How I Built a 5‑Minute Full‑Stack Feature with AI: Lessons Toward a Real‑World Jarvis

Introduction

The article recounts the author’s hands‑on experience using AI coding assistants such as Vibe Coding and Claude Code, describing how AI can take a high‑level request—e.g., “create a user login page”—and automatically browse project files, understand the codebase, and produce runnable code without constant supervision.

How I Collaborate with AI

Clear initial brief: Define the goal, provide background information, and list constraints so the AI knows the boundaries.

Dynamic task sizing: Start with a broad objective, then split into finer subtasks only when the AI stalls or misinterprets.

Decision points: Identify moments where the AI must choose a solution, handle exceptions, or balance trade‑offs, and intervene to keep the work aligned.

Feedback‑driven iteration: Treat each deliverable as a validation checkpoint rather than a time‑boxed sprint.

What’s Missing Compared to a “Jarvis” Assistant

The current AI agents lack three key capabilities:

Continuous memory: Every session requires re‑explaining context; there is no persistent work memory.

Intent alignment: Repeated clarification rounds are needed because the chat model often misinterprets the user’s intent.

Decision autonomy: Critical choices (task splitting, priority setting, quality assessment) still rely heavily on human judgment.

These gaps motivate a more systematic, template‑based approach.

Path Forward: Template‑Based Pipeline in Next.js

In a Next.js full‑stack project the author built a standardized workflow that lets AI generate a complete CRUD feature in about five minutes. The pipeline consists of three layers: a shared schema, Server Actions, and UI components.

// Form schema – defines what the user must fill
const attractionFormSchema = z.object({
  name: z.string().min(1, '名称不能为空'),
  cityId: z.string().min(1, '请选择城市'),
  minDays: z.coerce.number().int().positive(),
  imagePaths: z.array(z.string()).optional()
});

// List schema – defines what is displayed
const serializableAttractionSchema = attractionFormSchema.extend({
  id: z.string(),
  createdAt: z.date(),
  city: z.object({ id: z.string(), name: z.string() }),
  images: z.array(z.object({ path: z.string() }))
});

Server Action (backend function) reuses the same schema for validation:

export const createAttraction = authActionClient
  .inputSchema(attractionFormSchema)
  .action(async ({ parsedInput }) => {
    const attraction = await prisma.attraction.create({ data: parsedInput });
    return { success: true, data: attraction };
  });

Frontend form uses zodResolver(attractionFormSchema) for client‑side validation, and the submit handler simply calls createAttraction(values) as if it were a local function.

const form = useForm({ resolver: zodResolver(attractionFormSchema), defaultValues: { name: '', cityId: '', minDays: 1 } });
const onSubmit = async (values) => { await createAttraction(values); };

A configuration‑driven table renders the data using a column definition array, completing the end‑to‑end feature without additional boilerplate.

Decision‑Making Rules

Task‑splitting signal: If the AI asks more than three clarification questions, the task is too large and should be broken down.

Direction‑adjustment signal: Two consecutive revisions without improvement indicate a need to pause and rethink the approach.

Documentation sync rule: Whenever the database schema changes, the AI should prompt to update API docs.

Task‑Type Classification

Execution‑type (AI‑led): Clear standards, e.g., CRUD development, code refactoring, bug fixes.

Exploration‑type (human‑AI balance): Requires multiple alternatives, e.g., performance tuning, algorithm selection, architecture design.

Creative‑type (human‑led): Needs aesthetic judgment, e.g., UI design, product positioning, copywriting.

Learning‑type (bidirectional): User is unfamiliar with the domain, e.g., learning a new framework or understanding complex code.

Key Takeaways

Standardized, template‑driven tasks dramatically increase AI efficiency, especially for repetitive CRUD work. Recording decision processes and reusing schemas across front‑ and back‑end ensures consistency and reduces cognitive load.

Final Thoughts

The author envisions an “AI Organizer” system with three layers—memory, execution, and learning—that would capture templates, track decisions, and continuously optimise collaboration, moving us closer to a practical Jarvis‑like assistant.