Privacy
Privacy-First Engineering: The Case for On-Device AI
Why we prefer local processing over cloud pipelines whenever the job can be done safely and well on the user’s own device.
Why we start from the device
A lot of modern software treats the network as the default place where intelligence lives. That model is convenient for vendors, but it often asks users to hand over far more data than the task actually requires.
Our starting assumption is narrower: if the device already has the context it needs, the device should do the work. That makes privacy better, but it also improves responsiveness, reduces failure points, and keeps the product useful when connectivity is poor.
Stashmark as a practical example
Stashmark helps people save links without turning their bookmark list into a landfill. The interesting part is the categorization step. Instead of sending saved links to a remote classification service, Stashmark uses on-device language tooling to suggest categories locally.
That changes the trust model. The saved URL does not need to pass through a third-party AI API. There is no account system required to make the feature work. The result is not just a privacy policy claim; it is a simpler product architecture.
Sahibinden Araç Analizi follows the same rule
The same principle shows up in Sahibinden Araç Analizi. The extension reads listing details in the browser, matches them against its local knowledge base, and highlights known chronic issues and risk signals directly in the page flow.
That is important because the task is sensitive. People do not want marketplace browsing behavior, listing context, and purchase intent flowing into a remote system if the product can avoid it.
Privacy is also a product decision
On-device processing is not a magic solution for every feature. Some jobs genuinely require synchronization, shared state, or external computation. But many products reach for the server long before they need to.
We try to use the network where it adds clear value, not where it merely adds habit. That keeps the software easier to trust, easier to explain, and usually faster to use.