Yiming Zhang张一明

Context

The product focuses on discovering local activities and meeting people offline. Its public homepage introduces activities, interest collection and product entry points to explain the experience. The project remains at an early Demo stage.

My contributions

• Conducted user research into local activities and social needs, organizing feedback to inform product messaging and content planning.
• Planned promotional content and activities, advanced video and social media promotion, and reviewed feedback and available data.
• Used AI-assisted coding (Vibe Coding) to create the homepage and core-function prototype, with GPT image tools for page and promotional assets.

Deliverables

• Accessible product homepage and core-function Demo
• Page and promotional visual assets
• Content promotion, event planning and feedback review

An early product prototype is available for demonstration, with marketing content and user feedback work continuing. The case presents personal contributions and current deliverables; user scale and conversion improvements have not been verified.

Context

The product focuses on reviewing basketball shooting videos. Its public page introduces video submission, reference comparisons, AI feedback entry points and recording guidance. The project remains at an early Demo stage.

My contributions

• Conducted user research into basketball training and shooting analysis, organizing needs and improvement opportunities.
• Planned video and social media content and promotional activities, consolidated content feedback and reviewed available data.
• Used AI-assisted coding to build the homepage and core-function prototype, developing demo pages for replay, reference comparison and AI feedback, alongside visual assets.

Deliverables

• Accessible product homepage and core-function Demo
• Feature introductions, recording guidance and training-review demonstrations
• Video and social media promotion, with needs and feedback summaries

The homepage and feature prototype support early product communication and promotion. This case demonstrates AI use-case understanding and prototyping; it does not present the page's feature claims as validated algorithm performance.

Context

The project covers SLA attainment, mean time to recovery (MTTR), severity distribution and weekly incident volume. The dataset is synthetic and supports logic validation; it is not bank production data.

My contributions

• Processed incident records with Python and pandas to calculate SLA attainment, MTTR, severity distribution and weekly volume.
• Developed a Streamlit dashboard with team and severity filters, plus a command-line version producing metric reports and charts.
• Validated the logic with 300 synthetic records and reused one metric module across both outputs, documenting the calculation definitions.

Deliverables

• Interactive dashboard with team and severity filters
• Command-line metric reports and trend charts
• Shared metric module, synthetic-data validation and calculation notes

Metric definitions and analytical uses

• SLA attainment: monitor whether resolution meets the defined time limit, using consistent team thresholds
• MTTR: review recovery time and investigate time-consuming steps
• Severity distribution: review incident mix and use team filters to identify priorities
• Weekly incident volume: aggregate records to review workload and trends

Completed a personal workflow from metric calculation to interactive presentation and reporting. The 300 synthetic records validate logic and do not represent actual business volume.