Why is REA blowing up? An explanation for non-coders

Recently, an open-source project called REA (Reverse Engineer Anything) has been blowing up.

It passed 100,000 GitHub stars on October 11, and by the 13th it had passed 200,000. As of October 14, among the GitHub repositories tracked by the third-party site GitTrend, it ranked 27th of all time by total stars.

You can think of a star as a bookmark. It doesn’t tell you how many people actually use it, but growth this fast is genuinely rare.

I spent a lot of time reading the introductions and asking AI over and over, and I was still a bit confused. Everyone in tech is talking about how powerful it is, but I didn’t even really understand why anyone would need it.

So I want to try explaining it in a way I can understand myself, for friends who, like me, don’t have a professional software engineering background but are interested in AI.

I. Why would software need reverse engineering?

When I studied mechanical engineering as an undergrad, I came across reverse engineering: you take a part, measure its dimensions, analyze its structure, then remodel and manufacture it.

But software is different.

A piece of software can be used again and again by countless people.

If it already runs well, why would you need to reverse engineer it?

Later I realized that being able to use a piece of software doesn’t mean you can modify it, or reuse what it can do.

Here’s an example:

Say a bookkeeping app stops being maintained. You’ve stored years of records in it, but there’s no way to export them to another app.

If a developer can figure out its file format, they might be able to build a conversion tool to get that data out.

You don’t want to copy the whole bookkeeping app. You just want your data back.

There are similar needs too: getting old games to run on new computers, checking whether an unknown program poses a security risk, or studying how some unique feature is implemented.

These are all uses of software reverse engineering.

II. What exactly does REA do?

The software we usually install only comes as a program that runs, not the full source code written during development.

In the past, if you wanted to study how it worked inside, it took a professional engineer using complex analysis tools, hunting for clues bit by bit.

REA connects these professional tools to AI coding agents like Codex and Claude Code.

You can just tell the agent: “Take a look at how this feature in this software is implemented.”

The agent then uses REA to investigate the program’s internals, find the relevant code, trace how it runs, and provide evidence wherever possible.

For example, the REA team studied Notion’s copy-and-paste feature, tracing how formatting information gets passed around inside the desktop app before finally landing in the system clipboard.

It isn’t just guessing from the interface—it’s studying how the program actually works.

REA isn’t a new AI model. It’s more like a professional toolkit for coding agents to use.

III. Codex can already write code, so why do you need REA?

This was a question I couldn’t figure out for a long time.

Say I want to build a search feature. I just tell Codex what I need, and it can build it for me using well-established technical approaches.

Why would I need to study how someone else implemented it first?

Actually, most of the time you don’t.

The difference comes down to what problem you’re actually trying to solve.

If you want to build a feature, a regular coding agent is usually enough.

But if you want to know how some software without public source code really works inside, you may not be able to tell just from how it behaves.

That’s where REA helps the agent use professional tools to dig further into the real program.

Of course, Codex could install and operate these tools on its own. REA’s value is that it bundles the relevant tools, workflows, and evidence-gathering ahead of time, lowering the barrier to using them.

IV. Why is it blowing up now, of all times?

I think there are two reasons.

First, coding agents have reached this stage of capability.

Before, AI could tell you how to do reverse engineering, but a lot of the complex work still had to be done by professionals.

Now agents can operate tools on their own, analyze the results, keep investigating, and even try to reimplement features.

REA makes it easier to put these capabilities together.

The other reason is the possibilities it opens up in people’s imaginations.

When we see good software someone else has made, we start to wonder:

Could we someday have AI take it apart, then recreate it to fit our own needs?

That possibility is really appealing. Even without a real need right now, it’s worth a lot of developers bookmarking and keeping an eye on.

That said, REA is nowhere near being able to copy any software at will.

It can’t get all the server-side code, data, and algorithms from the client program alone. And studying software through reverse engineering doesn’t mean you can freely copy protected code or get around software licensing.

The hype on GitHub says more about people’s interest in this capability. It doesn’t mean it’s been proven in large-scale real-world use.

V. Does everyone need REA?

Looking at real needs, it’s currently better suited to software developers, security researchers, and people who maintain legacy systems or study how software works internally.

As for me, even though I’m building some small products with coding agents, I don’t have any problem right now that calls for reverse engineering.

So for now, I won’t install it just to try out a trending project.

Still, looking into REA did give me a bit more understanding of where AI is heading.

What amazed me before was that, without writing code myself, I could turn ideas into products with AI.

REA shows another direction: investigating software internals, which used to take professional engineers, can now start to be partly handled by AI agents.

For people who need these capabilities, that’s a really valuable change.

For people who don’t need them yet, understanding what it can do, and where its limits are, is something worth taking away too.

There’s no need to rush to find a reason to use a tool just because it’s really popular.

Sources: REA official GitHub, REA Notion case study, GitTrend (2026.10.14)