Before you install an AI tool, read its GitHub page: a 15-minute checklist for non-coders
Every week a new AI tool gets shared with "install this and your work gets 10x faster". Some are excellent. Some are a fresh GitHub repo with a nice logo and no one behind it. You do not need to read code to tell the difference. You need to read the page. Here is the check I would run before a tool touches your files, your email or your accounts. It takes about 15 minutes. 1. Who made it? Start…
With the rise of AI tools, it's easy to be swayed by flashy logos and promises of increased productivity. However, before installing any new tool, it is crucial to conduct a quick 15-minute checklist to ensure its legitimacy and reliability. Here's what you need to do:
1. Identify the creator: Begin by examining the repository owner. Is it a reputable company or an established maintainer, or is it a newly created account? Verify the website link; if it directs you to a different repository than the one provided, cease the process immediately. Shared links can often be deceptive, and fake download buttons are a common scam.
2. Check for recent updates: Examine the date of the most recent commit and the Releases tab. Even if a tool appears functional, a stagnant development cycle may indicate a lack of support. Look for a consistent flow of minor updates rather than a single major release followed by silence.
3. Read the README file diligently: Ignore the initial hype and focus on three key aspects: the tool's functionality, its requirements (such as accounts, API keys, servers, or paid services), and the permissions it seeks to access. If a simple task necessitates multiple API keys or excessive permissions, question the necessity of such a tool. If the permissions seem excessive for the task at hand, reconsider your choice.
4. Verify the licensing: The License file, often overlooked, determines whether you can use the tool for commercial purposes or solely for personal experimentation. If no license is present, refrain from assuming commercial rights. Many individuals neglect this step, only discovering the issue when a client raises concerns.
5. Review the Issues tab: Examine both open and closed issues. Are there genuine problems reported by users? Does the maintainer respond to inquiries? A plethora of unanswered reports concerning data loss serves as a clear warning, even if the underlying code remains unclear.
6. Conduct a test run: Before entrusting the tool with critical work, subject it to a test run using dummy files, folders, or accounts. Observe its behavior, noting any creations, deletions, or emails sent. Begin with a small, unimportant task to ensure the tool operates as intended.
By following these steps, you can safeguard your files, emails, and accounts from potentially harmful AI tools. While understanding the code is not mandatory, slowing down before installation and dedicating fifteen minutes to this checklist significantly enhances the safety of your digital assets. As Devesh Sarkaar, founder of Aivesh Academy and Gharleads, emphasizes, the critical questions to answer are the creator's identity, the tool's maintenance status, and the scope of access it requires.
With this approach, you can navigate the AI landscape with confidence, ensuring that only trustworthy tools are integrated into your workflow.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
