FUTO Swipe is a family of small, open models for accurate swipe typing. It includes a layout-agnostic encoder, a layout-specific decoder, and a lightweight context language model. The full system runs efficiently on-device with a very small footprint, and FUTO has also released the 1 million swipe dataset used to train it.
Swipe typing is one of those features that seems simple, but is actually incredibly hard to get right without relying on closed keyboard systems.
FUTO built a three-model system to solve this: a 635K-parameter layout-agnostic encoder, a 300K-parameter QWERTY decoder, and a 1.5M context language model. Because the footprint is tiny, the whole thing can run locally in milliseconds.
Releasing the weights and the C++ beam search library means people can now build proper swipe typing for VR, laptops, or alternative mobile platforms without relying on closed-source blobs.
The best way to experience it today is FUTO Keyboard.
privacy-first ux only works when the core interaction feels genuinely good, and this is a rare case where it does. keeping the typing path local while still shipping something polished is the right product bet, especially from a team that keeps backing this space.
The split between a layout-agnostic encoder and a layout-specific decoder is the interesting call here, since it implies adding something like AZERTY or a custom split layout only needs a new ~300K decoder rather than retraining the whole stack. Is that actually the case, can I train just the decoder against the released 1M swipe dataset while reusing the encoder, or does the context LM need retuning per layout too? Also curious what the license allows for embedding the C++ runtime into a third-party keyboard.
I've been using swiftkey after trying out a bunch of different swipe-y keyboards a couple years ago. Any plans to launch this on the app store? I'd love to try it out!
Releasing both the models and the 1M-swipe dataset is what stands out here. Beyond keyboards, have you seen developers experimenting with FUTO Swipe for non-traditional input systems like VR, AR, smart TVs, or accessibility-focused interfaces? Curious which unexpected use cases have emerged so far.
shipping open on-device models AND releasing the 1M swipe dataset is the rare open-source move that actually moves the field. respect.
Well done, this is a really worthwhile project, thanks for sharing. There seems to me to be a lot more scope for open-standard solutions being picked up and baked into devices, and maybe even built upon as a store, like Chrome's plugins. That would be a big unlock across devices.
Interesting launch 🚀
I like that this runs fully on-device instead of depending on closed keyboard systems or cloud inference.
Curious how well the model handles different typing styles over time. Is there any personalization layer planned, where the keyboard can adapt locally to a user's common words, slang, or swipe patterns without sending data off-device?
Open models for keyboard input feels like an important direction.
About FUTO Swipe on Product Hunt
“Open models for on-device swipe typing”
FUTO Swipe launched on Product Hunt on June 24th, 2026 and earned 124 upvotes and 8 comments, placing #9 on the daily leaderboard. FUTO Swipe is a family of small, open models for accurate swipe typing. It includes a layout-agnostic encoder, a layout-specific decoder, and a lightweight context language model. The full system runs efficiently on-device with a very small footprint, and FUTO has also released the 1 million swipe dataset used to train it.
FUTO Swipe was featured in Custom Keyboards (2.3k followers), Open Source (68.5k followers) and User Experience (366.2k followers) on Product Hunt. Together, these topics include over 44.7k products, making this a competitive space to launch in.
Who hunted FUTO Swipe?
FUTO Swipe was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how FUTO Swipe stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Zac Zuo
Hi everyone!
Swipe typing is one of those features that seems simple, but is actually incredibly hard to get right without relying on closed keyboard systems.
FUTO built a three-model system to solve this: a 635K-parameter layout-agnostic encoder, a 300K-parameter QWERTY decoder, and a 1.5M context language model. Because the footprint is tiny, the whole thing can run locally in milliseconds.
Releasing the weights and the C++ beam search library means people can now build proper swipe typing for VR, laptops, or alternative mobile platforms without relying on closed-source blobs.
The best way to experience it today is FUTO Keyboard.