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Stop Chasing AI Coding Tools and Build a Workflow That Outlasts Them

Hello everyone, I'm Yuying!!!

Follow the official account 【Yuying AI Lab】 for continuous sharing of new frontend and AI-assisted frontend coding knowledge~~

I write notes, bits of life, and frontend experience from time to time.

In the current environment, pure frontend developers can find breakthroughs through technical deepening, horizontal expansion, entering emerging fields (MCP TOOLS, AI application product direction, etc.), and product-oriented thinking. (No matter how long you've been coding or how deep and broad your skills are, you must have the ability to earn money independently!! Folks, because one day you will leave the workplace and face choices and the ruthlessness of the professional world.)

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The era of the most intense competition in frontend might truly be over. AI Agent engineers will be the protagonists of the future. An era where humans only make decisions and think about commercialization models, while AI executes, is in full swing.

Sorry everyone, I've been busy making a living, working part-time, taking care of my kids, and running an e-commerce store over the past year, so technical articles have really been delayed. It's been almost a year since my last update, but I will still post some core technical shares from time to time, learning about the frontend journey and future AI vision and AI business thinking together with everyone.

The future is here. In the era of AI and Agent engineers, there is no longer a strict distinction between frontend and backend. With good models and sufficient tokens, you are a senior programmer (but not necessarily a senior AI engineer)~

I finished writing this article yesterday and just posted the first one because I was busy. A series on AI cognition will be released soon.

What you learn is not important. What's important is whether, after working in this industry for so long, you have always been easily replaceable, unclear about the future, unclear about industry cognition, or still in a state of constant learning. If this advice is useful to you, stop first~ adjust your state and keep moving forward!!

Author: Yuying | 10-year Frontend | Yuying AI Lab · AI Cognition Improvement Series 02

First, a Dose of Cold Water

It's 2026, are you still chasing the next AI tool?

When Cursor was hot, you learned Cursor. When Claude Code was hot, you learned Claude Code. When Trae became free, you quickly installed it. When GitHub Copilot updated, you quickly caught up...

And the result?

You've switched tools repeatedly, but your efficiency remains the same.

Because you've been learning "tools" and never built a "workflow."

1. Why "Learning Tools" is a Trap

1.1 Tools Change, Process Thinking Doesn't

Over the past two years, the iteration speed of AI programming tools rivals that of phone launches:

Time Mainstream Tool Current Status
Early 2024 Copilot Still around, but no longer the lead
Mid 2024 Cursor Was hot, but Claude Code is better
Early 2025 Claude Code Expensive, but the most capable
Late 2025 Trae (Domestic) Free, native Chinese
2026 Multi-Agent Collab Trend is set

Every time you switch tools, you have to relearn shortcuts, prompt writing methods, and boundary capabilities.

But if you have process thinking, tools are just the execution layer—swap one out, and the upper layer remains unchanged.

1.2 A Single Tool Always Has Boundaries

No single AI tool is all-powerful:

You will never find the "perfect tool," but you can use a combination of several tools to cover all scenarios.

2. Comparison of Two Mindsets

Tool Mindset ❌ Process Mindset ✅
Starting Point There's a new tool → I must learn to use it There's a task → What combination should I use to complete it
Focus Shortcuts, Prompt techniques, feature lists Task decomposition, tool division of labor, output verification
Result Learned a lot, but still don't know which to use when the time comes Quickly match the most suitable tool for each task
Risk Resistance Tool dies, capability resets to zero Tool changes, workflow remains unchanged

3. My AI Workflow (Frontend Perspective, July 2026 Practical Version)

3.1 Writing New Features → Claude Code

Scenario: Starting a requirement from scratch, the requirement is clear, let it work directly.

Why use it: Terminal-native autonomous Agent, reads code, modifies code, runs tests, self-iterates, no need for human supervision.

My measured data:

Key usage: Give it a clear goal description, don't change requirements as you go.

❌ "Help me write a login feature" (too vague)
✅ "Implement a React form login component, including email + password validation,
   display error message on failure, call /api/login on success,
   use toast for error handling" (clear)

3.2 Maintaining/Modifying Old Projects → Cursor

Scenario: Taking over an unfamiliar codebase, or modifying someone else's old project.

Why use it: VS Code fork, inline dialogue + diff approval, you can see every change step, manually approve before applying.

Measured data:

Key usage: First use @ to load the entire codebase, let it understand the context, then make requests.

3.3 Understanding Unfamiliar Code → Let Agent Explain

Scenario: Inheriting legacy code, don't know what a certain module does.

Why use it: AI explains code 10 times faster than reading code.

Measured: A 3000-line old project, using Claude Code to explain core logic + trace data flow, 1 hour to grasp the full picture. Originally, manual reading would take at least 3 days.

3.4 Repetitive Tasks → Hand to Agent, Do Acceptance Yourself

Scenario: Batch renaming components, standardizing code format, writing unit tests.

Why use it: Mechanical work takes up the most time, Agent does these quickly and accurately.

3.5 Tool Combination Effect (One Person, Three Tools)

Data Explanation
67% Code repository commits from AI (not human-written)
3.2 hours/day Repetitive labor time saved
12 days → 5 days Project delivery cycle compressed

This isn't the credit of a single tool, it's the victory of process combination.

4. How to Build Your First AI Workflow

Step 1: Record Your Daily Repetitive Tasks

Open a document and list them:

- Writing repetitive components (forms, lists, modals)
- Renaming variables/functions
- Writing unit tests
- Code review
- Reading unfamiliar code to understand logic
- Writing documentation comments

Step 2: Match a Tool to Each Task

Writing new features      → Claude Code (autonomous execution)
Modifying old projects    → Cursor (human-machine collaboration)
Understanding unfamiliar code → Claude Code (explanation mode)
Repetitive rewriting      → Claude Code (batch processing mode)
Code review               → Cursor (diff approval)

Step 3: Fix It in Place, Don't Change Casually

A workflow needs 2–3 weeks to form muscle memory.

Once set, use it. Don't switch just because "a new tool came out." Unless the new tool can replace a link in your process, don't change it.

5. In the Next 3 Years, Tools Will Change, the Process Framework Won't

Clear trends by 2026:

Direction Explanation
Multi-Agent Collaboration Planning Agent + Execution Agent + Verification Agent, like a digital assembly line
Natural Language Replaces Dev Entry "Speaking requirements" replaces "writing code," tools will change, requirement decomposition ability won't
Human-Machine Collaboration at Scale One person supervises multiple Agents, output ≈ a small team

Abilities that won't change:

6. One Step You Can Take Today

Don't learn Cursor today, install Trae tomorrow, and watch Claude Code tutorials the day after.

Do just one thing today:

For the project you are currently working on, list:

  • Which parts are repetitive? (AI can do)
  • Which parts require judgment? (Humans do)
  • Which AI tool is best suited to handle the repetitive parts?

Hand the repetitive parts to AI, invest the saved time into the parts that require judgment.

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July 2026 · Yuying · Tools Change, Process Thinking Doesn't