🧠Magika
Fast and accurate AI powered file content types detection
⭐ Very popular: 18k stars, gaining about 78 a week
View on GitHub ↗repo profile
momentum
durability
bus factor = how many people it takes to cover more than half the commits (6 months). 1 is a solo project; higher means the work is spread across a team. top-author share is the single busiest author's slice of those commits.
since we covered it
why it's a big deal
- Replaces rule-based detection with learned classification, improving reliability across diverse file formats.
- Already deployed at massive scale, showing AI classification is now core infrastructure.
- Forms a foundational layer for AI systems to continuously interpret raw data.
under the hood
- Lightweight model with millisecond inference on CPU.
- Uses partial file content for near-constant inference time.
- Available across multiple runtimes including Python, Rust, and JavaScript.
our take from PR#32, 2026-04-22
star history
- PR#32 13k 2026-04-22
- now 18k + 5k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Better known than its recent output, coasting a little on attention.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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AI-powered file classification at scale
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