Made good use of Qoder to build an AI-native dev system—1 person, 2 months, delivering what once took 20 people 3 months (an MES product).
Hello everyone, I'm Li Yuzhong, Product Technology Lead of Guanjiapo Huihuang AIMES. In an era when artificial intelligence is sweeping through the programming world, many developers are still watching from the sidelines, or feel frustrated because "AI-generated code is unstable." As an "old programmer" with 22 years of experience, I believe that a single person is enough to complete what a team of 20 used to accomplish in three months. This is no fantasy—it is the result of the reconstruction of "engineering discipline" in the AI era.
Many people feel frustrated when they first use AI for programming, because they treat AI as a "god." But in reality, AI is essentially an "intern" with extremely powerful capabilities yet no business intuition whatsoever.
If you throw a coding instruction at AI and let it guess your intent, the result is usually chaos. AI doesn't understand your business, nor does it understand the logic a company has accumulated over 30 years. Therefore, transitioning from "coder" to "architect" is a path every developer must take—what you do is no longer writing code, but "commanding" and "defining."
When I first started, I took many detours. I only knew it worked fast; I didn't know how to write rules or how to write skills. At the very beginning I burned through more than a hundred thousand Credits, over 8,000 yuan a day, and at the time it felt amazing. But later on, the AI couldn't keep going, because it couldn't even modify its own work—all because I hadn't done much of the preparatory work up front.
Later, I rebuilt a very important part of the ERP—MES (Manufacturing Execution System). With just myself plus Qoder, I built a product in two months, and it's already being demoed and sold.
The advantages of Qoder lie in:
The biggest contradiction for enterprises implementing AI is not that "AI is unstable," but the "human's ability to command."
If someone internally says "AI is unstable," it's often because they can't master AI. AI is essentially a tool that executes according to specifications; the quality of its output depends directly on the precision of the rules (Spec) you input. AI is a powerful "intern," not a reliable "engineer." It has extremely strong execution ability but lacks business experience and a global perspective. Therefore, trust but always verify. Don't expect AI to guess your intent. Treat AI as a new team member and manage it through institutionalized, standardized means—only then can you truly achieve the efficiency transformation of "one person doing the work of ten."
At first I didn't know what SDD was. Once you use it, you'll find that if you don't write specs, if you don't write these things, the AI later becomes an idiot. You ask it to fix something, and it can't. If we hand old code—the kind of "shit-mountain code"—to AI, it becomes an idiot, because it's all wrong inside, with no rules. What is the most core thing in software engineering? Doing things by the rules—that's what returns us to the essence. The core of a computer is input, computation, output; if you don't even have rule-based input, how can it give you rule-based output?
The core of AI coding is "specification precision." SDD is about converting traditional development requirements into extremely precise engineering specification documents.
Before generating any code, you must define field types, validation rules, UI presentation, and so on. The more detailed the "constraints" you provide, the higher the quality AI delivers. After writing the Spec, you must require AI to simultaneously generate a complete set of test cases, achieving closed-loop verification through automation and avoiding the repetitive labor of humans manually hunting for bugs.
By using clear Specs to constrain AI's behavior, you turn abstract requirements into executable boundaries. This is not only about regulating AI—it's also about re-organizing the team's development logic. Without constraints, there is no quality; without rules, AI merely repeats your mistakes.
From personal tools to team infrastructure—if you have no skills, it's all in vain. ERP boils down to nothing more than three big pieces: basic information, documents, and reports. When a new employee arrives, I'll finish writing the product information and tell them: for business partners, just follow my example. That's a skill.
Skills abstract complex business logic into "modular skills" that AI can invoke. Their essence is solidifying the industry experience, coding habits, and architectural decisions you've accumulated over many years into an "engineering operation manual" that AI can understand and execute. Construction path: First, through in-depth communication, get AI to nail one document (such as a purchase order), then distill it into a general Skill. When you do a similar task later (such as a sales order), you directly invoke that Skill and finish in a few minutes.
Skills are not set in stone; they are iterated through weekly summaries and retrospectives, and they are the core assets of both the individual and the team.
Memory solves AI's "context discontinuity" problem across different business scenarios.
Like human employees, AI needs to have "memory" of the project. As project complexity grows, Memory ensures AI can understand the difference between version 10.0 and version 11.0, avoiding forgetting and logical confusion.
All Memory is essentially structured data (such as specific folder directories). By copying this Memory, you can quickly let a new project inherit the business capabilities of an old project, achieving seamless knowledge transfer.
From personal memory to team memory: Memory should not belong only to the individual; it should be a shared knowledge asset of the team.
Let Rule guard the bottom line, Memory record the standards, and Skill deliver the capabilities—only when these three layers work together do you have complete AI Coding engineering.
● Context Pollution: As the project iterates, long-running conversations lead to context redundancy and even contradictions. Lesson: Clean up regularly, and require AI to re-organize based on the current Rule, Memory, and Skills.
● The AI Hallucination Repair Loop: If AI still can't solve a specific problem after three attempts, it means it has fallen into a mental dead end. Lesson: Switch conversation windows in time; don't stubbornly grind away—learn to "communicate from a different angle."
● Rule Drift: Unsupervised AI easily deviates from predetermined coding standards. Lesson: You must set mandatory "red line" rules (such as SQL injection prevention and coding standards) to ensure AI won't break the bottom line in pursuit of speed.
● Over-Reliance: Assuming there's nothing wrong with what the AI writes is extremely dangerous. Lesson: Enforce a mandatory Review process + manual review of key modules.
Don't expect AI to guess your intent. When I build a document, I define the structure, types, validation rules, and discount presentation, then let Qoder load it and write the front end, back end, and interfaces according to the rules, then run tests. This not only writes code—the best part is that I now write all of my industry solutions with AI, because it has my entire knowledge system.
Many people say AI is unstable. If a colleague is still saying "AI hallucinates a lot," don't listen—it's because they can't command it. If your Spec is written well, I'm telling you the output quality is very high, higher than the quality of everyone we had in the past. When it works by the standards, its execution ability is extremely strong.
Me alone plus Qoder is equivalent to doing the work of 20 people over three months. Having walked this path for so many years, my biggest feeling is that the dimension of my life has been extended. Things I used to say I could only do at 80, I might get done by the time I'm 50. We used to spend five years validating a project only to fail; now in a few months I know whether something can succeed.
Technological change brings commercial change, and only when commerce changes do you have an opportunity. Twenty years ago I was staying up late writing code; ten years ago I was doing cloud transformation; today, 20 years later, technology has changed. If there are any entrepreneurs among you, remember: the essence of AI Coding is command. Feed your memory and knowledge to AI as "private-domain knowledge," and it will be your strongest intern.
As long as you have ideas, you can create a great many things. Engineering records matter even more, because the stronger AI's capabilities become, the more it will fail to work the way you want without your constraints and conventions.
1. The Turning Point in Mindset: AI Is an "Intern," Not an "Omnipotent God"
Many people feel frustrated when they first use AI for programming, because they treat AI as a "god." But in reality, AI is essentially an "intern" with extremely powerful capabilities yet no business intuition whatsoever.
If you throw a coding instruction at AI and let it guess your intent, the result is usually chaos. AI doesn't understand your business, nor does it understand the logic a company has accumulated over 30 years. Therefore, transitioning from "coder" to "architect" is a path every developer must take—what you do is no longer writing code, but "commanding" and "defining."
2. Why Choose Qoder
When I first started, I took many detours. I only knew it worked fast; I didn't know how to write rules or how to write skills. At the very beginning I burned through more than a hundred thousand Credits, over 8,000 yuan a day, and at the time it felt amazing. But later on, the AI couldn't keep going, because it couldn't even modify its own work—all because I hadn't done much of the preparatory work up front.
Later, I rebuilt a very important part of the ERP—MES (Manufacturing Execution System). With just myself plus Qoder, I built a product in two months, and it's already being demoed and sold.
The advantages of Qoder lie in:
- "Solution Encapsulation" Matters More Than "Model Selection" The model itself is just a tool, whereas the core value of Qoder is that it "makes the choice for me." The Qoder team continuously evaluates and integrates the most powerful large models currently on the market, packaging them into a mature, engineered product. This "packaged practical experience" saves me the effort of frequently switching models and studying the underlying details, letting me directly use the most cutting-edge technical capabilities available today.
- The Efficiency Dividend from "Engineering Wrapping" AI development is essentially an engineering problem. Qoder is not just a model interface; it does deep engineering work at the application level. It tightly binds AI capabilities with the developer's IDE environment and project codebase. This "engineering wrap" makes AI's output far more productive, rather than staying at the level of mere chat conversation.
- A Deep Accounting of "Cost-Effectiveness" I chose it not out of "worshipping foreign things" or "blindly following the crowd," but based on the optimal cost solution: Time cost: Using the strongest combination of tools significantly boosts coding speed and greatly reduces the number of bugs. Opportunity cost: A person's time is a constant 24 hours. Making a product in the shortest possible time to seize the market window is far more important than fussing over model subscription fees. Deployment cost: Once you factor in labor costs and delivery cycles, Qoder's compute consumption is far cheaper than hiring real engineers—it's a rational business decision.
- A Leading "Expert Team" (Agent) Collaboration Mechanism