🔍 Read the full analysis: A Better Starting Point For Reverse Engineering With AI on ThorstenMeyerAI.com
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TL;DR
Reverse Engineer Anything (REA) connects AI coding agents to analysis tools for inspecting existing software, including binaries, apps and databases. Its maintainer’s published QuickBooks case study reports that a locally built accounting app matched selected statements against Intuit’s sample company, but the account is not independent verification of the broader results.
A report published October 19 describes how Reverse Engineer Anything (REA), an open-source toolkit, lets AI coding agents call software-analysis tools to investigate how existing programs behave. Its central example is a developer’s account of using REA to study QuickBooks Desktop and build a local accounting app whose selected financial statements reportedly matched Intuit’s sample company account by account, to the cent—a result presented in the project’s case study, not independently verified in the source.
REA is described as an MIT-licensed GitHub project that combines a local Model Context Protocol (MCP) server, a command-line utility and an agent skill that guides investigations. The report says it can connect agents including Claude Code, Codex, Cursor and Gemini CLI to analysis engines for software and data formats such as native programs, Electron and JavaScript apps, .NET and Android apps, firmware, saved network traffic, WebAssembly and SQLite databases. For websites, it can observe activity in a browser controlled by the user.
The proposed method is narrower than asking an AI to “figure out” an entire application. The report recommends choosing one behavior and one decision, then asking the agent to identify stored data, calculations and conditions that change the result. It suggests labeling findings as observed, inferred or unknown, and checking whether the agent can explain a concrete example with inputs and outputs. A description of a screen alone is not evidence of the logic behind it.
The QuickBooks example focuses on accounting rules, including inventory arithmetic, budget signs, reconciliation balances and cash-basis report paths. According to the report, its author used the QuickBooks Desktop for Mac 2024 trial, REA 6.4.0 and Ghidra. The source says the resulting app’s profit-and-loss statement, balance sheet and trial balance matched the official sample company. It does not provide an independent audit or enough detail here to assess how broadly that match was tested.
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 Screens to Business Rules
The example matters because recreating software from its visible interface can miss the rules that determine whether it works in real use. Accounting software, for instance, must handle how transactions affect balances and reports, not merely reproduce screens. REA’s stated role is to give an AI agent access to evidence from existing software so a developer can turn a particular behavior into a written specification.
That approach may help developers investigate legacy programs, document unfamiliar systems or build replacements where source code or documentation is limited. But REA does not, by itself, establish that an agent’s explanation is correct. Observed behavior, inferred logic and unanswered questions need to remain distinct, and results should be checked against concrete examples. The QuickBooks account is one reported case, not proof that the same process will reproduce other software reliably.
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How REA Connects to Agents
The report describes REA as a way to connect a coding agent with tools such as disassemblers and application analyzers. It says version 6.7.0 was released October 18, 2026, and that the project had about 325,000 stars and 82,000 forks on GitHub as of October 19. Those are figures reported by the source, not independently checked here. The source also says the project appeared on GitHub’s trending page in early October.
Setup is described as a guided process, with Node.js 22.19 or newer required. Native-code analysis may also require Ghidra and Java, or licensed software such as Hopper or IDA. The report advises restarting the coding agent and checking that REA tools are available before asking questions about a target. A setup check can confirm files and prerequisites, it says, but does not by itself prove the agent has a live connection.
That distinction is important: if an agent answers without calling an REA tool, its response may be based on model-generated guesses rather than an inspection of the program. The report recommends testing the connection and beginning with a harmless application the user owns.
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Limits of the QuickBooks Evidence
The account does not establish whether the reported QuickBooks matches were independently reproduced, how many cases were tested, or whether the new app matched behavior outside the selected statements and rules. It also does not provide the full case-study evidence in the supplied material. The reported match should therefore be treated as the author’s account, rather than a general measure of REA’s accuracy.
Other practical limits remain unclear, including how results vary across analysis tools and target programs. The source says REA runs locally, but also warns that analysis results go to the coding agent and its model provider, subject to that provider’s data policy. Local execution does not mean all investigation data stays on the machine. The report identifies the project’s maintainer as anonymous; it does not give further details about the project’s governance or support.
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Verify Before Building on Findings
The immediate next step for anyone trying REA is to verify the installation and tool connection, then test a narrowly framed question against software they own or are authorized to inspect. Developers should ask for a complete example, retain the distinction between observed and inferred claims, and compare the agent’s explanation with known outputs before using it as a specification.
Further evaluation would need to show whether the QuickBooks findings can be reproduced and whether the method holds across more applications and behaviors. The source report does not announce a new release beyond REA 6.7.0 or provide a roadmap. Until broader evidence is available, the case study is best read as an illustration of a possible workflow—not a guarantee that AI agents can accurately reconstruct software.
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Key Questions
What is Reverse Engineer Anything?
REA is an open-source toolkit that connects coding agents to software-analysis tools through a local MCP server, a command-line utility and an agent skill.
What did the QuickBooks case study report?
The report says its author investigated QuickBooks Desktop for Mac 2024 and built a local accounting app whose profit-and-loss statement, balance sheet and trial balance matched Intuit’s sample company account by account, to the cent. That result is reported by the case-study author and is not independently verified in the supplied material.
Does REA prove an AI agent’s explanation is correct?
No. REA provides access to analysis tools, but an agent’s conclusions still need checking. The report recommends separating observed, inferred and unknown statements and testing explanations against concrete inputs and outputs.
Does REA keep all analysis data local?
The analysis runs locally, according to the report, but results are passed to the agent and may also be handled by its model provider under that provider’s data policy. The project warns that REA is not a sandbox, so untrusted programs should be treated cautiously.
Source: ThorstenMeyerAI.com
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