# DIY WiFi Analyzer Sniffs Out Congestion and Coverage Issues, Suggests Fixes > Source: > Published: 2026-10-31 17:00:53+00:00 [Roni Bandini]’s [JEV WiFi Analyzer](https://github.com/ronibandini/JevWiFiAnalyzer-) 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](https://hackaday.com/wp-content/uploads/2026/10/JEV-WiFi-Analyzer-1-thumb.png) [scapy](https://scapy.net/) 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](https://hackaday.com/2026/10/06/a-new-type-of-llm-on-the-block-decision-making-models/), 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.