About

From venue administration to software engineering

Saw the problems of manual and paper-based work first-hand at the venues, then worked with AI to build two operations systems and take over three; fixes made outside work have also been merged by open-source projects.

Role
Software Engineer, DAOS Sport, 2025.09 – present; retention confirmed 2026.01
Education
Tamkang University, Department of Information Management; graduated 2026.06
Tech
React · TypeScript · Node.js · Express · PostgreSQL · LINE LIFF · Ragic API
Location
New Taipei, Taiwan
Email
ians098365@gmail.com
Phone
+886 921 233 310
Résumé
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Summary

Collaborating with AI to turn large volumes of manual, paper-based work into digital systems and to streamline workflows, reducing HR workload and raising customer satisfaction.

Background in venue administration and IT; turns requirements into automated systems that lower the cost of manual processing. Keeps up with emerging technology and the open-source ecosystem, and has found problems while digging in, proposed fixes, and had the fixes adopted and merged by mainstream open-source projects.

Education Graduation capstone awarded an Honorable Mention in the department competition

Tamkang University, Department of Information Management. Graduated 2026.06.

Graduation capstone What The Hair (What The 髮): an AI hairstyle try-on app built by a four-person team; contributed to back end, deployment and optimization. Honorable Mention, Information Applications category, department capstone competition.

Experience Venue operations administration → IT internship → Software engineer

DAOS Sport|Software Engineer|2025.09 – present

  • 2026.01: Retention confirmed
  • 2025.09 – 2025.12: IT internship
  • 2024.03 – 2025.08: Venue operations administration

Venue administration meant working with paper sign-ups, Excel rosters and repeated inquiries. The IT internship covered APIs, databases and data mapping, followed by feature integration and the full build of the Tutoring Course System.

Work Internal process integration, customer-facing course digitization, deployment and delivery

  • B2E internal operations process integration: Integrates scheduling, HR form write-back and employee Q&A to reduce repeated work. One notification and self-service entry replace contacting more than 100 employees one by one; for availability collection, a comparison of the old and new processes estimates time savings of at least 70%. Related pages: Cross-Venue Workforce Scheduling System, Pool Co-Teaching Scheduling System, LINE OA BOT.
  • B2C course operations and customer experience digitization: Simplifies course items, categories and the course-purchase flow to raise purchase intent; integrates customer needs and interaction records to identify high-value customers within the existing customer base, supporting follow-up and sales strategy planning. Related pages: Tutoring Course System, Summer Course Registration Platform.
  • Deployment and engineering delivery: Work systems are deployed on cloud platforms, with build and start commands kept in configuration files; on release the platform builds and then goes live. Depending on the nature of each system, it runs either as a service that scales with traffic or on an always-on host. After a push to the main branch, the personal site is verified and deployed automatically by Cloudflare Workers Builds. Open-source contribution PRs pass the upstream GitHub Actions tests. Releases of the work systems still include manual steps and are not yet fully automated.

Outside work, problems were reproduced and fixes proposed in ioredis and WorldMonitor; both PRs have been merged upstream. This site is also a personal project.

Team and division of work Led development, planned requirements with the PM

The 5 systems at DAOS Sport: led development, working spec-first. The spec is written first, requirements are planned together with the PM, and the spec is then discussed and revised. Once the spec is settled, development is done with AI tools such as Claude Code and Codex, through to launch testing. The role in each system is listed in the DAOS Sport 5-system overview.

AI collaboration Spec-driven; AI-written code still has to be verified

Development is spec-driven: the spec is written first, then discussed, revised and built against.

Follows new technology trends and the open-source ecosystem, tries things hands-on first, then evaluates adoption into company workflows or daily work. Helps colleagues understand and use AI and clarify requirements, and develops with tools such as Claude Code and Codex.

AI-written code still has to be verified: data compared before and after release, bugs reproduced with a test before being fixed, and every API endpoint called before launch. The point is delivering tools that are usable.

Projects (representative work) Tutoring Course System and ioredis PR #2180

Two representative projects: a system built from requirements at work and self-maintained, and an open-source fix merged by the official upstream project.

  • Tutoring Course System: Built from requirements, covering architecture, development, deployment and maintenance; 265 API routes and 125 test scripts. All three post-launch investigations traced to a root cause.
  • ioredis PR #2180: Fixes missing commands and misaligned callbacks in auto-pipelining; 6 regression tests added; merged by the official project.

Verifiable records Two merged upstream PRs and one department competition award

Skills React, TypeScript, Node.js, PostgreSQL and LINE integration

  • AI collaboration: Claude Code, Codex
  • Back end and system integration: LINE Bot / LIFF, Ragic API, Node.js, Express, REST API
  • Interface and interaction: React, TypeScript, Astro, HTML / CSS
  • Testing and collaboration: GitHub, Postman
  • Data and storage: Neon (PostgreSQL), Redis
  • Deployment and operations: Cloudflare Workers (automatic deployment through Workers Builds), cloud platform deployment; GitHub Actions used for tests on open-source contributions

Used in the production work systems: React, TypeScript, Node.js, Express, PostgreSQL, Drizzle ORM, LINE LIFF / Messaging API, Ragic API (cross-check with the tech list on each project page).

Direction AI applications, data integration and automation

Continues to explore AI applications, data integration and automation, with an aim of extending technology applications to green energy and sustainability issues in the future.

Contact

GitHub
boyam01

The full résumé is available as a PDF download.