How to evaluate an AI tool before trusting it (what the REA case teaches)
Carlos Imlau, AI Product Builder · Certified trainer (CCP) · AI applied to work ·
Summary
To evaluate an AI tool, read the official source, confirm the repository, look for independent tests and try it on a throwaway project. GitHub stars measure attention, not quality.
- REA went from 3 points on Hacker News to 250,000 stars between July and October
- Famous tools attract fake copies with malware
- Test without real data or access to your accounts
- In a team, decide who can install what
How do you know whether an AI tool can be trusted? Not by its fame. Read the documentation and warnings at the official source, confirm you are on the right repository, look for independent tests, try it on a throwaway project with no real data, check that the use is allowed and measure the result on a case of your own. GitHub stars measure attention, not quality.
I build software with AI every day and test new tools often. The REA case shows why this care matters.
What happened with REA?
REA (Reverse Engineer Anything) is a reverse engineering tool built on AI agents. It went from 3 points on Hacker News in July to 250,000 stars on GitHub in October. Online, people said it "clones any app".
I went through the documents, the code and the independent tests. What I found is in the video: what the tool really does, a sentence that was removed from the README, a fake copy with a trojan going around, and whether that use is even allowed.
How to evaluate an AI tool in 6 steps
- Read the README and warnings at the official source. What a tool promises in its documentation is usually far more modest than on social media. Read the legal and security notices too.
- Confirm you are on the right repository. Famous tools attract fake copies, and some carry malware. Reach the project through the author's official site or profile, not a random link.
- Look for independent tests, not demos. A demo shows the best case. An independent test shows the normal case.
- Test on a throwaway project. No real data, no access to your accounts, ideally in a separate folder or machine.
- Check that the use is allowed. Read the tool's licence and the terms of the service you plan to use it with.
- Measure on your own case. The question is not "is it impressive?" but "does it solve my problem better than what I already use?".
Why do stars and hype mislead?
Because they measure how many people saw it, not how many used it successfully. A tool can gain thousands of stars in a weekend thanks to a bold sentence, a viral video or a promise its own documentation does not back up.
What does this change for a company?
In a team the risk multiplies: one person installing a fake copy or pasting customer data into a dubious tool is enough. A "who can install what" rule should exist before the first tool. It is one of the topics in my AI training for teams.
Frequently asked questions
Is an open source tool always safe?
No. Open code lets someone check it, but it does not guarantee that anyone did. Fake copies can be open source too.
How do I spot a fake repository?
Be wary of repositories with look-alike names, few contributors, executable files to download and links that do not come from the author's official profile.
Should I avoid new tools?
No. Test them carefully: always on a separate project, before taking any of them into real work.
Where can I see more analyses like this?
On my YouTube channel (in Portuguese) and on the blog.