🔍 Read the full analysis: Reverse Engineer Software You Didn’t Write With These AI Steps on ThorstenMeyerAI.com
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TL;DR
A report published by ThorstenMeyerAI.com on October 19 describes Reverse Engineer Anything (REA), an open-source toolkit that connects AI coding agents to software-analysis tools. The report highlights a QuickBooks Desktop case study but does not independently verify its results; it also stresses that agent output needs evidence checks and that REA is not a security sandbox.
REA is described as a local MCP server, command-line tool and agent skill that lets agents such as Claude Code, Codex, Cursor and Gemini CLI call analysis tools. The source says it supports a range of targets, including native applications, Electron and JavaScript apps, .NET and Android software, firmware, saved network traffic, WebAssembly, SQLite databases and older DOS code. It can also observe activity in a browser controlled by the user.
The report says version 6.7.0 was released October 18 and that the project is MIT-licensed and hosted on GitHub. It recommends defining a narrow investigation rather than asking an agent to explain an entire program: specify one behavior, the decision the answer will inform, and the evidence needed. A useful result should distinguish direct observations from inferences and unknowns, then walk through a concrete example with inputs and outputs.
The article also sets out setup and checking steps. It says REA requires Node.js 22.19 or newer; native software analysis may also require a disassembler such as Ghidra, or licensed software such as Hopper or IDA. A supplied diagnostic can check files and prerequisites, but the report says that does not prove the agent is connected live. Users should also check that the agent actually calls an REA tool before trusting its account of a target.
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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From Software Behavior to Specifications
The method matters to developers trying to build compatible tools or replace software whose internal rules are not documented. A screen can show what a program displays without revealing which records it stores, how it calculates a value, or which conditions change the result. Establishing those rules can help turn observations into a testable specification instead of relying on an AI-generated imitation based on appearances.
That distinction is especially relevant for accounting software, where small differences in inventory arithmetic, account treatment or report logic can change financial statements. The source’s QuickBooks example suggests a way to test a reconstructed application against a known sample company, but the reported matching results should be treated as a case-study claim, not proof that REA or an AI agent will reproduce other programs reliably.
There are also practical limits. The report says REA runs analysis locally, but results are passed to the coding agent and may also reach its model provider under that provider’s data policies. It warns that REA is not a sandbox: opening an untrusted program exposes it to analysis tools running with the user’s permissions.
software reverse engineering tools
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How the QuickBooks Case Was Tested
The report identifies Ledger Reborn as a local accounting application built after its developer investigated QuickBooks Desktop. According to the source, the developer used the official QuickBooks Desktop for Mac 2024 trial, REA version 6.4.0 and Ghidra. The investigation focused on specific mechanics, including inventory calculations, how account types affect budget signs, reconciliation balances and branches used in cash-basis reports.
The article says Ledger Reborn’s profit-and-loss statement, balance sheet and trial balance matched QuickBooks account by account, to the cent, for Intuit’s official sample company. That outcome is attributed to the project and the source’s account of its case study. The material supplied here does not include independent test results or enough detail to assess the full range of transactions covered.
REA’s described workflow combines analysis engines with an agent’s ability to call tools. The report recommends checking the connection before investigating a real target and beginning with a harmless application the user owns. It also cautions that an agent may generate plausible explanations without using REA, in which case those explanations may be guesses rather than findings from the target.
“This is not a sandbox.”
— Reverse Engineer Anything project, as quoted in the report
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Limits of the Reported Results
The supplied material is a report about REA and one project, not an independent evaluation of the tool. It does not provide a reproducible test suite, detailed transaction coverage or outside confirmation of Ledger Reborn’s reported cent-level matches. It is also unclear how well the same workflow performs across other software types, versions or unusual cases.
The source says REA was trending on GitHub in early October and gives approximate figures of about 325,000 stars and 82,000 forks as of October 19. Those counts are observations reported by the source; no comparison baseline or measurement method is provided. The maintainer is described as anonymous. The supplied case-study quotation is truncated, and no further statements from the developer or project maintainer are included.
Security and privacy depend on the target, tools and agent configuration. The report says analysis runs locally but results go to the agent and potentially its model provider. It does not specify what data each provider retains or how the risks differ by setup, so users would need to consult the policies for the services they use.
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Verify the Agent Before Analysis
For developers following the described approach, the immediate next step is to install REA through its guided setup, review the proposed changes and confirm that the chosen agent can call its tools. The report recommends checking prerequisites with the project’s diagnostic, then separately verifying the live connection inside the agent. A first inspection should use a harmless program the user owns.
Any investigation should remain narrow and produce a testable example: the input, observed output, relevant stored data and conditions that alter the outcome. Developers can then compare the explanation with behavior in the original program and record which points remain inferred or unknown. The next meaningful evidence would be more detailed, independently repeatable tests of Ledger Reborn and additional examples showing where REA’s findings hold up or fail.
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Key Questions
What is Reverse Engineer Anything?
REA is an open-source toolkit described in the report as a local MCP server, command-line utility and agent skill. It connects compatible AI coding agents to software-analysis tools.
Does REA prove that an AI has understood a program?
No. The report recommends checking that the agent actually calls REA tools and can explain a concrete example with inputs and outputs. An answer produced without tool use may be a model’s guess.
Did Ledger Reborn match QuickBooks?
The source reports that Ledger Reborn matched three financial statements to QuickBooks account by account, to the cent, for Intuit’s official sample company. This is a claim presented in the case study; the supplied material does not provide independent verification.
Is it safe to analyze an unfamiliar program with REA?
The report warns that REA is not a sandbox and that analysis tools run with the user’s permissions. It advises treating unknown binaries as untrusted files. It also says results are passed to the coding agent and may be handled by the model provider under its own data policy.
What does REA require?
According to the report, REA needs Node.js 22.19 or newer. Native program analysis may also require a disassembler such as Ghidra, which requires Java, or licensed tools such as Hopper or IDA. Requirements vary with the target and analysis method.
Source: ThorstenMeyerAI.com
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