Vibe Coding in Practice: Building a GPS Check-in App from Scratch with AI — A Complete Walkthrough

What is Vibe Coding?

Recently, a programming approach called "Vibe Coding" has been gaining popularity in developer communities. Unlike traditional programming, the core philosophy of Vibe Coding is to describe requirements in natural language and let AI generate the code. Developers take on the roles of "product manager" and "architect" rather than writing code line by line.

The term "Vibe Coding" was first coined by OpenAI co-founder Andrej Karpathy in February 2025. He described a completely new programming experience on social media: fully immersing yourself in the "vibe," relying on AI's code completion and generation capabilities, and barely writing any code manually. The concept quickly sparked widespread discussion in the developer community because it represents a paradigm shift from "writing code" to "directing AI to write code."

A Chinese content creator recently shared their complete process of developing a GPS check-in app from scratch using the Vibe Coding approach. The entire workflow required no knowledge of any programming language, yet produced a fully functional mobile application. This practical case study effectively demonstrates the current capabilities and workflow of AI-assisted programming.

Core Differences Between Traditional and AI Programming

Before diving in, it's important to understand the fundamental differences between the two approaches:

Dimension	Traditional Programming	AI Programming (Vibe Coding)
Input Method	Programming language syntax	Natural language descriptions
Entry Barrier	High — requires mastering syntax and frameworks	Low — just describe what you need
Development Speed	Relatively slow	Rapid prototyping
Rigor	High — developer has full control	Requires manual review and adjustments

Traditional programming may be slower, but it excels in rigor and control. AI programming dramatically lowers the barrier, enabling non-programmers to bring their software ideas to life. The two aren't replacements for each other — they're complementary. AI programming is particularly well-suited for quickly validating ideas and building prototypes.

Tool Preparation: Setting Up the Vibe Coding Environment

The tool stack used in this project includes:

• VS Code: Code editor, serving as the primary workspace
• Flutter: Google's cross-platform mobile development framework for building the app
• Emulator: For previewing and testing the app on your computer
• Kimi (AI Assistant): Responsible for generating code based on natural language requirements
• SQLite: Local database for storing GPS check-in records

Flutter is an open-source UI framework released by Google in 2017, using the Dart programming language. Its core advantage is "write once, run anywhere" — a single codebase can compile into native applications for iOS, Android, Web, desktop, and more. Flutter uses the Skia rendering engine rather than platform-native widgets, ensuring UI consistency across platforms. For Vibe Coding scenarios, Flutter is particularly suitable because its declarative UI syntax naturally aligns with the logical structure of natural language descriptions, resulting in relatively high readability and accuracy in AI-generated code.

SQLite is the most widely deployed database engine in the world — virtually every smartphone has SQLite built in. Unlike MySQL, PostgreSQL, and other databases that require separate server processes, SQLite is an embedded database where the entire database is a single file that runs without a network connection. This makes it the go-to solution for local data storage in mobile applications. In the GPS check-in app, using SQLite means all check-in records are stored locally on the user's device, eliminating the need for a backend server and significantly reducing project complexity.

Key steps in environment setup include: creating a Flutter project, installing SQLite dependency packages, and configuring the Kimi AI tool. One detail worth noting — installing certain dependency packages may require a VPN, which is a common issue for developers in China.

Core Methodology: The Four-Step Document-Driven Development Approach

The most valuable part of this case study isn't the specific code — it's a structured AI programming workflow. The entire process is divided into four clear steps:

This four-step document method is essentially an AI-era adaptation of classic software engineering methodology. In the traditional waterfall model, software development follows the same flow: "requirements specification → high-level design → detailed design → implementation." The difference is that in traditional workflows, documentation is written for human developers, while in Vibe Coding, documentation serves as "Prompt Engineering" written for AI. Prompt Engineering refers to the technique of carefully designing input text to guide large language models toward producing high-quality outputs — it has become one of the core skills in AI application development.

Step 1: Create the Master Requirements Document

First, clearly define what the app should do. The goals for this project were specific:

• User taps a button to capture current GPS location and timestamp
• Each check-in's location and time is recorded in the local database
• Users can view historical check-in points
• All check-in points are displayed on a map, connected by lines in chronological order

The key technique here: use a questioning approach to have AI help organize requirements, rather than letting AI improvise. For details you're unsure about, ask AI to proactively ask you questions — this prevents AI from making assumptions that lead to misalignment.

Step 2: Create the Master Design Document

Feed the requirements document from Step 1 as input and have AI output a technical design document. The design document should cover data structures, interface layouts, module divisions, and other technical decisions.

The same principle applies: require AI to ask you questions about uncertain technical choices rather than deciding on its own. The quality of this step directly determines the accuracy of subsequent code generation.

Step 3: Split into Sub-task Documents

Based on the requirements and design documents, break the entire project into multiple sub-task documents at the smallest granularity. Each sub-task document corresponds to an independent functional module, such as "GPS positioning module," "data storage module," "history display module," "map line-drawing module," etc.

The benefit of this decomposition: AI generates significantly higher-quality code when handling small, well-defined tasks compared to generating an entire project at once. The technical reason behind this is that current large language models (LLMs) are limited by context window length and attention mechanisms. When processing long, complex instructions, they tend to produce "hallucinations" — code that appears reasonable but is actually incorrect. By splitting tasks into independent small modules, the context for each conversation becomes more focused, allowing AI to better understand intent and generate correct code. This is why the industry recommends an "incremental iteration" strategy for AI programming.

Step 4: Create a One-Click Launch Prompt Package

Consolidate all previous documents — master requirements, master design, and all sub-tasks — into a single prompt file that can be referenced with one click. Once this step is complete, subsequent code generation becomes a simple operation: just @reference this file, and AI can begin generating code based on the complete context.

This "four-step method" essentially adapts classic software engineering processes — requirements analysis, architecture design, task decomposition — to the AI programming paradigm.

Key Lessons from Actual Development

During the hands-on process, several practical insights emerged:

1. Let AI handle UI design freely: For interface layouts and visual styles, give AI more creative freedom to design based on best practices — the results are often quite good.

2. Strictly constrain business logic: Unlike UI, core elements like functional logic and data structures must be explicitly described without ambiguity. "If you're not entirely sure about something, don't let AI decide on its own" — this is the key to avoiding rework.

3. Understand project structure: Even without writing code, you need to understand the basic structure of a Flutter project — for example, that the lib directory is where the main code lives. This fundamental framework knowledge remains necessary.

4. Iterate rather than expecting perfection: Don't expect AI to generate perfect code on the first try. Instead, refine and improve through multiple rounds of conversation.

Capability Boundaries and Use Cases for Vibe Coding

This case study showcases the enormous potential of AI programming while also revealing some practical limitations:

Clear advantages: For applications with relatively standardized functionality (such as data recording and information display apps), Vibe Coding genuinely enables non-programmers to produce usable product prototypes in a short time.

Limitations also exist: Environment configuration still requires some technical foundation (command-line operations, dependency installation, network troubleshooting); complex business logic and performance optimization still require programming knowledge; the quality and security of generated code needs professional review.

The security issue deserves special attention. A 2023 Stanford University study found that developers using AI-assisted programming actually produced code with a higher proportion of security vulnerabilities than the control group that didn't use AI, and users tended to be overconfident about their code's security. Common issues include SQL injection, hardcoded keys, and insecure data transmission. Therefore, even in Vibe Coding mode, security review of generated code remains an essential step that cannot be skipped.

More importantly, this "four-step document method" reveals a trend: the core competitive advantage for future developers may shift from "writing code" to "describing problems" and "architecture design". The ability to clearly decompose requirements, design system architecture, and write high-quality prompts will become critical skills in the AI era.

Conclusion

Vibe Coding isn't meant to replace traditional programming — it opens the door to software development for more people. Through a structured document-driven approach — from requirements to design, from design to sub-tasks, from sub-tasks to one-click generation — even people without programming backgrounds can leverage AI to turn their ideas into reality. This is perhaps the most exciting aspect of AI programming: lowering the barrier to creation and empowering more people to become builders.