Introduction to Vibe Coding

Recently, a new term has been circulating in my tech circles: Vibe Coding. Initially, I thought it was just another piece of jargon, but after using natural language to create a simple demo of “Plants vs. Zombies” in just ten minutes, I realized its significance.

We are at a pivotal moment in software engineering. Programming, once considered a high-intelligence craft, is transforming into a design task that requires only “intention” and “aesthetic”.

Today, I want to step beyond mere technical parameters and deeply analyze the performance of several core AI models in the Vibe Coding space from a product manager’s perspective, discussing how to seize this opportunity and address potential concerns.

Identifying the Problem: Why Do We Need “Vibe” Instead of “Syntax”?

As a product manager, one of my most frustrating moments is when I have a brilliant idea and a beautifully drawn prototype, but the developers tell me, “This logic is complex; the backend architecture needs to be restructured, and it will take at least two weeks.”

The greatest barrier to innovation has never been imagination, but rather the marginal cost of implementation.

Vibe Coding emerged to solve this problem. Its core logic is that humans define the “Vibe” (intention, business logic, vague goals), while AI handles all the dirty implementation details (code, dependencies, debugging).

This reminds me of Andrej Karpathy’s prediction: in the future, natural language will be the highest-level compiler for programming.

But the question arises: can our domestic AI models compete in this arena defined by OpenAI and Anthropic?

Understanding the Problem: Vibe Coding in Action by Three Major Players

To find the answer, I deeply experienced three core products supporting Vibe Coding in China: DeepSeek, Trae, and Tongyi Lingma. They felt like three engineers with distinct personalities.

DeepSeek: The Hidden “Intelligence Engine”

DeepSeek’s first impression was “hardcore”. It lacks flashy interfaces and serves more as an intellectual powerhouse behind the scenes.

I attempted to use it to solve a complex logic problem, with the core instruction being: “Recreate a simplified version of Plants vs. Zombies. On the left is a plant card bar with Peashooters and Sunflowers; on the right is a 9x5 lawn grid. Sunflowers produce sunlight over time, which can be collected by clicking; sunlight is consumed to plant Peashooters; zombies randomly appear from the right and move left, with Peashooters automatically attacking the zombies in line.”

I was amazed by DeepSeek R1’s performance. Instead of directly outputting code like ordinary models, it first entered a “DeepThink” mode. It planned the game loop, inheritance relationships of entities (plants, zombies, bullets), collision detection mechanisms, and even considered the timer logic for sunlight production.

The generated code was logically sound with very few bugs. This “Chain of Thought” reasoning capability solved the biggest pain point of Vibe Coding—AI often generates code that “looks correct but doesn’t run.” In scenarios involving multi-role interactions, state management, and complex timing logic, DeepSeek demonstrated remarkable control.

Interestingly, its cost is low due to the MoE architecture, making API calls very affordable. For scenarios requiring frequent code modifications and repeated debugging of game balance, this is a delightful advantage.

Trae: The “All-round Partner”

If DeepSeek is the engine, then Trae from ByteDance is a finely furnished sports car.

What surprised me most about Trae was its “SOLO mode”. In this mode, it is not just a code completion tool but an “autonomous agent”.

After generating the core logic with DeepSeek, I used Trae to optimize the interface and interactions. I instructed it: “Help me optimize the style of the plant card, adding a highlight border when selected. Additionally, when a zombie is hit, add a brief red flash effect to indicate damage.”

Trae not only modified the React component’s CSS styles but also automatically added a hit state (isHit) and corresponding visual feedback logic to the zombie component. It even executed terminal commands to install necessary animation libraries. This “self-looping” capability allowed me to refine the game experience without writing a single line of code.

Moreover, Trae employs a clever “dual-model strategy”: using its own Doubao model for simple style modifications and completions (fast) while using the DeepSeek model for complex logical reasoning (accurate). This creates an extremely smooth user experience.

However, as a PM, I also noticed privacy concerns. Trae uploads the entire library code to build an index during operation, and its data collection strategy is quite aggressive. This may not matter for individual developers, but it could be a red flag for enterprise users.

Tongyi Lingma: The Steady “Enterprise Gatekeeper”

Alibaba Cloud’s Tongyi Lingma has a completely different temperament. It feels more like a consultant in a suit.

Its Vibe Coding capability is reflected in its “Enterprise Knowledge Base (RAG)”. When I asked it to “add a user login and points leaderboard feature to this game, complying with the company’s internal user center specifications,” it automatically retrieved the internal SDK documents and API definitions I uploaded. The generated code was not only syntactically correct but also fully utilized the company’s unified login authentication component, adhering to the team’s coding style.

For medium to large enterprises, this “controllable vibe” is essential. Additionally, its support for private deployment completely resolves compliance issues regarding data sovereignty. In the B2B market, Tongyi Lingma firmly maintains its defenses.

Comparative Analysis: Our Gap with the Global Leaders

During my experience, I continuously compared it with foreign products like Cursor and GitHub Copilot.

Objectively, DeepSeek R1’s logical reasoning capabilities can already match OpenAI’s o1, providing us with great confidence in complex algorithms and logic implementations. However, in terms of engineering experience, Cursor’s Composer function remains the industry benchmark, offering precise context handling and fluid interactions. Trae is closing the gap but still has room for refinement.

Another gap lies in the openness of the ecosystem. Foreign Vibe Coding toolchains are very flexible, allowing Cursor to switch between Claude, GPT, or DeepSeek models freely. In contrast, domestic products are more like “walled gardens”; Trae is tied to Doubao/DeepSeek, and Tongyi Lingma is bound to Qwen. This closed nature somewhat limits developers’ choices.

Solutions: New Infrastructure for Product Managers

Based on the evaluations above, how can product managers effectively utilize domestic Vibe Coding capabilities?

Embrace the Development Flow of “Prototype as Product”

The combination of Trae and DeepSeek has effectively shortened the MVP (Minimum Viable Product) development cycle from weeks to hours.

I suggest PMs start trying hands-on development. Instead of creating static Axure prototypes, describe a game requirement in the style of “Plants vs. Zombies” using natural language and let AI generate an interactive web application. This not only allows for a more intuitive verification of gameplay and requirements but also provides a more tangible reference for communication with developers. Your core competitive advantage will shift from “drawing” to “defining architectural aesthetics” and “describing complex interactions”.

Beware of “Vibe Coding Hangover”

This is a risk that must be acknowledged. Over-reliance on AI’s “Apply All” could lead to codebases rapidly swelling into incomprehensible “spaghetti code”.

The solution is to introduce an AI code review mechanism. Enterprises implementing Vibe Coding must accompany it with an automated Code Review Agent. Tongyi Lingma has already explored this aspect. We must ensure that the AI-generated code not only runs but is also maintainable.

Build a Tool Stack Suitable for Your Team

There are no best tools, only the most suitable combinations.

If you are a startup team: Trae is the first choice. Free, ready to use, and fast, one person can replace an entire team. If you are a tech enthusiast: use VS Code + DeepSeek R1 (local deployment). You have complete control over your data and can enjoy the fun of tinkering. If you are in a state-owned enterprise or large corporation: Tongyi Lingma Enterprise Edition. Compliance is paramount; utilize RAG technology to consolidate enterprise knowledge, allowing AI to become a knowledgeable external employee.

Conclusion: From “Craftsman” to “Industrial Designer”

The rise of Vibe Coding signifies that software development is transitioning from the workshop era to the industrial age.

In this new era, DeepSeek has single-handedly lowered the cost of intelligence, Trae has reshaped the ultimate interactive experience, and Tongyi Lingma has safeguarded enterprise security.

For us product managers, this is a tremendous empowerment. We finally have the opportunity to step out of the quagmire of implementation and focus on what truly matters: understanding needs, defining value, and designing experiences.

The future of software development may indeed only require a precise “Vibe”. Are you ready?