This is a submission for the GitHub Copilot CLI Challenge
What I Built
I built an AI-powered async standup platform to address a common issue in remote teams: daily updates are written, but rarely analyzed. Blockers get buried, momentum is unclear, and leadership lacks a real-time view of team performance.
This system transforms simple standup entries into structured insight. As team members log what they worked on, what they plan next, and any blockers, the platform automatically tracks consistency, calculates weekly health, highlights pending members, and generates an AI-powered executive summary that captures momentum and risk in seconds.
Behind the scenes, the application uses Supabase with strict Row-Level Security for secure team isolation and follows a clean layered architecture separating database logic, business rules, APIs, and UI. AI summaries are generated on demand, cached for performance, and automatically refreshed when new logs are added, ensuring both accuracy and efficiency.
This project turns routine standups into measurable operational intelligence for modern remote teams.
Demo
Video Walkthrough: https://youtu.be/2r2LKphy1RE
Live App: https://standup-logger-bay.vercel.app/
Repository: https://github.com/vikaskushwaha/standup-logger
Demo Account: vikas.vikas.kushwaha@gmail.com
Pass: 123456
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My Experience with GitHub Copilot CLI
I used GitHub Copilot CLI throughout development to accelerate both architectural decisions and implementation. I relied on it to draft PostgreSQL schemas, refine Row-Level Security policies, and structure API routes cleanly. It was especially helpful when iterating on streak calculations and handling timezone-safe, weekend-aware edge cases in weekly analytics.
During development, I learned that clear context is critical when using Copilot CLI. In a few cases, ambiguous instructions caused it to generate new files instead of editing existing ones. This pushed me to be more precise with prompts and improved how I structured AI-assisted workflows. It reinforced that AI tools amplify developer intent — clarity directly affects output quality.
Beyond backend logic, I also used Copilot to improve UI refinements such as implementing loader states, enhancing dashboard responsiveness, polishing card layouts, and restructuring weekly analytics calculations for better accuracy and performance. These refinements improved both perceived performance and overall user experience.
Copilot ultimately reduced iteration time, surfaced edge cases early, and helped me focus more on system design and product quality rather than repetitive boilerplate.
Built with collaboration from:
@ykiirraann
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