Case Study: How a University Student Completed His Graduation Project in Two Weeks with KAIHE AIBOX

Zhang Mingyuan, a senior CS student at a Wuhan university, faced a crisis in March 2026: two weeks until his graduation project deadline, and his "Deep Learning-Based Campus Security Anomaly Detection System" hadn't progressed beyond the proposal stage.

The scope was overwhelming: 5,000 manually annotated images, model training with hyperparameter tuning, a Vue.js frontend, and a 20,000-word thesis — in 14 days.

The Breakthrough

His advisor suggested KAIHE AIBOX, preloaded with a cloud model aggregation gateway capable of running multiple LLMs locally — DeepSeek for code generation and thesis polishing, Qwen for Chinese literature summarization, Gemini for dataset annotation.

Day 1-3: AI auto-annotated 5,000 images at 0.3 seconds each versus 30 seconds manually. Human review corrected only 8%.

Day 4-7: Transfer learning on Kaggle pretrained models. When hyperparameter tuning stalled, the model diagnosed overfitting and suggested Dropout adjustments.

Day 8-10: AI-generated Vue.js frontend with real-time monitoring, anomaly highlighting, and history query.

Day 11-13: AI generated thesis draft from experimental data and analysis points. Zhang spent three days refining — 20,000 words completed.

Day 14: AI-generated defense PPT outline.

Result

Final score: 86/100. The evaluating professor praised the annotation standards and experimental rigor. Zhang's takeaway: "KAIHE didn't do my thinking for me — it handled the repetitive work I knew how to do but didn't have time for. The time it saved me went straight into what mattered: experimental design and innovation论证."