Most AI tools follow a prompt → response pattern. We built one that flips it. Yansu runs on your desktop and observes how you work. The pipeline: Listen — captures desktop activity and messaging app context Crystallize — distills observations into structured, actionable knowledge Solve — generates apps (GUI or CLI) based on that knowledge, proactively The knowledge layer compounds — behavior patterns, generated apps, and improved workflows all feed back into a reusable base. The longer it runs, the sharper it gets. Architecture: everything processes locally. LLM calls send structured context, not raw data. SOC 2 + ISO 27001. We validated with enterprise customers first — shipping integrations to legacy software companies using the same engine for the past year. Yansu App applies the same approach to individual workflows. Example: before every customer call I’d manually pull context from Slack, email, and the CRM. Yansu noticed the pattern and proactively built a pre-call brief. Free BYOA(bring your own agent) tier. $20/mo managed. Mac/Win/Linux. yansu.app Happy to go deep on the pipeline architecture or how proactive generation decisions are made. submitted by /u/yubozhao
Originally posted by u/yubozhao on r/ArtificialInteligence