- 01
Joined FunPlus in 2023 as a campus-hire level designer; grew into Level Lead. Main campaign, combat experience, combat AI and version drops all ship live — player-facing content with every release.
- 02
Walked the three generations of hero design end-to-end myself: Batman (fully manual) → Riddler (AI-assisted, semi-automated) → Supergirl SP (AI-native pipeline). Same person, one pipeline, pushed from zero to production.
- 03
Supergirl SP is the flagship hero supporting ~1/3 of total project revenue — and the first proof point that the AI-native pipeline actually ships.
- 04
SP Card System Owner — the system itself is a core revenue module. I own teardown, plan, ship, iterate end-to-end and sit directly on its P&L.
- 05
Took over a stalled SLG content line; currently rebuilding it with the same AI pipeline playbook to bring it back to stable delivery.
- 06
Horizontally: built the AI production line covering levels, heroes, numerics, data, art and tooling — 60+ heroes, 100,000+ config rows, 100+ Agent Skills. Personal capability turned into team infrastructure.
For three years,I rebuilt game content as an industry with AI.Next: AI-native games — co-built.
Welded AI into the level / hero / numeric / data / art / tooling layers of a live SLG. Now looking for the team that wants to point this pipeline at a new shape of game.
/ 02 · receipts
The numbers are mine.
Every figure traces back to a live build artifact — no borrowed credit, no narrative inflation.
Full content + recommendation pipeline for every hero in the live game.
01Across 7 interlocking tables, all flowing through reusable AI workflows.
02Six business domains: levels, heroes, numerics, data, art, tooling.
03End-to-end MD → CSV → multi-language → validated, no human re-typing.
04Gacha, pool positioning, system teardowns, monetization decisions.
05Supergirl SP content I owned end-to-end is the project's single largest revenue contributor.
06/ 03 · experience
Title, scope, and the P&L line I sit on.
/ 04 · pipelines
AI is a production line, not a prompt.
Six business domains, all flowing through reusable workflows. Each pipeline is something I personally built, dogfooded, and handed off.
Level Production Line
Spec → level config → monster config → reward config → numeric tuning → ship
Hero End-to-End Pipeline
Brainstorm → design doc → engineer spec → config → CSV export
Hero Guide Auto-System
Recommendation copy + lineup + stats + signature gear + environment + multi-language
Decision-Grade Data Reports
Raw data → structured analysis → version & monetization decision support
100+ Agent Skills Library
Reusable skill packs across six business domains
Code-as-Spec Decoder
When the doc is missing, AI reads the code and reconstructs the design intent
/ 05 · atelier
Things I actually shipped.
Each card is work already running in production with data behind it. Full case-study docs available — just ask.
Hero design · one person, three eras
Three hero generations · manual → AI-native
Batman (fully manual) → Riddler (AI-assisted, semi-automated) → Supergirl SP (AI-native pipeline). Same person, one pipeline, pushed from zero to production.
Hero · Monetization core
Supergirl SP content line
Owned design → combat mechanics → skill feel → config → version delivery. The flagship hero where the AI-native pipeline first truly shipped.
System Owner · Revenue module
SP Card System
A core revenue module of the project. I own teardown, plan, ship, iterate end-to-end and sit directly on its P&L.
AI Pipeline
Hero Recommendation Stack
Structured guides for the full hero roster: text → MD → CSV → multi-language → validation.
System Owner · Earlier
Tower System (1700 levels)
Levels, monsters, rewards, numeric balancing, lore, art briefs, milestones, BI requirements — single-owner.
System Owner · Earlier
Tag Tower System
2 small towers × 120 levels + 5 big towers × 600 levels, with tutorial, ranking, lore, UE design.
Throughput
Multilingual Sprint
1000 lines of multilingual content shipped in a single day — the old cycle was a 2-week version, the new one is one day.
/ 06 · about
I'm a player, rebuilding the games I design with AI.
I played SLGs to the point of dissecting their data tables, and then walked into the industry. That's where my taste for what's good comes from — I judge from the player's side of the screen first.
I use AI because I'm allergic to doing the same thing three times. I tear the process apart, let AI handle what's repeatable, and keep the judgment calls for myself.
Next stop: AI-native games — not AI as a sticker on a product page, but games whose mechanics grow out of AI from the bottom up.
/ SLG player credentials
- Shuaitu Wuji
- S3 Sango Zhejiang Region · MengMa
- Bameng Invitational · Champion (Returning)
- Sanqi Sanmou · Xuantian Si
Top-tier player. That's where my ability to read the math and still feel the pull comes from.
/ 07 · let's talk
If you build AI-native gamesor 3D AI products,we should talk.
Especially if your team thinks of AI as a production line, not as a costume on a product page.