The bell for 2026 is about to ring. Following the custom of the past decade, right now I should be taking stock of this year’s GitHub commit history, summarizing what new technologies I’ve learned, what new features I’ve developed, and what difficult bugs I’ve solved.
But this year, sitting before the screen with the cursor blinking in the Markdown editor, an unprecedented feeling wells up: This might be the last time I do a year-end summary as a “person who writes code”.
With tools like Claude Code enabling autonomous AI programming, starting next year, I may never need to manually type out that tedious code again.
My development experience and architectural ability, which I once took pride in, are not disappearing, but are undergoing a marvelous morphological transformation:
They are transforming from a “craft” into “instructions,” from “muscle memory” into “technical taste.”
The Inflation of Code and the Scarcity of Taste
In 2025, we witnessed a collapse in the cost of code production. AI can generate complete code in seconds, but problems follow: AI’s default output is often mediocre, verbose, or even “coding for the sake of coding.”
In this new era, Coding (the ability to code) depreciates into a consumable, while Taste appreciates into an asset.
This year, my time in the IDE shifted more from “writing” to “tuning.” I discovered that the architectural preferences I’ve accumulated over the years are no longer just abstract concepts in my head; they have become the supreme constitution constraining AI behavior.
I encoded my “technical taste” into Claude Code’s configuration, making the AI a digital twin of my technical soul.
1. Complexity Control: Simplicity is the Ultimate Complexity
AI loves to generate complex boilerplate code based on its pre-training knowledge base. But I tell my AI: “Simplicity is the ultimate complexity.”
In my configuration, I mandate it to pursue extreme minimalism in code. I don’t need over-encapsulated design patterns or redundant inheritance hierarchies just for the sake of being “object-oriented.” Because I know that simple code inherently possesses high readability and maintainability.
Before, I had to refactor code personally to reduce complexity; now, as a reviewer, I just need to say to the AI: “This logic is too convoluted, rewrite it in a more intuitive way,” and it instantly delivers concise code that meets my aesthetic standards.
2. Architectural Preference: The Art of Data Flow and Decoupling
My architectural aesthetic has always leaned towards Functional Programming (FP) and Event-Driven Architecture (EDA). I dislike entangled shared states and the quagmire of synchronous blocking.
Now, this preference has become the “factory setting” I set for the AI.
- “Default to pure functions, reduce side effects.”
- “Prioritize EventBus for inter-module communication to maintain decoupling.”
When the AI generates code according to these principles, I no longer see messy scripts, but clear data flows and elegant asynchronous processing. It has become my most obedient “FP Evangelist,” tirelessly executing the dogma of immutable data.
3. Asset Accumulation: From “Writing Features” to “Building Blocks”
In the past, we often fell into the trap of reinventing the wheel. But I have always insisted on modular development and top-level architectural design. My goal is: as the project progresses, we should have more and more “building blocks” in hand. Developing new features should be as fast as assembling Lego, rather than falling into maintenance hell as the code volume increases.
This year, I trained the AI to identify and extract common modules. “This logic looks like it can be abstracted into an independent component.” “Encapsulate this component and put it into our asset library.”
With AI assistance, my project turned into an organically growing ecosystem. Every new requirement adds assets to the system, rather than debt.
Furthermore, AI gave me the confidence not to compromise, allowing me to achieve the satisfaction of a “perfect architecture.”
4. Process-Oriented: Making Code Speak “Human Language”
I have always detested code filled with technical jargon where the business intent is indecipherable. I insist on process-oriented development, where code must align with business language.
Now, this is the core protocol of my interaction with AI. I no longer describe to it “Loop through array A and assign to B,” but tell it: “Execute the ‘User Order’ process, trigger the ‘Inventory Deduction’ event.”
In the code generated by AI, variable names are business terms, and function names are business actions. The code itself becomes executable documentation. This realm of “Code as Business” has become easier to achieve than ever before with AI’s support.
Conclusion: From Coder to Commander
Next year, I might no longer spend half an hour debugging a logic error, nor flip through documentation for syntax and library usage.
But this doesn’t mean I’m “unemployed.” On the contrary, my job has upgraded.
I will evolve from a Coder to a Commander.
User Prompts are my baton;
System Prompts are my operational guidelines;
Agents are my loyal corps.
They will perpetuate my taste, tirelessly building systems that are simple, modular, event-driven, and semantically aligned with the business.
Goodbye, Coding.
Hello, Commander.