Boundary before capability
We define what an agent may and may not do before we make it powerful. Control comes first.
Yunce AI (深圳云策人工智能有限公司) focuses on applied AI and agentic systems. Our core direction is building AI agent execution systems — not models that merely answer, but systems that understand goals, call tools, advance workflows, and leave verifiable evidence behind.
An AI agent should not merely look automated. It should be a controllable, observable, verifiable, and recoverable execution system. We emphasize boundaries, approvals, logs, evidence chains, and human-in-the-loop — rather than overstating that AI replaces people.
// Turn intent into action.
We define what an agent may and may not do before we make it powerful. Control comes first.
Every outcome carries logs and proof. "It worked" is a statement we can always back up.
Judgment, accountability, and sensitive decisions remain with humans — by design, not by accident.
Yunce AI is led by 程序员阿江-Relakkes, an engineer with 10 years of software development experience, large-company experience, and a visible track record in open-source infrastructure, data crawling, AI agents, developer tooling, and applied AI education.
His open-source and research-grade engineering projects include MediaCrawler, a 50K+ star multi-platform crawler project, and cc-haha, a 12K+ star developer-tooling project. This is the kind of community-validated engineering signal investors and venture teams can inspect directly: real users, real forks, real technical discussion, and shipped systems rather than resume-only credibility.

Founder · Open-source engineer · AI builder
Tell us about the workflow you want to make trustworthy, observable, and automatable.