AI App Store

The MLX Score

One number out of 5, built from real public data only. No invented ratings, no estimated user counts, no paid reviews.

What goes in

App Store rating
weight 35%
Apple's own average and count for the Mac App Store or App Store listing, read from Apple's public lookup. A rating from a handful of people counts for less than one from thousands.
Homebrew installs
weight 20%
Installs in the last 90 days from Homebrew's public analytics. The closest real measure of how many Macs install a tool.
GitHub stars
weight 20%
Stars on the project's repository, from GitHub's public API. A repository with no push in a year counts at half.
Hugging Face
weight 15%
Downloads in the last 30 days and likes, from the Hugging Face Hub. For models and their tools.
npm and PyPI
weight 10%
Downloads in the last 30 days, from npm and pypistats. For command-line tools and libraries.

How it's put together

Each signal a listing has is ranked against the same signal of the other listings in its category (or the whole store when fewer than four in the category have it). The ranks are averaged with the weights above, over the signals that listing actually has: a missing signal is left out, never guessed. The average maps to a score from 1.0 to 5.0, with one decimal.

Every listing shows each input under its score: the source, the value it reported and the day we read it. Sources are read again every few hours.

Ratings from MLX users who have the app installed (checked by MLX Node on their own Mac) will blend in as they come, weighted by how many there are. They're shown separately too.

What we never do: show a rating count we didn't read, present an estimate as a fact, write reviews ourselves or with AI, or pay for reviews. There's no paid placement, so nothing can buy a higher score. See the rule book.