Startup Projects

Things keeping me busy on weekends

Crowdlistening

Inspiring insights, amplifying voices. (crowdlistening.com) From Content Aggregation to Original Research Crowdlistening transforms large-scale social conversations into actionable insight by integrating llm reasoning with expanding model context protocol(MCP) capabilities. While extracting quantatitive patterns from realtime data is already an rewarding task, our focus is not just on analyzing content at scale, but rather conducting original research directly from raw social data, generating insights that haven’t yet appeared in established reporting.

Work Projects

Insight Spotlight

Analyze thousands of tiktoks to provide actionable trends & insights for key agencies. (Worked on multi-modal content understanding) To be released on TikTok Creative Center (https://ads.tiktok.com/business/creativecenter/pc/en) Credits: TikTok Creative Team Beyond Data: The Evolution of AI-Driven Insight Products for Content Creation Introduction: The Shifting Landscape of Creative AI Tools In the rapidly evolving space of AI-driven creative tools, we’re witnessing a significant transition from general-purpose large language models to specialized, task-specific agent systems. This shift represents a fundamental change in how AI approaches creative work, particularly in advertising and marketing.

Symphony Assistant

Leverage generative AI capabilities for creative script ideation and video ad creation. (Worked on agentic workflows and interface optimization) https://ads.tiktok.com/business/copilot/standalone?locale=en&deviceType=pc Credits: TikTok Creative Team Building Agentic Workflows From LLMs to Agents The transition from LLMs to Agents has become a consensus in the AI community, representing an improvement in complex task execution capabilities. However, helping users fully utilize Agent capabilities to achieve tenfold efficiency gains requires careful workflow design. These workflows aren’t merely a presentation of parallel capabilities, but seamless integrations with human-in-the-loop quality assurance. This document uses Typeface as a reference to explain why a clear primary workflow is necessary, as well as design approaches for functional extensions.

Research

Improving & Scaling LLMs for Coaching

Situated Practice Systems: Improving and Scaling Coaching through LLMs Authors: Terry Chen, Allyson Lee Abstract Effective coaching in project-based learning environments is critical for developing students’ self-regulation skills, yet scaling high-quality coaching remains a challenge. This paper presents an LLM-enhanced coaching system designed to support project-based learning by helping connect peers struggling with the same regulation gap, and to help coaches by identifying regulation gaps and generating tailored practice suggestions. Our system integrates vector-based semantic matching with LLM-generated regulation gap categorizations for Context Assessment Plan (CAP) notes. Results demonstrate that our system effectively retrieves relevant coaching cases, reducing the cognitive burden on mentors while maintaining high-quality, context-aware feedback.

Prototypes

Towards Differentiable Quality - Content Synthesis

Interactive chat interface with multiple AI agents, enabling dynamic conversation flows and specialized problem-solving capabilities.

LLM Enhanced Recommendation and Search

Content recommendation system leveraging embedding similarity for personalized content recommendation (based on profile).

Exploring Unknown Unknowns - Textbook Interface

Interactive learning aids for reading comprehension and engagement.