banned after the company identified his location in Iran, and that Codeberg’s no-AI policy prompted him to remove the AI-generated material rather than move the repository. He reports that nearly half the codebase was removed during the rewrite and that the resulting player has the same features, plus others, and is more robust. The report does not provide a before-and-after code comparison or independent test results.
The Cost of Reviewing Generated Code
The account offers a practical warning for software developers: generating code quickly does not remove the work of understanding, testing and maintaining it. In this case, the author says the time spent reviewing an unwieldy codebase led him to rewrite much of a hobby project. That is a personal experience, not evidence that every LLM-assisted project will need similar rework.
AMP’s origins also show how access to software can depend on internet availability. The developer says blackouts led him to seek offline music and made working on the player a source of enjoyment during a difficult period. For readers interested in both AI coding tools and resilient software, the project connects those issues without establishing a broader technical finding.
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From Offline Listening to AMP
The report presents AMP as a response to the developer’s own listening needs. After experiencing internet disconnections, he started archiving music and used Strawberry, but wanted lyrics presented differently. He says he was unaware of alternatives such as foobar2000 when he decided to make a player himself.
The project’s planned work includes a music server, Android and Windows support, and packaging for major Linux distributions. The developer describes the current release as version 0.1. These plans are a roadmap, not announcements that those features or platforms are already available.
“Little did I know I’m mentally incapable of reviewing tens of thousands lines of code.”
— AMP’s developer, in “How Not to Build a Music Player”
What the Rewrite Does Not Show
The report does not name specific code changes or show examples from before and after the rewrite. The author says he has no pre-rewrite repository history available to check and cannot recall a concrete example of the unnecessary complexity he encountered. The claims that the project is more robust and has additional features are therefore the developer’s own assessment; no test results, code review or outside comparison are provided.
It is also unclear how much of the original code was generated by an LLM, which parts were rewritten, and whether the full project history or licensing implications were examined. The account does not give a release schedule for the planned server, mobile and Windows work.
AMP’s Planned Platform Expansion
The developer’s stated roadmap calls for a music server, support for Android and Windows, and packages for major Linux distributions. The report does not give target dates, so these remain plans rather than confirmed upcoming release milestones. For now, AMP is presented as version 0.1, and the author says he intends to return to a more hands-on approach to development.
Key Questions
What is AMP?
AMP is a music player written in Zig. Its developer says the project began as a minimal, offline-oriented player with desktop integration and a custom interface.
Why did the developer rewrite the code?
He says an LLM generated a substantial part of the project, but he found the resulting code too difficult to review and more complicated than necessary. He chose to rewrite it rather than move the repository after Codeberg announced a no-AI policy.
Was nearly half the code removed?
That is the developer’s reported result after four months of rewriting. The report does not provide a repository comparison or independent verification.
Is AMP available for Android and Windows?
The developer lists Android and Windows support on the roadmap. The report does not say those versions are currently available or provide release dates.
Does the report prove LLM-generated code is unreliable?
No. It documents one developer’s experience with one project. The author says the rewritten version is more robust, but provides no independent tests or evidence that would support a general conclusion about LLM-generated code.
Source: alichraghi.github.io
