🔍 Read the full analysis: Use An AI Agent To Map Out An Unfamiliar Software Project on ThorstenMeyerAI.com
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TL;DR
A ThorstenMeyerAI.com report published October 19 describes using the open-source Reverse Engineer Anything (REA) toolkit with AI coding agents to investigate unfamiliar software. Its examples include a QuickBooks Desktop accounting project whose author says selected financial statements matched the original sample company to the cent. That result is a reported case study, not an independent evaluation of REA.
ThorstenMeyerAI.com reported October 19 that developers can use the open-source Reverse Engineer Anything (REA) toolkit to help AI coding agents inspect unfamiliar software and document how particular features work. The report describes five investigations, including a developer’s reconstruction of accounting behavior in QuickBooks Desktop, and stresses that an agent’s findings need verification rather than being treated as fact.
REA connects coding agents, including Claude Code, Codex, Cursor and Gemini CLI, to software-analysis tools through a local server using the Model Context Protocol, or MCP. It also includes a command-line utility and an agent skill that guides investigations. The project supports analysis of several kinds of software and files, including JavaScript and Electron apps, .NET and Android applications, databases, firmware and native programs handled through tools such as Ghidra, Hopper or IDA, according to the report.
The article’s central example is Ledger Reborn, a local accounting application developed after its creator investigated QuickBooks Desktop for Mac 2024. The source says the developer used REA 6.4.0 and Ghidra to examine selected behavior, including inventory calculations, account-type effects on budget figures, reconciliation balances and cash-basis reporting. The developer reported that the new app’s profit-and-loss statement, balance sheet and trial balance matched Intuit’s official sample company account by account, to the cent. That is the case-study author’s reported result; the supplied material does not provide an independent audit.
The report recommends investigating one defined behavior at a time, rather than asking an agent to explain an entire program. A useful question identifies the feature, the decision the developer needs to make, the data involved and the conditions that could change the result. The agent should distinguish what it observed from what it inferred or could not establish, then demonstrate its answer with a concrete example.
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 Working Specs
For developers maintaining an old application or building a compatible replacement, knowing what a screen looks like is not enough. The underlying rules may depend on stored records, calculations and branching conditions that are not obvious from ordinary use. REA’s proposed value is to let an agent gather evidence about those mechanics, giving a developer a basis for a written specification before implementation begins.
The accounting example shows why that distinction matters: a feature can appear correct in a demonstration while producing different balances in real cases. A repeatable example with inputs and outputs offers a practical check on an agent’s explanation. It does not, by itself, establish that every behavior has been found or that a replacement is fully equivalent to the original.
There is also a data and security trade-off. REA runs analysis locally, according to the report, but findings are passed to the connected AI agent and may also be handled by the model provider under its own data policy. The project warns that it is not a sandbox; opening an untrusted program exposes it to analysis tools operating with the user’s permissions.
software reverse engineering toolkit
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How REA Connects Agents to Analysis
The source describes REA as an MIT-licensed open-source project on GitHub. It reported about 325,000 stars and 82,000 forks on October 19, following a period on GitHub’s trending page in early October. These figures are time-stamped observations from the source, not measures of technical accuracy or adoption. The reported current version was 6.7.0, released October 18; the project’s maintainer was described as anonymous.
Setup depends on the user’s environment. The report says guided installation requires Node.js 22.19 or newer, while native analysis may also require a disassembler such as Ghidra and its Java dependency, or licensed software such as Hopper or IDA. REA’s diagnostic command checks files and prerequisites, the article notes, but does not prove that an agent’s live connection is working. The report recommends verifying the connection from inside the agent before relying on its findings.
The article frames the Ledger Reborn work as an example of using analysis to turn observed behavior into implementation decisions, rather than as a reason to copy a program wholesale. It also points readers to the case study for its detailed account of the QuickBooks investigation. The material supplied here does not include the complete case study or independent testing of its results.
“REA analyses targets on your machine. Your agent receives the results, though, and so does the model provider behind it, under its own data policy.”
— ThorstenMeyerAI.com report
Limits of the Reported Evidence
The supplied source describes a developer’s results, but does not include an independent review of the Ledger Reborn comparison, test data beyond the official sample company, or a complete account of which accounting cases were checked. It therefore cannot establish how broadly the reported match applies or whether other parts of QuickBooks behave the same way.
More generally, the report does not quantify REA’s accuracy across supported software types or explain how often agent-generated conclusions need correction. An analysis can mix direct observations with inferences, and the report’s own recommended safeguard is to label those categories and test a complete example. The project’s popularity figures also do not establish reliability.
Security and privacy depend on how the tool, agent and model provider are configured. The source says analysis runs locally but that results reach the agent and may reach its provider; it does not specify a single data-retention policy that applies across providers. Developers should check the terms for the agent they use and avoid exposing sensitive or untrusted files without an appropriate review.
Verify the Connection, Then Test
The report’s next step for a developer is to confirm that REA is actually available to the chosen agent, rather than assuming installation succeeded. Its diagnostic command checks local files and prerequisites; the developer should also ask the running agent to call an REA tool and confirm that it returns results. The article cautions that an agent answering without calling REA may simply be guessing from its existing knowledge.
After connection checks, the recommended workflow is to start with a harmless application the developer owns, then choose one behavior with a clear purpose. The investigation should record the relevant data, calculation and conditions, and test the explanation against a concrete input and output. Developers considering a replacement can then use the verified findings as a specification, while marking unresolved behavior as unknown rather than filling gaps with assumptions.
Key Questions
What is Reverse Engineer Anything?
REA is an open-source toolkit that connects AI coding agents to software-analysis tools. The source describes it as a local MCP server, a command-line tool and an agent skill for guiding investigations.
Does REA prove that an AI agent’s explanation is correct?
No. The report recommends separating observed, inferred and unknown information and checking whether the agent can explain a concrete example with real inputs and outputs. It does not present a general accuracy evaluation of REA.
Did Ledger Reborn match QuickBooks?
The source says its developer reported that three statements for Intuit’s official sample company—a profit-and-loss statement, balance sheet and trial balance—matched account by account, to the cent. The supplied material does not include an independent audit of that result.
Does REA keep all analysis data on the developer’s computer?
The report says REA runs analysis locally, but results are sent to the connected agent and may also be handled by the model provider under that provider’s data policy. Local analysis alone does not mean all information stays on the device.
What should a developer check before using REA?
Check the required software and analysis tools, confirm the live agent connection, and begin with a harmless program you own. The project warns that REA is not a sandbox, so untrusted programs require particular care.
Source: ThorstenMeyerAI.com
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