A GitHub project claiming to use WiFi signals to see through walls rocketed to the top of trending repositories this week. Engineers who actually looked at the code say it's a complete scam with no working technology. This is what I live for—calling out vaporware before people waste time on it.
The repository, called RuView, claims to leverage WiFi signal interference patterns to create visual representations of objects behind walls. The demo videos look impressive. The technical explanation sounds plausible. The code is garbage.
What WiFi Sensing Can Actually Do
To be clear: WiFi sensing is real technology. Researchers have demonstrated that by analyzing how WiFi signals bounce off objects, you can detect motion, identify activities, and even estimate body posture. The physics is sound—WiFi signals do interact with physical objects, and those interactions create measurable patterns.
But there's a massive gulf between detecting motion through walls and creating visual images. Legitimate WiFi sensing research uses sophisticated signal processing, machine learning models trained on thousands of examples, and expensive antenna arrays. The results are impressive but limited: you can tell that someone is moving in the next room, maybe identify whether they're walking or sitting, but you're not getting anything close to a visual image.
RuView claims to do all of this with consumer hardware and a few hundred lines of code. That's the first red flag.
What Engineers Found (Or Didn't Find)
Several developers dug into the RuView codebase and found exactly what you'd expect from vaporware: a lot of impressive-sounding function names, almost no actual implementation, and demo videos that were clearly created with completely different technology.
The signal processing code that should be the core of the system? Missing. The machine learning models that would be needed to interpret WiFi interference patterns? Not there. The actual algorithms that would turn signal data into visual representations? Nonexistent.
What the code does include: some basic WiFi scanning functionality that you could pull from a dozen existing libraries, placeholder functions that return random data, and a UI that displays pre-rendered videos disguised as real-time output.
In other words, it's a fake. A well-marketed fake, but a fake nonetheless.
