Why I Write ‘见远而行’

“见远而行” is not a slogan about predicting the future.

To me, 见远 means looking past the feature in front of me to its data boundaries, failure modes, and long-term maintenance cost. 而行 means not stopping at an opinion, but turning that judgment into something that can run, be inspected, and keep improving.

This site records the work between those two ideas: how a problem becomes visible, how constraints shape a decision, and how that decision eventually becomes code, a product, or a conclusion that remains open to verification.

Three long-term themes

Data infrastructure

I write about data lineage, governance, semantic modeling, and observability. I am more interested in how systems exchange trustworthy data, how contracts are expressed, and whether failures can be traced than in cataloguing tools and terminology.

Trustworthy AI applications

Language models are good at interpreting natural language and ambiguous input. Systems that handle permissions, money, state changes, or data ownership still need deterministic boundaries. I document ways to separate AI from business logic, observe usage and cost, and balance speed with privacy and control.

Small, maintainable products

I also write about concrete engineering choices in independent projects: why a Web Component can be the right boundary, why a service lives in the user’s own account, how runtime dependencies can be reduced, and which capabilities a tool should deliberately leave out. The goal is not to make a project sound large. It is to keep it understandable, deployable, and able to improve over time.

What I expect from an article

Each post should begin with a real problem or a project being built. I try to follow a few plain standards:

• establish the context and constraints before presenting a solution;
• explain why a choice was made and what was deliberately not chosen;
• distinguish facts, estimates, personal judgment, and the project’s current state;
• provide code, diagrams, demos, data, or a public repository whenever possible;
• update the article when the project changes or the original judgment no longer holds.

These standards do not guarantee that every design is correct. They create a starting point that other people—and my future self—can inspect and improve.

Where to start

If this is your first visit, these posts are useful entry points:

• Building a Data Lineage Viewer as a Web Component, on deterministic layout, cross-framework integration, and component boundaries;
• In a Ledger, AI Should Interpret Rather Than Decide, on candidate actions, confirmation, and deterministic ledger logic.

The AI Footprint publishes aggregate usage and cost trends. The About page collects my current areas of focus and links to the rest of my work.

I do not plan to fill this site on a fixed schedule. A new post appears when a problem has gone through enough implementation, verification, or reflection to be worth explaining clearly.