About
I'm Huang Yihang, a Master's student in Artificial Intelligence at Monash University (Class of 2027, coursework ends 2026.07, degree conferred 2026.10), with an undergraduate background in data science. My target direction is AI application / Agent development. I have worked as an Agent development intern at Sugon, covering enterprise RAG Q&A, multi-agent cross-validation, and SFT data QA. I have also done an AI-product internship, driving the AI-detection rate of an academic-writing tool from 100% down to 10-20%. On the side, I independently built NoWorries, an open-source desktop AI assistant, contributed a merged PR to OpenClaw, and forked it to rebuild its memory and context-management modules. I've taken several AI products from zero to launch, and two of them are running for real. The first is the intelligent operations system I built for Chenxi Flowers: solo-built, in production ever since, and now running 3 growing sites, 65 varieties and 56 customers, against RMB 5.4M+ in cumulative company sales. On top of it sit 5 Feishu group AI Agents: staff place orders, verify outbound stock and log costs by typing a single sentence, and the owner just asks a question to pull inventory, receivables and business numbers. Safety is the spine of the design — read-only by default, writes restricted to an allowlist, and even those execute only after a human confirms, so the AI never holds write access to the database. The second is PowerMyWeb: upload a résumé, and 8 specialized Agents read your background and generate a portfolio site that looks like no one else's. It is now officially live, with Free / Pro subscriptions open. This website itself is also part of the proof: the AI twin, build logs, and architecture trade-offs are all inspectable. I believe the best proof that you can use AI is shipping something that runs, can be checked, and can be reviewed.
Skills
Experience
2025.12 - 2026.02
Sugon · Agent Development Intern
- Built an intelligent HR Agent on Dify (automated resume parsing + multi-dimensional candidate evaluation); stood up an enterprise RAG knowledge-base Agent and tuned the chunking strategy to reach 85%+ answer accuracy
- Designed three specialized review Agents to cross-validate SFT training items automatically: independent assessment + structured scoring + conflict arbitration, cutting manual-review cost
- Built an automated QA workflow on Feishu handling prompt validation and multi-table sync for 500+ items a day, shrinking the manual effort from 3 hours to 10 minutes; owned Agent behavior-trace annotation (line-by-line Tool Calling / CoT review)
2024.11 - 2025.02
Fanquan (AceEssay AI-detection reduction tool) · AI Product Intern
- Drove 4 release cycles, building an evaluation framework on the dual Turnitin / GPTZero platforms; brought the core AI-detection metric from 100% down to 10-20%; distilled hundreds of pieces of user feedback into a prioritized backlog (MoSCoW) and pushed features to launch
- Planned and produced 60+ pieces of content that drove 75K site visits, growing the following from 0 to nearly 30K; lifted the core keyword from #48 to #9, with organic traffic up roughly 3x month over month
2024.07 - 2026.07 (degree conferred 2026.10)
Monash University (QS 37) · Master of Artificial Intelligence
- Core coursework: machine learning, deep learning, natural language processing, planning and automated reasoning, multi-agent systems
2019.09 - 2023.07
Tianjin University of Technology · Bachelor of Data Science and Big Data Technology
- Core coursework: algorithm design and analysis, database systems, data mining, data visualization
Contact
- Email: 1653120857@qq.com
- GitHub: https://github.com/hlbbbbbbb