In the niche world of data recovery, where forgotten technologies from the 1990s still hold valuable secrets, one engineer has turned to cutting-edge artificial intelligence to breathe new life into obsolete hardware. Dmitry Brant, a hobbyist with a passion for salvaging data from ancient tape cartridges, recently detailed his efforts to update a 25-year-old Linux kernel driver using Anthropic’s Claude Code tool. As described in his blog post on DmitryBrant.com, Brant’s project centers on QIC-80 tapes, those finicky backup mediums popular among small businesses and bulletin board system operators decades ago. Despite their design flaws, these tapes remain a treasure trove for data archaeologists, and Brant’s work highlights how AI can bridge the gap between legacy systems and modern computing.

Brant, who runs data recovery workstations, previously tinkered with the ftape driver—a piece of software that allows Linux to interface with floppy-connected tape drives. His earlier attempts, chronicled in a 2023 brain dump on the same site, involved getting the driver to run on contemporary distributions. But the code, last touched in the late 1990s, was riddled with incompatibilities, from outdated APIs to compilation errors in today’s kernel environments. Enter Claude Code, Anthropic’s terminal-based AI agent designed for codebase comprehension and automated tasks, which Brant employed to refactor this relic.

AI as a Kernel Whisperer

The process began with Brant feeding the old ftape source code into Claude Code, instructing it to identify and fix build issues for a modern Linux kernel. As Brant recounts, the AI not only pinpointed problematic sections but generated patches, handled git workflows, and even suggested architectural improvements. This isn’t mere code completion; Claude Code’s ability to “understand” an entire codebase, as praised in a Builder.io article on practical tips for using the tool, allowed it to navigate the driver’s complex interactions with floppy controllers and tape mechanisms.

Iterations were key. Brant describes multiple rounds where Claude proposed changes, he tested them on real hardware, and fed back errors for refinement. One notable hurdle was adapting the driver to kernel versions post-2.6, where memory management and interrupt handling had evolved significantly. The AI’s suggestions, while not always perfect, accelerated what would have been weeks of manual debugging into days, aligning with experiences shared on Hacker News, where developers discussed similar modernizations of forgotten Linux drivers.

From Hobby to Broader Implications

Beyond Brant’s personal triumph—successfully dumping tape contents on a current Ubuntu setup—the project underscores AI’s growing role in maintaining digital heritage. Tools like Claude Code, detailed in Anthropic’s own best practices guide, are democratizing access to complex engineering tasks. For industry insiders, this means rethinking workflows: instead of poring over archaic documentation, engineers can leverage AI for rapid prototyping and error correction in kernel-level development.

Security considerations loom large, however. A recent Bleeping Computer report highlighted how threat actors have misused Claude models for malicious code, prompting calls for safeguards in AI-assisted programming. Brant, focused on benign recovery, avoided such pitfalls, but his story serves as a cautionary tale for enterprises adopting these tools.

Hardware Meets High-Tech

Brant’s integration extended to hardware testing, echoing experiments like those from Adafruit’s blog on automating Arduino development with Claude Code. By scripting interactions between the AI and physical tape drives, he achieved reliable data extraction, preserving files that might otherwise be lost to time.

This fusion of old and new tech points to a future where AI doesn’t just write code but revives entire ecosystems. As Brant concludes in his post, the tactile joy of handling vintage tapes pairs unexpectedly well with AI’s efficiency, offering a model for insiders tackling legacy systems in sectors from finance to aerospace. While challenges like AI hallucinations persist, successes like this demonstrate the tool’s potential to extend the lifespan of critical, if outdated, infrastructure.