Reviving a 1990s Linux Tape Driver with AI in Two Evenings

The author used Claude Code to modernize the legacy ftape kernel driver so it builds out-of-tree and works on current Linux kernels. Through iterative compilation, API updates, and dmesg-driven debugging, he resolved configuration issues and achieved reliable tape detection and raw data dumps. He concludes that AI can be a powerful collaborator—like a junior engineer—when paired with human expertise and precise guidance.

Key Points

AI-assisted modernization: Claude Code helped update ftape from Linux 2.4-era APIs to build and run as an out-of-tree module on modern kernels.
Iterative, log-driven debugging: dmesg output and a known-good log enabled pinpointing issues like default base addresses of -1 becoming 0xffff and causing ENXIO.
Human-in-the-loop is essential: baseline kernel/C knowledge, careful prompting, and manual verification were required; the agent acted like a capable but junior collaborator.
Practical benefits: the updated driver can dump raw tape data independent of proprietary formats, reviving 1990s hardware on current distros.
General lessons: be specific with prompts, pick tasks suited to agents, use them as force multipliers, and for rapid onboarding into unfamiliar frameworks.

Sentiment

Mostly positive and optimistic about AI-assisted kernel and systems work, with pragmatic caveats about security practices and upstream kernel acceptance.

In Agreement

AI coding agents act as a major force multiplier for experienced developers, especially within familiar frameworks.
These tools dramatically speed up onboarding to new stacks and reduce time spent on boilerplate and framework quirks.
Keeping the revived driver out-of-tree is prudent and avoids friction with Linux kernel maintainers wary of AI-generated code.
AI lowers the barrier to kernel hacking and could broaden support for embedded/ARM hardware and niche devices.
Well-structured internal APIs/schemas let AI quickly generate usable UIs and scaffolding, enabling higher-level human focus.

Opposed

Enabling passwordless kernel module operations via sudoers is a serious security risk on non-isolated machines.
The Linux kernel community is skeptical of AI-generated code, so upstreaming such work would face scrutiny even if it functions.
Novice ‘vibe coding’ with AI can lead to trouble; without expertise and oversight, results may be unsafe or low quality.