You should have consulted me when you entered Japan and especially the Japanese market, my friend.😂

Local-first approaches will make their comeback and it is the time.

It's definitely time!

It’s always deeply motivating to connect with teams who see that same future.
Because we’re not just building technology — we’re redefining the paradigm.

We’ll define the boundaries together.

The fact that you've chosen NexQS as the foundation for Sage speaks volumes — not just about the technology itself, but about the shared vision behind it. NexQS isn’t just a solution; it’s the embodiment of a new standard in local, fast, and customizable AI.

Replacing BYOTA’s core with NexQS is aimed at significantly boosting responsiveness and enabling fully local AI customization without compromising performance or privacy. I’d love to explore a benchmark collaboration — let’s push the limits of what edge-native AI can really do.

Great question, Çağan. The NexQS Engine is being designed precisely with edge efficiency in mind. We are optimizing for real-time inference, zero-copy data flow, and a sub-5MB deployment footprint — all tailored for devices like the Raspberry Pi 5 and below.

At NexAI (NexQS) Engine, we're also prioritizing modularity, user agency, and real-time interaction in inference pipelines. Projects like BYOTA point toward a future where personalization doesn't have to come at the cost of transparency or privacy. Excited to see where this movement goes.

Really impressed by how BYOTA approaches decentralized AI customization. Letting users build their own timeline algorithms entirely on-device with full data ownership — using components like Llamafile for local embeddings and Marimo for browser-based UI — is both elegant and empowering.
+++

As an engine dev, this framework could standardize systemic reliability beyond detecting hallucinations. External evaluators quantifying behavior without internal access is huge but how effective is it for black-box models?

3/3
The new era of decoding the brain’s silent signals through data offers tremendous hope for humanity, paving the way for earlier interventions and better outcomes in neurodegenerative disease management.

2/3
By decoding the complex architecture of the human brain through data-driven analysis, AI drives revolutionary advancements in neurological health and becomes an unparalleled tool for enhancing quality of life.

The potential of ambient intelligence to detect subtle neuromotor shifts opens a new frontier: diagnosing cognitive decline not through what patients say, but through what their silence reveals. Are we approaching a future where AI can map the invisible topography of human thought?