Background

Achieving rapid MVP deployment within a three-month timeline presents unique challenges, particularly when balancing speed with the complexity of multi-layered architectures. The necessity lies in developing robust systems quickly without succumbing to technical oversights common in hastily written code.
This reflects our approach to AI-ready UX-architecture, where delivery speed is aligned with contextual intelligence and long-term system reliability.

What We Tried (and Why)

Our approach embraced a technology stack designed to maximize development velocity while maintaining quality. By integrating Cursor IDE with GPT-5 and Claude, we aimed to leverage AI not only for code generation but also for architectural insights, thus compressing development cycles without sacrificing implementation depth.

What Broke or Didn’t Work

While our setup introduced significant velocity, we encountered notable “blind spots” where AI-generated code lacked logical oversight:

  • GPT-5 occasionally over-simplified complex logic, resulting in human-like but technically incorrect code.
  • The AI introduced architectural inconsistencies, such as redundant caching databases, reflecting its struggle with comprehensive integration tasks.

📌 Takeaway: AI can generate code at unprecedented speed, but human oversight remains crucial to ensure logical cohesion.

The Shift We Made

To mitigate these AI-related pitfalls, we bolstered our workflow with increased human oversight. Developers’ roles expanded to focus on AI output validation and architectural integrity, ensuring that the generated components meshed seamlessly with our overall system.

What Worked (and What Still Doesn’t)

The incorporation of AI resulted in a 2–3x increase in development velocity, allowing us to meet our project deadline efficiently. However, dependency on human oversight persists as AI still lacks the capability for holistic problem comprehension on its own.

Tradeoffs and Strategic Decisions

Maximum Automation Human Oversight
Speed and volume of generated code Ensures logical and architectural coherence
Risk of oversights and inconsistencies Increased resource allocation to QA

Open Questions We’re Still Exploring

  • How can we enhance AI’s capability to self-validate its code logic?
  • What structures can be implemented to ensure comprehensive code oversight?
  • How do we train AI models to interpret and integrate with larger codebases effectively?

If You’re Solving Something Similar…

Your insights and experiences could significantly enhance the collective understanding of AI-driven development workflows. If you’re leveraging AI like GPT-5 in your processes or have strategies to handle AI “hallucinations,” we invite you to share your thoughts.

We believe we can reframe software delivery from the ground up, where every decision, tool, and interaction is guided by contextual intelligence.

— Cadabra Studio

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