🔍 Read the full analysis: Understanding Existing Software: A Hands-On AI Approach on ThorstenMeyerAI.com
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TL;DR
Thorsten Meyer’s October 19, 2026 report examines Reverse Engineer Anything (REA), an open-source toolkit that lets AI coding agents investigate existing programs. A case study describes matching QuickBooks Desktop sample-company financial statements in a locally built accounting app, but the reported result is not an independent validation of REA or a guarantee that agents will produce accurate answers.
A report published by ThorstenMeyerAI.com on October 19 describes how the open-source tool Reverse Engineer Anything (REA) can help AI coding agents inspect existing software and explain specific behaviors. Its central example is a developer’s local accounting app whose financial statements reportedly matched those of QuickBooks Desktop’s official sample company account by account, to the cent—a result that illustrates the approach but is reported by the project’s author, not independently verified in the supplied material.
REA connects tools for software analysis to coding agents including Claude Code, Codex, Cursor and Gemini CLI. The report describes three components: a local MCP server agents can call, a command-line program for setup and analysis, and an agent skill that guides investigations. Its analysis tools cover several kinds of software and data, including native applications, Electron and JavaScript programs, .NET and Android apps, firmware, saved network traffic, WebAssembly and SQLite databases. For websites, it can observe activity in a Chrome-family browser controlled by the user.
The report argues that an investigation works best when it targets one behavior and a practical decision, rather than asking an agent to explain an entire program. For example, a developer might ask how a partial invoice payment changes the remaining balance and which account receives the posting. The agent should report stored data, calculations and conditions, and distinguish what it directly observed from what it inferred or could not establish. A concrete example with inputs and outputs gives the user a way to check whether the explanation is adequate.
Setup does not prove that an agent is connected correctly. The article 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 files and prerequisites, according to the report, but not whether the live agent connection works. The author recommends checking inside the agent that an REA tool can be called before relying on its answers.
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 Specs
Understanding existing software can matter when a team is replacing a legacy program, building an integration or implementing a familiar business rule in a new system. A screen can show what a program does in one situation without revealing the records, calculations and conditional paths that determine what happens in others. REA’s reported use is to help an agent gather evidence about those mechanics, giving developers material to test and turn into a specification.
The QuickBooks example is relevant because accounting outputs depend on more than interface resemblance: inventory arithmetic, account types, reconciliation and reporting rules can all affect results. Meyer says the locally built app matched the official sample company’s profit-and-loss statement, balance sheet and trial balance account by account, to the cent. That is a specific reported outcome, not proof that the same process will reproduce every QuickBooks feature or handle all real-world accounts correctly.
There is also a data and security consideration. Although analysis runs locally, the report says the results are sent to the agent and may also reach the model provider under that provider’s data policy. REA is not a sandbox: opening an untrusted program exposes it to analysis tools running with the user’s permissions. Users need to weigh that access alongside the usefulness of the analysis.
software reverse engineering toolkit
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How the QuickBooks Case Was Built
The article identifies REA as an MIT-licensed open-source project on GitHub, maintained under the name morluto/rea. It reports that version 6.7.0 was released on October 18, 2026, and that the project had about 325,000 stars and 82,000 forks on October 19. Those figures are observations reported by the source; they do not by themselves establish how many people use the tool or whether its analyses are accurate.
The accounting example, called Ledger Reborn, examined the QuickBooks Desktop for Mac 2024 trial, according to the case study described by Meyer. The investigation used REA 6.4.0 and Ghidra and focused on a limited set of behaviors: inventory arithmetic, how account types affect budget number signs, reconciliation-balance paths and cash-basis report branches. This scope matters: the reported match concerns specified financial statements for an official sample company, not a complete recreation of the software.
Meyer frames the work as turning observations about a program into decisions a developer can document. In this approach, the agent’s explanation is a starting point for a testable specification, not a substitute for verification. The report recommends asking for a complete example and checking whether the tool was actually called, rather than accepting an answer that merely describes what appears on screen.
“This is not a sandbox.”
— Reverse Engineer Anything project, as quoted in Thorsten Meyer’s report
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Limits of the Reported Match
The supplied material does not include an independent reproduction of the QuickBooks statement match, detailed test results or a full account of what the local app does and does not support. It also does not establish that REA can reliably recover every business rule from every program. The reported result should be read as a case study by the article’s author, not as a general performance finding.
It is unclear from the source how well the tool handles programs whose behavior depends on external services, unusual data or features not covered by the investigations described. The source also does not provide enough detail to complete the case study’s quoted description of REA’s role; the supplied quotation ends mid-sentence. Project popularity figures and the release date are reported, but no independent usage or accuracy measurements are provided.
Privacy and safety depend in part on the setup. The report says processing takes place locally, while agent outputs are shared with the agent and potentially its model provider. The applicable provider data policies, and the risks for any specific target program, are not detailed in the material. Users should not treat local analysis as a guarantee that data stays on their machine.
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Verify Each Investigation
The practical next step described in the report is to check setup, confirm the agent can call REA, and begin with a harmless program the user owns. The investigation should then be narrowed to one behavior, with a decision it will inform and a request for an example that can be checked. Users can compare the agent’s explanation against observed inputs and outputs and mark conclusions as observed, inferred or unknown.
The source does not announce a future release, an independent audit or a broader validation of the QuickBooks case. Readers evaluating the tool should check the project’s current documentation and version information before setup, since software requirements and supported tools can change. For any implementation based on an investigation, the reported approach points toward validating the resulting rules against relevant test cases before relying on them.
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Key Questions
What is Reverse Engineer Anything?
REA is an open-source toolkit that connects software-analysis tools to AI coding agents. The report describes a local MCP server, a command-line tool and an agent skill for guiding investigations.
What did the QuickBooks example reportedly achieve?
Meyer says a developer built a local accounting app whose profit-and-loss statement, balance sheet and trial balance for QuickBooks Desktop’s official sample company matched account by account, to the cent. The source provides this as a case study, not an independently verified benchmark.
Does REA verify an agent’s answer automatically?
No such guarantee is established in the report. It recommends asking about a specific behavior, checking that the agent actually called an REA tool, and testing the explanation against a concrete example.
Does local analysis mean data stays local?
Not necessarily. The report says REA runs analyses locally, but results go to the agent and may also go to its model provider under that provider’s data policy.
Is it safe to analyze an unknown program?
The source 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.
Source: ThorstenMeyerAI.com
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