[Roni Bandini]’s JEV WiFi Analyzer is an interesting experiment that glues together a single-board Linux computer, a USB WiFi adapter, and a specialized AI model to create a new kind of automated tool that passively monitors WiFi activity to measure and profile coverage, congestion, and suggest possible optimizations.
Here’s how it works: a Python program connects to WiFi, cycles through available channels, and uses scapy to gather details like signal strength, number of clients, WiFi frames per second, and similar data about each given channel. That data is fed to Jev, a highly efficient sort of decision-making AI model responsible for interpreting that data on an ongoing basis.
Briefly, Jev is a new kind of AI that is completely unable to generate text or images. It outputs only numbers representing bounded decisions. We covered these decision-making models earlier, including locally-installed versions. Jev is cheap enough to throw at a variety of problems, but it’s also practical to run your own model on your own hardware.
In [Roni]’s analyzer, Python gathers the raw numbers and Jev takes care of classifying the WiFi network’s coverage, congestion level, and basic remedial actions if applicable. There’s a brief video embedded below (Spanish only) that shows the Jev WiFi Analyzer in action.
The AI isn’t making particularly deep analyses, but it doesn’t take much to imagine Jev taking a more active role like deciding which channels to spend more (or less) time monitoring. Using a decision-making model in this way is interesting, and makes us wonder if similar models integrated with other kinds of hardware is a branch of projects waiting to happen.