I Showed an AI a Program — It Started Rebuilding It

If you spend enough time in overseas developer communities these days, you keep running into the same story.

Feed an existing program to an AI, let it analyze the internals, then have it build something similar from scratch. The open-source project at the center of this is REA (Reverse Engineer Anything), and it's taken off on GitHub to the point where people are genuinely asking whether software development as we know it is finished.

There's even a circulating story about someone analyzing Windows Paint and building a clone — complete with features the original never had.

Honestly, I was skeptical at first. So I spent a few hours digging into the project and the public case studies.

Here's the short version: the tech is genuinely impressive, but "development is over" is an overstatement. Let me break down what I actually confirmed versus what I couldn't.

1. What REA Actually Does

First, let's clarify what REA is.

REA is an open-source project that bridges existing reverse-engineering tools with AI coding agents. It helps AI investigate a program's code and structure using tools like Ghidra, Hopper, and IDA.

In simple terms: it puts analysis tools in the AI's hands.

Ask a typical AI to "build me something like Paint" and it'll write code based on general patterns of graphics applications it learned during training. But actually diving into how a specific existing program implements its features? That's a completely different problem.

With REA, the AI can invoke analysis tools, reason about what functions do, implement the feature, and test the result — all in a loop. Simplified:

1. Analyze the existing program.
2. Investigate the code and how the features work.
3. Write new code based on the analysis.
4. Compare against the original and fix discrepancies.

One important distinction: REA is not a magic button that clones a program wholesale. It's a connector between analysis tools and the AI — nothing more.

2. Does It Actually Match the Original?

This was my biggest question. So I worked through the Paint example.

Say the AI analyzes Paint and builds a similar interface. It draws with brushes, picks colors, saves files. On the surface, it might look pretty close.

But does that mean it perfectly replicated the original?

No. There's a long way to go before you can confirm parity:

• Does it handle the same variety of image files identically?
• Do copy/paste, undo/redo work the same way?
• Do zoom and pixel manipulation produce identical results?
• Does it hold up with large files or edge cases?
• Does it integrate with the OS the same way?

A similar-looking interface and identical functionality are not the same thing. One working feature and an entire program matching the original are also different claims.

So when you hear "AI cloned a program," the first question should be: what exactly was replicated, and how much of it?

None of this diminishes the technology. The fact that AI can now handle a significant chunk of what used to require a human analyst working manually is already a big deal.

3. Have There Been Successful Experiments?

The public materials include some genuinely compelling experiments.

In some cases, features like zoom, annotation, drawing, rotation, and save were reimplemented from an existing app. There are also published accounts of analyzing a custom-built executable, replicating specific behaviors, and verifying the results against the original with multiple test runs.

That confirms the AI can go beyond mimicking the interface — it can investigate how a feature works and reimplement it in new code.

But here's where you need to draw a line.

Reimplementing a specific feature in a small program and replicating an entire large-scale commercial application are vastly different challenges. Just because some features were successfully replicated in published experiments doesn't mean any program can be perfectly cloned.

Another thing: REA isn't always faster or more accurate. In published comparisons, a standard coding agent built something similar too — and in some cases finished first.

Ultimately, REA isn't a universal solution. It's a tool that extends AI's analytical capability in specific situations.

4. Is "Software Development Is Over" an Overstatement?

My conclusion: yes, it is.

Being able to analyze a program and reimplement similar features is one thing. Software development itself being "done" is another.

Building real software isn't just about laying out a screen and writing code. You need to understand what users actually want, handle countless edge cases, and ensure security and stability. As programs grow, managing interactions between features becomes critical too.

AI writing code doesn't automatically solve any of that.

So should we dismiss this technology?

No. The claim that "software is over" may be overblown, but the way software gets built is genuinely likely to change.

In the past, replicating a feature from an existing program required a developer to manually operate analysis tools. Now AI handles a lot of that analysis. The human's role is shifting toward deciding what to build and verifying that the result actually matches the original.

After about an hour with REA, I feel like I've developed a slightly better eye for separating flashy demos from real working software. That's what I took away from this.

Next time I come across an impressive-sounding tool, I plan to do the same thing: test it myself, verify the claims, and write down what I find.