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I am interested in helping build the Youth Sports Family Assistant MVP. My background is especially relevant to the AI and full-stack aspects of the product. At Google, I build AI-powered experiences for Fitbit Health Coach, integrating Gemini with real-time contextual data and user-facing applications. My work spans React and TypeScript interfaces, backend APIs, structured AI workflows, data processing, security, and production deployment. Previously at Microsoft, I helped build Azure Machine Learning as a customer-facing SaaS platform across React applications, APIs, authentication, data systems, and cloud infrastructure. For this MVP, I would keep the architecture deliberately simple: a responsive web application, relational data model for families/children/teams/events, authenticated APIs with strict per-family authorization, and a structured AI extraction flow where model-generated schedule details are always presented for parent confirmation before persistence. I would treat the family calendar and manually confirmed data as the source of truth rather than allowing AI output to modify schedules autonomously. Conflict detection, reminders, subscription limits, account lifecycle, and analytics can then be layered around that core workflow without introducing unnecessary services. I am comfortable taking an ambiguous product from requirements through implementation and production, and I particularly like that this project has a focused MVP boundary rather than trying to solve every youth-sports workflow in the first release. Best regards, Jingcong Wang…I am interested in helping build the Youth Sports Family Assistant MVP. My background is especially relevant to the AI and full-stack aspects of the product. At Google, I build AI-powered experiences for Fitbit Health Coach, integrating Gemini with real-time contextual data and user-facing applications. My work spans React and TypeScript interfaces, backend APIs, structured AI workflows, data processing, security, and production deployment. Previously at Microsoft, I helped build Azure Machine Learning as a customer-facing SaaS platform across React applications, APIs, authentication, data systems, and cloud infrastructure. For this MVP, I would keep the architecture deliberately simple: a responsive web application, relational data model for families/children/teams/events, authenticated APIs with strict per-family authorization, and a structured AI extraction flow where model-generated schedule details are always presented for parent confirmation before persistence. I would treat the family calendar and manually confirmed data as the source of truth rather than allowing AI output to modify schedules autonomously. Conflict detection, reminders, subscription limits, account lifecycle, and analytics can then be layered around that core workflow without introducing unnecessary services. I am comfortable taking an ambiguous product from requirements through implementation and production, and I particularly like that this project has a focused MVP boundary rather than trying to solve every youth-sports workflow in the first release. Best regards, Jingcong Wang

Jingcong Wang

Software Engineer, DevOps Developer, QA Engineer, +2

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I am interested in helping build the Youth Sports Family Assistant MVP. My background is especially relevant to the AI and full-stack aspects of the product. At Google, I build AI-powered experiences for Fitbit Health Coach, integrating Gemini with real-time contextual data and user-facing applications. My work spans React and TypeScript interfaces, backend APIs, structured AI workflows, data processing, security, and production deployment. Previously at Microsoft, I helped build Azure Machine Learning as a customer-facing SaaS platform across React applications, APIs, authentication, data systems, and cloud infrastructure. For this MVP, I would keep the architecture deliberately simple: a responsive web application, relational data model for families/children/teams/events, authenticated APIs with strict per-family authorization, and a structured AI extraction flow where model-generated schedule details are always presented for parent confirmation before persistence. I would treat the family calendar and manually confirmed data as the source of truth rather than allowing AI output to modify schedules autonomously. Conflict detection, reminders, subscription limits, account lifecycle, and analytics can then be layered around that core workflow without introducing unnecessary services. I am comfortable taking an ambiguous product from requirements through implementation and production, and I particularly like that this project has a focused MVP boundary rather than trying to solve every youth-sports workflow in the first release. Best regards, Jingcong Wang…I am interested in helping build the Youth Sports Family Assistant MVP. My background is especially relevant to the AI and full-stack aspects of the product. At Google, I build AI-powered experiences for Fitbit Health Coach, integrating Gemini with real-time contextual data and user-facing applications. My work spans React and TypeScript interfaces, backend APIs, structured AI workflows, data processing, security, and production deployment. Previously at Microsoft, I helped build Azure Machine Learning as a customer-facing SaaS platform across React applications, APIs, authentication, data systems, and cloud infrastructure. For this MVP, I would keep the architecture deliberately simple: a responsive web application, relational data model for families/children/teams/events, authenticated APIs with strict per-family authorization, and a structured AI extraction flow where model-generated schedule details are always presented for parent confirmation before persistence. I would treat the family calendar and manually confirmed data as the source of truth rather than allowing AI output to modify schedules autonomously. Conflict detection, reminders, subscription limits, account lifecycle, and analytics can then be layered around that core workflow without introducing unnecessary services. I am comfortable taking an ambiguous product from requirements through implementation and production, and I particularly like that this project has a focused MVP boundary rather than trying to solve every youth-sports workflow in the first release. Best regards, Jingcong Wang

Available to hire

I am interested in helping build the Youth Sports Family Assistant MVP. My background is especially relevant to the AI and full-stack aspects of the product. At Google, I build AI-powered experiences for Fitbit Health Coach, integrating Gemini with real-time contextual data and user-facing applications. My work spans React and TypeScript interfaces, backend APIs, structured AI workflows, data processing, security, and production deployment. Previously at Microsoft, I helped build Azure Machine Learning as a customer-facing SaaS platform across React applications, APIs, authentication, data systems, and cloud infrastructure.
For this MVP, I would keep the architecture deliberately simple: a responsive web application, relational data model for families/children/teams/events, authenticated APIs with strict per-family authorization, and a structured AI extraction flow where model-generated schedule details are always presented for parent confirmation before persistence. I would treat the family calendar and manually confirmed data as the source of truth rather than allowing AI output to modify schedules autonomously. Conflict detection, reminders, subscription limits, account lifecycle, and analytics can then be layered around that core workflow without introducing unnecessary services.
I am comfortable taking an ambiguous product from requirements through implementation and production, and I particularly like that this project has a focused MVP boundary rather than trying to solve every youth-sports workflow in the first release.
Best regards,
Jingcong Wang