🔍 Read the full analysis: How To Explore A Software Project Before You Know Its Code on ThorstenMeyerAI.com
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TL;DR
A report published by ThorstenMeyerAI.com on Oct. 19 describes how Reverse Engineer Anything (REA) can help AI coding agents investigate the behavior of existing software. Its examples stress asking narrow questions, checking that REA is connected, and verifying findings with concrete inputs and outputs; the article also warns that analysis results go to the model provider and that REA is not a sandbox.
ThorstenMeyerAI.com published a guide on Oct. 19 to using Reverse Engineer Anything (REA) with AI coding agents to investigate how existing software behaves before building a compatible or replacement application. The report argues that focused investigations—such as tracing one accounting calculation—can produce more useful evidence than asking an agent to explain an entire program, while warning that findings must be checked and that REA is not a security sandbox.
REA is described as an MIT-licensed open-source project available on GitHub. It combines a local Model Context Protocol (MCP) server, a command-line tool and an agent skill that guides investigations. The report says it can work with several kinds of software and data, including native applications, Electron and JavaScript apps, .NET and Android programs, firmware, saved network traffic, WebAssembly and SQLite databases. For websites, it can observe activity in a Chrome-family browser controlled by the user.
The guide recommends starting with one specific behavior and a practical decision. Instead of asking an agent to reverse engineer accounting software generally, a developer could ask how a partial invoice payment affects the remaining balance and which account receives the posting. The proposed investigation should identify stored data, calculations and conditions that change the result, while marking statements as observed, inferred or unknown. A useful test is whether the agent can explain a complete example using actual inputs and outputs.
Setup is not proof that the tools work. The source says REA requires Node.js 22.19 or newer; native analysis may also require software such as Ghidra and Java, or a licensed disassembler. Its diagnostic command checks installation files and prerequisites, but, according to the guide, does not verify a live connection to the agent. The report recommends confirming that the agent can call an REA tool before trusting its explanation.
Infographic 01 · Reverse Engineer Anything
Five places to point REA first, and how to tell when the answer is wrong.
REA gives coding agents such as Claude Code and Codex tools to inspect programs, apps and browser sessions. Each investigation needs a finishable question and a check.
npx rea-agents@latest setupThe five investigations
What to ask, and the check that catches a wrong answer.
- 1
Ask a question you can finish
One behaviour, one decision it informs. Example: how a partial payment changes an invoice balance.
It’s wrong ifThe agent can't walk through one concrete example with real inputs and outputs. - 2
Get REA connected and confirmed
Run the guided setup, then doctor, then call binary_session inside the agent before any real target.
It’s wrong ifAnswers arrive without a single REA tool call: you're reading the model's guesses. - 3
Turn a business rule into a spec
Inputs, stored fields, formula, edge cases, plus test cases checked against the original's own output.
It’s wrong ifYour version and the original disagree on a test case, even by one cent. - 4
See what has to survive a copy
Find what an app writes to the clipboard and what it keeps locally, then design your own copy and paste.
It’s wrong ifThe explanation doesn't predict what lands in a plain text editor, an email and your app. - 5
Your own app, your equipment, an old program
Start with software you own or rely on. Ask what it sends, stores or calculates when you do one thing.
It’s wrong ifIt only works for the example you watched. Test with a new file or a read-only command first.
Sources: REA on GitHub (stars via GitHub API, 19 Oct 2026); REA installation guide; REA security policy.
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Why Behavior Checks Matter
For developers recreating or replacing a program, a similar-looking interface does not establish that the underlying rules match. Calculations, stored records and conditional behavior determine whether an application produces the right outcomes in real use. REA’s approach, as presented in the report, is to use analysis tools to gather evidence about those mechanisms and then turn the evidence into implementation decisions.
The article’s central example is Ledger Reborn, a local accounting application whose developer investigated QuickBooks Desktop. The source says the replacement’s profit-and-loss statement, balance sheet and trial balance matched Intuit’s official sample company account by account, to the cent. That is a reported result for this project, not independent proof that REA will produce equivalent accuracy in other investigations. The distinction matters: an agent’s confident explanation is not itself validation; developers need reproducible examples and comparisons against the original program.
The method also changes how teams can approach unfamiliar software. A question with a clear stopping point can help developers determine what they have actually observed and where assumptions remain. That is relevant when building a replacement, documenting a business rule or deciding whether an existing tool fits a workflow. It does not remove the need for human review, especially where financial calculations or other consequential behavior is involved.
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From QuickBooks to REA
The guide places Ledger Reborn at the center of its case for REA. Its author says the developer examined the QuickBooks Desktop for Mac 2024 trial with REA 6.4.0 and Ghidra. The investigation focused on specific accounting behavior: inventory arithmetic, how account types affect signs in budgets, paths behind reconciliation balances and branches used for cash-basis reports. The guide characterizes the work as an effort to understand and implement rules, rather than simply copy screens.
The report describes REA as a project that drew attention after appearing on GitHub’s trending page in early October. It gives figures of about 325,000 stars and 82,000 forks as of Oct. 19, and identifies version 6.7.0 as released the day before. These are source-reported project figures, not independently verified here. The report also says the maintainer is anonymous.
REA’s local operation does not mean all information stays on the user’s device. The source says analysis runs locally, but the agent receives the results and the model provider may also receive them under its own data policy. It quotes the project’s warning: “This is not a sandbox.” Opening an unknown program gives analysis tools running with the user’s permissions access to it, so the guide advises treating unfamiliar binaries as untrusted files.
“The docs are explicit that doctor checks files and prerequisites, “not the live agent connection.””
— ThorstenMeyerAI.com’s guide
Limits of the Reported Results
The source presents the Ledger Reborn comparison as a case study, but does not provide an independent audit, full test methodology or evidence that the results generalize to other software. It also does not establish that REA can recover every relevant behavior from every target, or that an AI agent will interpret analysis results correctly. The guide itself recommends marking findings as observed, inferred or unknown and checking whether an agent can trace a concrete example.
Security and privacy depend on the target and tools used. The source says REA analyzes locally, while results may be sent to a model provider; it does not specify a single data-handling policy for every provider. Users must check the applicable service terms and avoid assuming that local analysis means confidential processing. The report also does not establish that running analysis on an unknown binary is safe.
Verify Before Building
The report’s next step for readers is to establish a working connection before asking substantive questions: install REA, restart the coding agent, run the diagnostic, and then confirm that the agent can call an REA tool. It recommends beginning with a harmless application the user owns. After that, developers can investigate one behavior at a time and compare the agent’s explanation with a real example from the original software.
For anyone applying the method to accounting or other high-impact logic, the practical next milestone is a documented test case: record the inputs, observed outputs, conditions and unresolved assumptions, then test the replacement against the original. The source does not announce a new release beyond its statement that REA 6.7.0 was released Oct. 18, or specify a further product milestone. Whether REA’s results hold across other programs remains to be tested by users and independent evaluators.
Key Questions
What is REA?
Reverse Engineer Anything is an open-source toolkit described as connecting software-analysis tools to AI coding agents through an MCP server, command-line utility and agent skill.
Does REA automatically explain an entire program?
The guide recommends against broad requests. It says to investigate one behavior at a time, specify the decision the answer will inform, and ask for stored data, calculations and conditions that affect the result.
How can a developer tell whether REA is connected?
The source says the diagnostic checks files and prerequisites, not the live agent connection. It recommends confirming within the agent that an REA tool can be called before relying on answers about a target.
Does running REA locally keep results private?
Not necessarily. The report says analysis runs locally, but the agent receives the results and the model provider may receive them under its own data policy. Users should check that policy before analyzing sensitive software.
Is it safe to analyze an unfamiliar program?
The source warns that REA is not a sandbox: its analysis tools run with the user’s permissions. It advises treating unknown binaries as untrusted files.
Source: ThorstenMeyerAI.com
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