I went straight into feature brainstorming: auto-captions, caption accuracy improvements, visual sound indicators for Live.
Start by framing the problem around the user need—real-time and on-demand accessibility for deaf and hard-of-hearing users—then propose concrete, scalable improvements like automatic captions, sign language overlays, and customizable text settings. Finally, define success metrics that balance user engagement, accessibility adoption, and quality (e.g., caption accuracy) to show measurable impact.
Pro tip: Tie every proposed improvement to a measurable metric and mention the trade-offs (e.g., latency vs. accuracy in live captions) to demonstrate engineering pragmatism and product sense.
Restate the concern: deaf and hard-of-hearing users lack equal access to live and recorded video content. Identify key sub-segments (e.g., ASL users, lip readers, those who prefer text) to tailor solutions.
Suggest features like real-time automatic captions with high accuracy, optional sign language interpretation overlays, customizable caption styling (size, color, background), and a searchable transcript for videos.
Rank improvements by user impact, technical complexity, and scalability. For example, automatic captions are high impact but require ML investment; sign language overlays may need manual or partner integration.
Choose metrics across adoption (e.g., % of videos with captions enabled), engagement (watch time, completion rate for deaf users), and quality (caption accuracy, user satisfaction scores). Include guardrail metrics like latency.
Describe how to measure via A/B tests, user surveys, and accessibility audits. Emphasize continuous improvement based on feedback and metric trends.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.