Personal Knowledge Intake: From Folders to a Shareable Profile

A large file collection first needs a decision about what belongs in the knowledge base and how it should be organized. This 50-second workflow animation separates the process into stages: treat a project, game, or application folder as a unit; decide whether its material is relevant to the user; choose from predefined categories; then generate summaries and metadata only for retained material. Colored paths represent work, learning, hobbies, everyday life, finances, and social information. Uncertain branches are routed to a person for confirmation, with their answers added to a feedback record. The final stage separates a local profile from a public profile for agents and illustrates private fields being filtered out. In this design, Jev handles bounded relevance and category decisions, a local language model writes summaries, and ordinary code handles traversal, rules, records, and display. It illustrates a division of responsibilities across the workflow. Implementation and limits: the supplied video is generated by an accompanying HTML animation using preset data, random routing, and timers. It is not a recording of a real file scan or live model inference. The 1,860 units, confidence threshold, roughly 100 ms label, processing counters, and retraining arrows are demonstration settings, not evidence of measured performance, effectiveness, or completed training. Only-public-profile-in-the-cloud is a design objective shown in the animation, not a tested privacy guarantee. This listing was prepared from the user-supplied video and animation code. No real personal files were scanned, no model was called, and no local knowledge base was uploaded. Original demonstration: https://x.com/Ldou20/status/2113478794057318502