Reverse engineering is becoming as simple as having a conversation with an agent. But when implementation scarcity disappears, software does not become authorless.
Earlier this week, I spent an evening examining morluto/rea (Reverse Engineer Anything), an open-source harness that connects AI coding agents to decompilers like Ghidra and Hopper through the Model Context Protocol. Its operational triad is simple: Decompile, Understand, Recreate. Point an agent at a compiled binary, an Electron application package, or a minified web bundle. The agent extracts symbols, reconstructs call graphs, maps control flow, and then drafts a clean-room implementation in modern TypeScript or Go.
Let us be precise about the reality. REA does not perform magic. It does not push a single button to recover Adobe Photoshop from an executable, and it does not magically decrypt obfuscated enterprise code into pristine original source. What it does is more interesting: it removes the mechanical friction of reverse engineering. It turns the arduous work of manual disassembly into an afternoon conversation with an agent.
That shift prompts a question that has occupied me for months:
If any piece of software can be inspected, understood, and rebuilt on demand, what is original software actually worth?
The Illusion of Universal Abandonware
A developer recently asked me a provocative question: "If an agent can inspect any application and recreate its core features on demand, is all software abandonware now?"
It is a striking line, but it confuses two entirely different things: the scarcity of implementation and the continuity of authorship.
Implementation scarcity is collapsing. Writing syntax, assembling API wrappers, and scaffolding database schemas used to require months of manual typing. Today, a capable model can produce functional code in minutes. When an agent can translate a compiled binary into a working web service before dinner, code alone stops functioning as a defensible moat.
Yet the collapse of implementation scarcity does not mean software has no authors, no stewards, and no history.
A generated codebase contains syntax. It does not contain the hard-won experimental history, the production scars, or the trust earned across years of real-world use. When we confuse a fast re-implementation with the invention itself, we mistake the shadow of a building for the architecture that cast it.
Skills Are Easy to Generate. Discoveries Are Not.
Consider procedural agent skills. A SKILL.md file is just Markdown instructions and execution guardrails for an AI harness. Anyone with access to a frontier model can type a prompt: "Write me an expert skill for end-to-end browser testing." In thirty seconds, the model emits two hundred lines of clean Markdown with numbered steps, lint assertions, and suggested flags.
Does that generated file know about the subtle race condition where headless Chromium on ARM64 Linux silently clips sub-pixel fonts during high-DPI canvas exports?
Of course not. That knowledge was not invented by a prompt. It was uncovered by a human engineer who lost three days of their life to broken CI builds, isolated the failure, documented the workaround, and packaged the solution into a repeatable procedure.
The prompt recreates the words. The original lineage preserves the scars.
When an agent copies that procedure without attribution, it consumes the fruit of that labor while discarding the record of how it was earned. Implementation can be replicated in seconds. The experience required to discover what was worth implementing cannot.
Open Source Has Provenance. Stars Are Merely Attention.
In open source, we have understood this distinction for decades. Git does not track popularity; it tracks provenance.
Every commit is a cryptographic hash linked to its parents. Author identities, signed tags, and merge histories form an unbroken chain of custody. You can inspect a Linux kernel driver or an open-source database and trace an optimization back to the exact developer who committed it fifteen years ago.
GitHub stars, by contrast, measure attention. A clever marketing hook or a viral video can generate ten thousand stars over a single weekend for a thin wrapper around a decade-old library.
Popularity is a signal. It is not proof.
When agents make software trivial to recreate, star counts become even noisier. An agent can clone a trending repository, rebrand its interface, and publish it under a new name in an hour. If our only metric of value is popularity, the loudest promoter wins and the person who actually solved the hard problem disappears.
The Electric Car: Inventors, Engineers, and Scalers
Consider the history of the electric automobile. Who invented it?
In the 1830s, Ányos Jedlik in Hungary and Thomas Davenport in Vermont built early direct-current motors and small electric carriages. Around 1890, William Morrison built the first successful six-passenger electric wagon in Des Moines, Iowa. In 1898, Ferdinand Porsche engineered the Egger-Lohner C.2 Phaeton, followed by the Lohner-Porsche hybrid in 1900. By the turn of the century, electric cars accounted for nearly forty percent of all automobiles sold in the United States.
Yet if you ask someone on the street today who created the electric car, they will name Elon Musk, or perhaps Martin Eberhard and Marc Tarpenning, who founded Tesla Motors in 2003.
Did Tesla invent the electric car? No. But Tesla's engineers solved the grueling problems of mass-market battery thermal management, inverter efficiency, and high-volume manufacturing, while creating the cultural desire that made electric vehicles mainstream.
These are three distinct kinds of contributions:
- The inventors: who proved the physics were possible when everyone else doubted it.
- The engineers: who endured the iterations to make the system reliable, safe, and maintainable.
- The scalers: who created the market, the infrastructure, and the cultural momentum.
None of these people are interchangeable. Crediting the popularizer with the invention erases the originators. But dismissing the popularizer as a mere copycat erases the immense difficulty of scaling. A healthy ecosystem recognizes all three without confusing one for the other.
Photoshop, Canva, and Cultural Imprint
In 1987, Thomas and John Knoll wrote an image display utility that evolved into Adobe Photoshop. Over thirty-five years, Photoshop became an eponym: a noun, a verb, and a global cultural standard for digital image manipulation.
Today, Ivan Kutskir has spent more than a decade building Photopea, an astonishing browser-based raster editor that replicates Photoshop's layer styles, filters, and PSD parsing almost single-handedly. Meanwhile, Melanie Perkins, Cliff Obrecht, and Cameron Adams founded Canva, building a multi-billion-dollar platform that bypassed Photoshop's steep learning curve entirely by reimagining layout design for everyday creators.
Canva and Photopea proved that Photoshop's feature set can be reimagined or re-implemented in a web browser. But neither erased Photoshop's cultural identity.
Photoshop is not just a collection of pixel-blending algorithms. It is thirty-five years of muscle memory, color-science refinement, and professional workflow trust.
Software is more than its compiled binary. When an agent recreates the functions, it does not recreate the cultural trust.
What Yggdrasil Adds to the Equation
This question is the founding reason Gaia Skill Tree exists.
When we designed Yggdrasil II, our core architectural rule was: stop storing what you can compute. We separated topological capability graphs from trust and prestige.
In Gaia Skill Tree, recognition operates along two distinct axes:
The first axis is immutable provenance: who authored the skill, who discovered the procedural technique, and where the prior art originated. That historical record is permanent.
The second axis is operational trust: who verified the behavior, who wrote the comprehensive test suites, and who maintains runtime reliability in production harnesses. That prestige is earned through evidence.
Trust should strengthen attribution, not rewrite history.
If an engineer takes an existing procedural skill, hardens its error handling, and proves in benchmark runs that it reduces failure rates by forty percent, that engineer earns high Trust Magnitude and ecosystem prestige. But they do not erase the person who originally discovered the capability. Gaia Skill Tree honors both: the originating pioneer and the disciplined maintainer.
Reverse Engineer Anything asks: How does this work?
Gaia Skill Tree asks: Who contributed to making this worth understanding?
The Counterargument: Why Reverse Engineering Matters
Let us steel-man the opposing view. Reverse engineering is not an enemy of progress.
Historically, clean-room reverse engineering has been one of the most powerful equalizers in computing. When Phoenix Technologies reverse-engineered the IBM PC BIOS in 1984, they broke IBM's proprietary stranglehold and birthed the modern personal computer industry. In cases like Sega Enterprises Ltd. v. Accolade, Inc. and Sony Computer Entertainment, Inc. v. Connectix Corp., the courts established that inspecting software to achieve interoperability and create compatible platforms is protected fair use under copyright law.
If attribution is twisted into a weapon to protect incumbents, innovation stagnates. Monopolies that abandon their software or extract punitive rents from their users should not be shielded from competitors who can build a faster, lighter, more accessible alternative. Technical possibility and legal permission are separate matters, but economic freedom depends on the right to understand how tools work.
Attribution is not a legal tollbooth. It is not an intellectual property lock. It is an acknowledgment of intellectual debt.
You are free to recreate what came before; you are simply not permitted to pretend you arrived in a vacuum.
Who Gets Remembered?
As AI agents become faster and more capable, the barrier to recreating software will continue to drop toward zero. A codebase that took five years to build may soon be understood and refactored in five days.
Everything may become reproducible. But that doesn't mean everyone becomes interchangeable.
AI may make software reproducible without making its creators disposable.
We do not build systems like Gaia Skill Tree to make ideas belong to fewer people. We build them to make sure the people who advance them aren't forgotten.
References:
- Reverse Engineer Anything (REA): morluto, Reverse Engineer Anything, 2026. [GitHub]
- Photoshop Origin: Thomas Knoll & John Knoll, Adobe Photoshop 1.0, Adobe Systems, 1990.
- Electric Vehicle Lineage: Ányos Jedlik (1828); Thomas Davenport (1834); William Morrison (1890); Ferdinand Porsche (1898); Martin Eberhard, Marc Tarpenning, Elon Musk, JB Straubel, Tesla Motors, 2003.
- Fair Use & Clean-Room Precedent: Sega Enterprises Ltd. v. Accolade, Inc., 977 F.2d 1510 (9th Cir. 1992); Sony Computer Entertainment, Inc. v. Connectix Corp., 203 F.3d 596 (9th Cir. 2000).
- Yggdrasil II Architecture: Gaia Research, Yggdrasil II: Two Types, One Trust Gate, and a Branch Axis That Is Never Declared, July 2026. [Release Report]