Local-first by default
Inference, memory, and tooling should start close to the operator. Hosted or paid calls should be visible, attributable, and controllable.
A Bitspace Applied Intelligence project
Do Your Focused Job, AI.
DYFJ is a sovereign AI stack: modular, vendor-loose, local-first by default, with cost visibility built in.
View the repo Early and active. Not a hosted SaaS.
What it is
An AI stack without the handcuffs.
DYFJ is a first-class AI stack: local-first by default, modular behind stable contracts, and designed so model choice, runtime choice, memory, permissions, and cost are visible parts of the system instead of convenient afterthoughts.
It is early, active, and deliberately not a hosted SaaS. The point is agency: durable context, swappable components, cost-aware choices, and tools that help the operator know what happened.
Operating stances
Vendor-loose, not vague
Components can be replaced behind stable contracts. Strong defaults matter; lock-in does not get to be the architecture.
Cost is visible
Token spend, model selection, and budget posture belong in the working surface before and during the work.
The log is ground truth
Memory is a derived view. The message and event log are the durable audit trail for what the system actually did.
Source
The repo is the current source of truth.
The DYFJ repo carries the current substrate work: prototype code, schema decisions, and the operating posture for the project. This site is the simple splash page for that work as a Bitspace Applied Intelligence project.