Yours.
Offline.
A domain-tuned model that runs on your device — your Mac, your phone. Private by default, Lightning-native, with a cloud boost only when you ask for one.
And in this hybrid mode, whenever you sync daily, your offline model quietly pulls the world down with it — so what runs on your machine still knows what happened today… whenever you need it.
Local is fast enough.
The cloud is faster.
A well-tuned 7–12B model clears 50+ tokens/second on a modern Mac and stays usable offline on a phone. When you want wafer-scale speed, Cerebras is a switch away — not a dependency.
Tuned, not
rented.
We start from a permissively licensed open base, then teach it our domain. The result is an adapter and a set of weights that are ours — not an API key pointed at someone else's model.
A clean licence
Pick an open base whose licence permits commercial derivatives. That choice decides both performance and whether the result is truly yours.
The real work
Domain data, cleaned and structured as instruction–response pairs, versioned and kept private. Quality over quantity, every time.
LoRA / QLoRA
Fine-tune on rented GPUs, validate on held-out domain tasks, iterate. The adapter is a derivative work we own.
Quantise & embed
Phone-friendly and macOS-optimised builds, embedded in the clients so inference is local by default — with real measured throughput, not claims.
Rented GPUs do the training; nothing rents the result. The weights ship inside the app.
Nothing leaves
unless you send it.
Offline-first isn't a mode you switch on. It's the default, and the network is the exception.
Hybrid — local by default, with a Cerebras boost for the heavy asks. You choose, per request, and you can see which one answered.