Day 2 - Agentic Engineering/VIbe Coding an AI App from Zero To Production | 🔴 Live

2026-05-05 · Source: Venelin Valkov · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

A developer live-streamed the process of building a habit/goal tracker application prototype using AI agents and specific skills within the Codex CI environment, powered by GPT-5.5. The session began with a brief mention of a multi-token prediction (MTP) improvement for Llama.cpp, promising over 2x speedup. The core of the stream focused on generating a Product Requirements Document (PRD) and then a Minimum Lovable Version (MLV) prototype specification using modified "interview me" and "to PRD" skills. The developer then used a "to issues" skill to break down the MLV into nine actionable tasks, which GPT-5.5 subsequently implemented into a React and TypeScript application with Tailwind CSS and Shadcn components. Although the resulting UI was deemed "quite ugly," the prototype successfully demonstrated core functionalities like goal and habit creation, linking, and local state persistence, validating the underlying data model.

Key takeaway

For AI Engineers building new applications, consider adopting an AI-driven workflow for project initiation. Your team can leverage tools like Codex CI with custom skills to rapidly move from abstract ideas to a functional prototype, even if the initial UI requires significant iteration. This approach allows you to quickly validate core data models and application logic, providing a solid foundation before investing heavily in front-end design or complex features.

Key insights

AI agents can rapidly generate functional prototypes and manage project workflows from high-level requirements.

Principles

• Iterative interviews refine project scope.
• Structured issue generation streamlines development.
• Context quality outweighs model size for refactoring.

Method

Use an "interview me" skill to crystallize ideas into a PRD, then a "to issues" skill to create tasks. An AI agent then implements these issues, focusing on a vertical slice for rapid prototyping.

In practice

• Employ AI-driven interview skills to clarify project requirements.
• Break down complex features into discrete, agent-ready issues.
• Prioritize a functional data model over initial UI aesthetics in prototypes.

Topics

• Agentic Engineering
• AI Application Development
• Product Requirements Document
• Minimum Lovable Version
• Habit Tracker

Best for: AI Engineer, Prompt Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Venelin Valkov.