I'm Yutong, with nearly seven years of experience in internet-related work and projects. From operations and development to enterprise AI applications, my work has expanded from keeping systems running to building products and understanding business problems.
I work across requirements, solution design, frontend and backend development, deployment and delivery. These skills help me turn an unclear idea into something concrete and refine my judgement through actual results.
From operations to AI applications
First, learn to keep systems running
I started in operations, working on Linux, deployment, reliability and automation. On container and GitOps platforms, I contributed to architecture and implementation, defined standards and helped teams adopt them. Keeping systems running after launch was a constant concern.
Container platform and application migration → · GitOps release platform →
Then, turn ideas into products
I gradually moved into frontend and backend development. On the multi-cloud platform, I connected the resource model, management interface and customer environments; on KeySafe, I contributed to full-stack development. Development lets me test solutions directly, while my operations background keeps me focused on failures, deployment and maintenance.
Understand delivery through real business work
In creator marketing and recruiting, I began spending more time understanding roles, client requirements and actual handoffs. How requirements are confirmed, how reasoning is retained and how feedback changes the next round of work determine where AI can help.
These projects also taught me that building a feature is only a start: we still need to verify whether it solves a worthwhile problem.
How I work
Clarify the purpose before choosing an approach
I used to jump into implementation when a requirement arrived. Now I first ask what change we want and where the current process gets stuck, then decide whether development is needed and where AI fits. In creator marketing, for example, confirmed client requirements became shared criteria for search and review.
Test judgement through real work
I prefer to start with a clearly bounded workflow, explicit deliverables, human review and acceptance criteria. AI output is uncertain, and business users' corrections are material for improvement. After a demonstration works, we still need to observe rework, decisions and handoffs in actual use.
What I'm focusing on
Enterprise AI delivery and personal AI collaboration
I focus on enterprise AI delivery, agent development and the connection between product and operations. I am also building Intent, a persistent workspace for goals, projects, tasks and materials connected to a personal assistant. It lets me test product decisions through my own daily work.
Where I can contribute
Business workflow analysis, product and solution design, full-stack development, AI integration, deployment and automation. My responsibilities and deliverables are in my Resume and Projects, to help you assess whether my experience fits your needs.
What I'd like to work on together
I'd like to help build enterprise AI applications and take products from zero to one, working with people who understand their industry and customers to examine problems and test solutions. I accept remote work and am interested in working internationally and across cultures. Employment, part-time work and project collaboration are all welcome.
Contact
- Career focus
- Enterprise AI delivery (FDE) / Agent development / Product and operations
- Ways of working
- Remote work (project-based, part-time, full-stack)
- Resume
- View resume →
- GitHub
- github.com/cytlabs
- Other work
- blog.csdn.net/cyt0906
- cytlabss@gmail.com
- xiamudewx