Vibe Coding in Practice: The Right Methodology for Open Source Secondary Development

Why Your AI-Assisted Code Always Gets Messier Over Time

Almost everyone who uses AI-assisted programming has fallen into the same trap: generating a small utility tool works with a single prompt, but once a project grows complex, fixing one feature mysteriously breaks another. When you try to build on top of an open source project and let AI modify the code, things spiral out of control — and you can't even figure out where it went wrong.

Many people blame their prompts for not being detailed enough, repeatedly screenshotting and re-describing requirements to the AI, going back and forth a dozen times without getting it right. But the real problem isn't the prompts — it's that you're applying two completely different development paradigms as if they were the same thing.

These two paradigms are: building from scratch — "jungle development" (Vibe Coding's native use case) — and modifying an existing system — "secondary development". Confusing the two is the root cause of failure for most people.

What is Vibe Coding? The concept was introduced by OpenAI co-founder Andrej Karpathy in early 2025. It refers to a development paradigm where developers describe intent in natural language and AI generates the code. Developers focus on sensing "is this going in the right direction?" rather than writing code line by line. This paradigm is highly efficient when building small projects from scratch, but applied to complex existing systems without structured thinking, code quality can quickly spiral out of control — which is exactly the problem this article addresses.

Jungle Development vs. Secondary Development: What's the Core Difference?

Jungle Development: Design the Architecture First, Then Let AI Generate

Jungle development means building a project from a blank slate. In this case, you have full control — you first design the overall architecture, define module boundaries and data flows, then let the LLM generate code step by step according to your plan. AI plays the role of "executor" here, with clear boundaries and easy-to-isolate errors.

Secondary Development: You're Working with Someone Else's System

Secondary development is completely different. You're inheriting a system someone else already built — you have no idea what the original author's design intent was. You know what the project does, but not how it works internally.

In this situation, if you let AI dive straight into modifying the code, it knows nothing about this unfamiliar system either — it can only make blind guesses. The result is a mess, and you can't even pinpoint where things went wrong.

The real first step isn't rushing to change code — it's having the LLM perform a full reverse deconstruction of the entire system: how the architecture is structured, what modules exist, how data flows through them. Only after you've mapped all of that out should you talk about how to modify anything. This "understand first, modify second" approach is exactly the path most people never take.

The Technical Essence of Reverse Deconstruction This step is known in software engineering as Reverse Engineering — reconstructing a system's design intent and module boundaries by analyzing existing code, runtime behavior, and dependency relationships. Modern LLMs are capable of performing static analysis on codebases: identifying inter-module dependencies, data flows, and interface contracts. This is effectively rebuilding a usable "system map" without the original design documents. This capability significantly lowers the barrier to understanding unfamiliar codebases — but only if developers know how to ask the right questions and can verify the model's output.

Path One: Building an AI Chatbot from Scratch with Cursor

For beginners, the best exercise is to build a working project from zero. The goal here is to use Cursor to develop an AI chatbot that actually runs.

What is Cursor? Cursor is an AI-native code editor built as a deep customization of VS Code, with built-in integration for major LLMs including GPT-4 and Claude. It supports codebase-level context understanding, can sense project structure across files, and offers inline completions, conversational editing, and automatic error fixing. Compared to plugin-based solutions like GitHub Copilot, Cursor embeds AI capabilities more deeply into the core editor workflow, making it one of the most widely used tools in Vibe Coding today.

This process is essentially the standard jungle development workflow:

• Define the requirement boundaries first: Which model should the bot connect to? What kind of conversational interaction should it support?
• Design the basic architecture: How should the frontend interaction, backend logic, and model API interface be layered?
• Let AI generate incrementally: With a clear architecture in place, use Cursor to generate code module by module — every step can be verified.

The value of this step is establishing the right "human-AI collaboration rhythm" — you own the architecture and direction, AI fills in the implementation details. Successfully running a project built from scratch is what gives you the foundation to tackle more complex scenarios.

Path Two: Full Workflow for Secondary Development on OpenClaw

The real advanced challenge is secondary development based on the open source project OpenClaw. OpenClaw is an open source enterprise-grade intelligent conversation bot framework that provides foundational implementations for model integration, message routing, and multi-channel adaptation. It was chosen as the case study because its codebase is reasonably sized, its module boundaries are relatively clear, and it authentically reflects common enterprise IM tool integration scenarios. This path is closer to real-world work and better tests whether you've truly mastered the "understand the system first" methodology. The entire process can be broken into seven key steps:

1. Download the Source Code and Validate the Minimum Viable Setup

After obtaining an open source project, the first thing to do isn't read the code — it's to get it running locally. Run the minimum viable scenario, confirm the environment is configured correctly, and verify that basic functionality works. This step builds an intuitive understanding of the project and establishes a "baseline to roll back to" for subsequent modifications.

2. Reverse-Deconstruct the System Architecture

This is the single most critical step in the entire secondary development process. Use an LLM to help you fully map out OpenClaw's architecture, module structure, and data flow paths, clarifying:

• What core modules the system is composed of and what each one is responsible for;
• How modules communicate with each other and what stages data passes through from entry to exit;
• What constitutes core logic versus replaceable peripheral components.

Only by drawing this "system map" clearly can you know the blast radius of every subsequent change, rather than modifying blindly.

3. Identify the Modification Points

Building on your understanding of the architecture, pinpoint exactly which modules your customization requirements will touch. For example, swapping out a model or integrating a new messaging channel — figure out which part of the system each change corresponds to.

4. Swap Out the Model

Replace the model the project originally used with your target model. Because you already understand which module handles model calls, this becomes a targeted, surgical modification rather than a global fumble.

5. Integrate with Feishu (Lark)

Connect the bot to Feishu, completing the channel-level customization. Feishu (Lark) provides a comprehensive open platform API that supports receiving message events via Webhook or persistent connection, and sending messages via RESTful interfaces. The integration essentially adds an adapter at both the input and output layers of the messaging system — converting Feishu's message format to the framework's internal format, and converting the framework's responses back to a message body Feishu can recognize. This adapter pattern reflects the value of your earlier architecture understanding: knowing "where messages come in and where they go out" is what enables a minimally invasive channel extension.

6. Engineering Convergence

After the modifications are complete, enter the engineering convergence phase: clean up redundant code, unify configurations, handle edge cases, and evolve the project from "works" to "stable and maintainable."

7. Full Delivery

The final step is full delivery — ensuring the entire project can be reproduced, deployed, and used by others.

The Core Insight: Your Ability to Understand Systems Determines Your Ceiling

After walking both paths, you'll arrive at a simple but profound conclusion: when it comes to AI-assisted programming, whether or not you take the time to deeply understand the system architecture directly determines whether you stay permanently stuck at the "modifying code" stage, or actually transform an open source project into something that's truly yours.

AI programming tools are getting more powerful, but they cannot do the job of "understanding an unfamiliar system" for you. In fact, precisely because AI can rapidly generate large amounts of code, humans need to maintain a stronger grasp of overall system structure — otherwise you're just creating bigger messes at a faster speed.

Whether it's "design first, generate second" in jungle development, or "deconstruct first, modify second" in secondary development, the core principle is always the same: build a clear understanding of the system first, then let AI execute the specific actions. That is the true dividing line between beginning and actually mastering Vibe Coding.

Complete documentation and course materials for this series are ready. Interested readers can find them in the comments section of the original video, including self-assessment tools and LLM technical community learning resources.

Key Takeaways