01 / NOTES

Latest Notes

Specifications, tool development, and thoughts on the future of AI.

Model the Future, Act in the Present

Starting with how society specializes and collaborates today, explore how AI might develop and assess which projects fit the changes underway and can be built now.

Specification-Driven Development: Building Projects That Last and Scale

Support project growth with clear, executable, evolving specifications, from code architecture to AI collaboration.

From Asset Standards to Executable Rules

Scope, condition, response: what does a check rule need to explain?

02 / PROJECTS

Tools & Projects

Build each iteration on what already works.

Expressing and executing rules

Standards Validation Toolkit

ResourceToolkit

Combine scope, conditions, object transformations, and actions into reusable rules that bring production standards into everyday workflows.

Read about rule design
Working with assets

Asset Check Toolkit

AssetCheckToolkit

Organize checks around assets, import and display results, and help users locate issues, understand findings, and take the next step.

My focus: how people review and use results, and how tools can share common property and UI capabilities.

AI chat and model selectionAndroid · In development

Yingying · AI Chat App

Yingying

An Android AI chat app I am developing. Users ask a question, and the server selects the reply model and reasoning effort based on the conversation, keeping the exchange in the same chat.

Sign in with an account. Model calls are billed by actual usage; the app explains the rules and prices.

Shared UI and editor capabilitiesStandards validation · Asset checks · Scene checks

The core library provides common capabilities; each tool owns its domain logic and workflow.

Editor tools are not available for public download yet. Yingying's Android package is available above.

03 / PRACTICE

Put standards into tools, then learn from feedback

Start with one rule: evaluate, act, and check again.

Rule workflow · Concept demo

A check that explains its results

This example uses fictional texture sizes: define the requirement, check the actual value, adjust it, and verify again. Real tools also need to handle data sources, limits on changes, and location information.

  1. Make the requirement explicitDefine what to check, what to measure, and what counts as a pass.
  2. Fit the implementation into the architectureClarify shared capabilities and domain boundaries, then use AI to implement a focused piece.
  3. Let results guide the next iterationVerify behavior, explain failures, and improve the tool based on real use.
Read: Specification-Driven Development
How a rule worksINDEPENDENT DEMO
Texture-size check results for fictional assets
Demo assetMax. dimensionResult
T_DemoStone_BC4,096Exceeds limit
T_DemoTile_N2,048Pass
T_DemoIcon512Pass
3 demo assets · 1 exceeds the limit

Fictional data illustrating rule logic only. This page does not connect to UE or change real assets.

04 / ABOUT

Turn experience into tools.
Keep notes as they evolve.

My work centers on large-world game development in UE5 and editor tools. I continue to develop a shared Python editor library, standards validation, and asset checks, while refining tools for scene checks.

I explore how AI can shorten the path from a requirement to a first implementation, and how architecture, shared capabilities, and verification support long-term maintenance. These notes cover concrete problems, implementation trade-offs, and improvements informed by real use.

Get in touch · Email29797471@qq.com

UE5 ProgrammingEditor ToolsPython / SlateAI CollaborationAsset & Scene Checks