Project Claims No Reverse Engineering, Only Adherence to Public Standards: AI Agents Release 16 Software Clones in Weeks
Developer Brandon Thomas released open-source clones of 16 Adobe and Microsoft applications in Rust over a few weeks. The project claims a clean-room approach—reimplementing based on observable behavior and public specifications rather than reverse engineering the original code. iX Magazine reports that the combination of AI agents and CI pipelines is key to this speed, but specific architectural details remain behind a paywall.
By late September 2026, developer Brandon Thomas had released open-source clones of several Adobe Creative Cloud applications in a very short time, including alternatives to Photoshop, Lightroom, and Premiere Pro. A few days later, clones of Microsoft Word, Excel, and PowerPoint also appeared. By mid-November, the project included 16 programs, all written in Rust, all marked as incomplete, yet many already functional.
Ulrich Wolf of iX Magazine reported on this phenomenon, highlighting the development method claimed by the project: no reverse engineering whatsoever. All clones position themselves as clean-room reimplementations—starting from observable application behavior and comparing results against publicly available specifications, such as ISO 32000 (PDF format), ECMA-376 (Office file formats), or Adobe's PSD file description.
This methodological choice is critical for AI-assisted development. Reverse engineering requires understanding a massive, undocumented codebase, which is nearly impossible for current AI agents. The clean-room approach reframes the problem as specification compliance: given an input, what should the output behavior be? Given a file format, what should the parsing result be? These are testable, decomposable tasks with clear success criteria—exactly the strengths of AI agents and automated testing pipelines.
The article's subtitle mentions that the synergy between AI agents and CI pipelines is the source of this speed. Although the full technical architecture explanation is behind a paywall, public information allows us to infer its working mode: AI agents generate implementation code for specific functional modules, while the CI pipeline automatically runs tests against public specifications; failure results are fed back to the agents for correction. This loop shifts the human developer's role from writing code to defining tests and reviewing results.