Brilliant. This experiment really highlights AI's power to accelerate the *discovery* phase, not just execution. Makes you reflect on what 'developer experience' truly means when AI is so integreated.
The ORM-like API for Alpine.js is brilliant. Using AI to rapidly prototype multiple design philosophies in 90 minutes shows just how powerfull AI can be as a whiteboarding tool. The jump from messy dictionaries to Alpine.at.click.prevent.once feels so much more natural, and getting that iterativ feedback loop with working prototypes is exactly how AI shines in development workflows.
What you’re describing—fast iteration with an LLM acting as a flexible assistant—is the right start. But the real unlock for prototyping isn’t a stronger model; it’s Modular Intelligence (MI): treating the LLM as one component inside a governed engineering stack rather than the whole system.
MI adds architectural structure around the model:
• Specification module to formalize requirements, interfaces, and invariants
• Constraint/safety module to enforce correctness, security, and policy boundaries
• Design-exploration module to generate alternative approaches
• Verifier module to check consistency, logic, and code-level correctness
• Adversarial module to test edge cases, failure modes, and regressions
• Memory/state module to track assumptions, dependencies, and iteration history
This changes prototyping from “LLM generates code and we hope it works” to a disciplined, multi-step pipeline where generation, critique, verification, and refinement are separate modules.
Result:
• fewer hallucinated APIs,
• cleaner architectural decisions,
• spec-driven development instead of prompt-driven drift,
• and prototypes that are structurally sound rather than just syntactically correct.
LLMs accelerate coding.
Modular Intelligence accelerates engineering.
That’s the shift that turns AI-assisted prototyping into reliable, repeatable system design.
Brilliant. This experiment really highlights AI's power to accelerate the *discovery* phase, not just execution. Makes you reflect on what 'developer experience' truly means when AI is so integreated.
The ORM-like API for Alpine.js is brilliant. Using AI to rapidly prototype multiple design philosophies in 90 minutes shows just how powerfull AI can be as a whiteboarding tool. The jump from messy dictionaries to Alpine.at.click.prevent.once feels so much more natural, and getting that iterativ feedback loop with working prototypes is exactly how AI shines in development workflows.
What you’re describing—fast iteration with an LLM acting as a flexible assistant—is the right start. But the real unlock for prototyping isn’t a stronger model; it’s Modular Intelligence (MI): treating the LLM as one component inside a governed engineering stack rather than the whole system.
MI adds architectural structure around the model:
• Specification module to formalize requirements, interfaces, and invariants
• Constraint/safety module to enforce correctness, security, and policy boundaries
• Design-exploration module to generate alternative approaches
• Verifier module to check consistency, logic, and code-level correctness
• Adversarial module to test edge cases, failure modes, and regressions
• Memory/state module to track assumptions, dependencies, and iteration history
This changes prototyping from “LLM generates code and we hope it works” to a disciplined, multi-step pipeline where generation, critique, verification, and refinement are separate modules.
Result:
• fewer hallucinated APIs,
• cleaner architectural decisions,
• spec-driven development instead of prompt-driven drift,
• and prototypes that are structurally sound rather than just syntactically correct.
LLMs accelerate coding.
Modular Intelligence accelerates engineering.
That’s the shift that turns AI-assisted prototyping into reliable, repeatable system design.