Inspiration We noticed that most ordinary users struggle to process and organize massive daily text and audio information efficiently. Traditional AI tools are either too complicated for beginners or lack targeted lightweight functions. This inspired us to build a simple, fast, and all-in-one AI assistant that helps users sort, summarize, and optimize daily content with one click. What We Learned During this hackathon, we learned how to quickly integrate large model APIs and optimize model response speed in limited time. We also improved our ability to locate technical bugs efficiently and design user-oriented product logic. In addition, we gained richer experience in team collaboration and rapid iterative development. How We Built It We built the entire project based on domestic stable LLM API services. Firstly, we sorted out core user demands and confirmed key functional modules. Then we completed the backend logic docking, parameter tuning and response optimization. Finally, we polished the interactive experience and finished functional testing to ensure stable and smooth output. Challenges We Faced The main challenge was unstable model response latency and occasional redundant output. We solved this by adjusting API request parameters, adding content filtering rules and optimizing prompt logic. Another difficulty was balancing functionality and simplicity; we trimmed unnecessary features to keep the project lightweight and user-friendly.
Built With
- ai
- ai-summarization
- api-integration
- backend-optimization
- content-filtering
- deepseek
- functional-testing
- lightweight
- llm
- llm-api
- logic-iteration
- prompt-engineering
- rapid-prototyping
- user-experience-design
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