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Fast, accurate offline swipe typing system and open models.
Decodes continuous touch swipe trajectories into accurate word predictions using lightweight neural network models. Runs dictionary-constrained beam searches via a high-performance C++ inference library. Operates completely offline on mobile devices to protect user typing privacy. Provides layout-agnostic and language-agnostic encoder features to support custom keyboards.
Utilizes an extremely small parameter footprint allowing models to execute on low-end mobile devices in milliseconds. Maintains a local-first offline architecture that prevents sensitive typing data from transmitting to the cloud. Combines a universal layout encoder with a layout-specific decoder to achieve high prediction accuracy. Releases models under permissive licenses with an open-source inference library and dataset.
Category: AI & Automation
Team Size: 11-25
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FUTO Swipe is an open-source family of neural models and algorithms designed to enable fast, accurate mobile swipe typing completely offline. Developed primarily for FUTO Keyboard, it solves the privacy and licensing limitations traditionally locked behind big tech or proprietary keyboard applications. The framework features an ultra-lightweight architecture using an encoder, context language model, and a C++ inference library to run efficiently on low-end devices.
FUTO Swipe was developed by the FUTO organization, founded by Louis Rossmann and based in Austin, Texas. FUTO focuses on building independent, user-respecting technology and open-source tools to give control back to individuals, creating FUTO Swipe to break the industry monopoly on high-performance mobile swipe typing.
