Tools, Resources & Workbench ·
U Nanda has launched a new course on "Standard Drive Development."
English translation of the Chinese original. This version is generated for international readers and may be refined over time.
English translation of the Chinese original. This version is generated for international readers and may be refined over time.
Date: 2026-04-19
Background
Two days ago, U Nda launched a new course called Spec-Driving Development with Coding Agencies, translated as "System-Driven Development."
This class appears at a delicate point in time. The concept of vibe coding is popular -- you describe demand, AI writing code, really fast. But soon you'll find one problem: it's fast enough to come out of code that doesn't meet the requirements, there's too many dirty codes, and it's more tiring to change.
That's how it hurts.
Why did he choose IDA instead of AI IDE
It is worth noting that U. N. did not choose Cursor, VS Code, the AI Native IDE, but instead used the relatively traditional IntelliJ IDA.
Speaking of IDA, it's estimated that people who are interested in this field are touched. This IDE king, who had ruled the Java domain for many years, in the current AI era, market share is declining year by year. Young developers are increasingly using lighter tools, and AI features are eroding the traditional IDE moat from every angle.
But the fact that U. N. D. A. chose IDA makes perfect sense. His goal is not to show what AI can do, but to teach you how to work with AI. In an environment you already know, where norms drive development, attention can be focused on methodology rather than being distracted by the use of tools.
What's a norm-driven development
The core concept of the curriculum is simple: ** Write the norms first and let AI achieve**.
Specific processes are:
- Prepare detailed instruction document (Spec) defining what you want to build
- Letting the code-based achievement
- Use specifications to verify the correctness of the output
It's not a new concept. In the development of traditional software, it is essential to write specifications and then code.
But in the context of AI coding, the methodology is given a new meaning - the problem with vibe coding is essentially a "northless rule": your instructions to AI are too vague, and AI, in its own understanding, guesses that nature can run away.
The example given by U Nanda in the course is practical: instead of "help me write a module to process user login", the login process, error processing, boundary profile, interface specifications are all documented, so AI can follow the document. It's much more stable.
What else does this class teach?
According to the DeepLearning. AI platform, this course mainly covers:
** Regulates how documents are written**. It is not a formalistic template document, but a truly useful specification - functional boundaries, input output formats, error-processing strategies, acceptance standards.
** How to collaborate with coding anent**. Including the writing and the iterative form of the command, when AI should decide for itself and when it should be clearly bound.
** Actual case-driven**. Take you through the standard-driven process in the real project.
"Building AI events that ship to program, not just imprests in demos."
The background to this is that a lot of AI coding shows now look amazing, but it's really used in actual projects where code quality, boundary processing, and long-term maintenance are problematic. Normatively driven development is a response.
In the Agent curriculum of U Nanda Look.
The Spec-Driven Development with Coding Agencies is not the only recent Agent course in U Nanda.
He has also published Agenic AI, which describes the design model of Agent: reflection, tool use, programming, multi-agent. And "Agent Skills with Anthropic" with Anthropic, teaching you how to use Claude's skills.
The combination of these classes shows the complete judgment of U Nda on the AI coding tool chain:
- Agenic AI on design mode
- Agent Skills.
- Spec-Driven Development on workflow methods
On three levels, from "AI can do what" to "how to do what" to "how to do what to do with "AI."
The normatively driven development problem is that it requires you to figure out what to do first.
It's a natural process for experienced developers, but sometimes it's more efficient for beginners or fast-discovery stages to get AI to run a prototype and adjust it before writing norms.
The exploration phase quickly validates the idea with vibe coding, moving in a clear direction to normative-driven development to enhance code quality - both approaches are more practical than otherwise.
U Nda actually admitted it in class. Instead of saying that vibe coding is bad, he said that "many good developers have adopted" a regulatory-driven approach. The subtext here is: it's not the only path, but it's worth understanding.
If you're often confronted with the problem of "out of code not what I want" when you use the AI coding tool, this lesson is worth looking at.
Core values don't teach you to write better prompt, but to teach you a higher-level method of working. How to figure out how to translate vague demand into clear norms and how to verify whether AI's output meets expectations.
These capabilities will become increasingly important in the AI era.
Models will grow stronger, but human judgment - knowing what to do, knowing how to do it, knowing how to prove it - remains irreplaceable.
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