TigerSystem

TigerSystem in 30 seconds

Every 3D-printing shelf hides the same mystery: twenty spools, half of them unlabeled, none of them able to say what they are, how much is left, or how they like to be printed.

TigerSystem fixes that with one small chip. Stick a TigerTag NFC chip on a spool and the spool introduces itself — to your phone, to your computer, to your printer: “I’m matte black PLA from brand X, 1.75 mm, print me at 210 °C, and there’s 640 g of me left.”

Printer manufacturers tag their spools too — but locked, in secret formats, working only with their own machines. TigerSystem flips it: the chip is open and readable by anything, the data belongs to your account, and every piece — apps, cloud, scale, reader, the chip spec itself — is published for anyone to use or build on. It is a format, not a walled garden.

More than 2.5 million chips have been produced and ship from the factory in filament by brands like Rosa3D, eSun, Sunlu and R3D, which makes TigerTag the most deployed third-party RFID protocol in the world.

Ready to tryMake a spool smart in five minutes — a blank NTAG chip, your phone, the free app. No account, fully offline. A userWhat each product actually does for you — chips, apps, the scale, the reader stand. A printer ownerDoes it work with your machine? Six brands are live, and the chip works with all the others. A developerRead and write chips from JavaScript or Python, or plug the identity into your own software. A filament manufacturerFactory integration in days, about one second per chip, at production scale. Just curiousThe questions everyone asks, answered — chips, cloud, printers, privacy, the lot.

Not a mockup: the sandbox on a real bench — Studio, TigerScale, TD1s and TigerPOD working together.

Where the truth lives

This wiki is rendered from the TigerSystem-Docs repository, which is the ecosystem’s source of truth. Every page here has an “Edit page” link that takes you straight to the Markdown file behind it — corrections travel as pull requests, and where another surface disagrees with the repository, the repository wins.

AI assistants should start at llms.txt, the condensed canonical explainer. Everyone else can start with the glossary if a term gets in the way.