The tech industry has a default playbook for building new products: spin up a React frontend, attach it to a Postgres database in the cloud, slap on user authentication, and charge $10/month. When building tools for the AI space, this playbook is incredibly tempting.
But for ContxtAI, we threw the playbook out the window. We chose to build a strictly local-first application. Here is why, and how we did it.
The Privacy Imperative
AI context managers handle the most sensitive data imaginable. Users are pasting in their company's proprietary source code, their messy architectural plans, and sometimes, inadvertently, their API keys.
If we built a cloud-synced backend, we would immediately become a massive security liability. We would have to convince enterprise developers that our random startup's cloud is secure enough to hold their intellectual property. By going local-first, that conversation vanishes. The data never leaves their machine. We couldn't look at it even if we wanted to.
The Technical Challenges
Building local-first in a browser extension environment presents unique hurdles, primarily due to Chrome's Manifest V3 (MV3) architecture.
In MV3, background scripts are replaced by Service Workers. These workers are ephemeral—they spin up when needed and die a few seconds later. You cannot store state in global variables. If a user selects a profile in the popup, and the background script needs to know about it 10 seconds later to inject it into a page, a standard variable will be undefined.
To solve this, we rely heavily on chrome.storage.local as our single source of truth. It acts as an ultra-fast, local NoSQL database. Every time a user updates a context bundle, it is serialized and written to local storage. Every time a content script needs to inject text, it reads directly from that storage.
The Future is Local
While the initial engineering effort is higher, the benefits are immense. ContxtAI is blazingly fast because there is zero network latency. It works perfectly on an airplane. It requires no login, no onboarding friction, and no server costs to scale.
As AI tools become more integrated into our private workflows, local-first architecture isn't just a feature—it's a requirement.