Figma-to-code tools promise fast design-to-dev handoffs, but they often deliver messy code and limited functionality.

Startups don t fail because of poor designs - they fail because they can t ship scalable systems fast enough.

At http://CanvasEight.io, we re rethinking this. Instead of chasing pixel-perfect exports, we focus on semantic code structure and intent.

Our platform maps designs to logic, data and user flows, scaffolding infrastructure-aligned modules in real time.

Figma-to-code tools promise fast design-to-dev handoffs, but they often deliver messy code and limited functionality.

Startups don t fail because of poor designs - they fail because they can t ship scalable systems fast enough.

At http://CanvasEight.io, we re rethinking this. Instead of chasing pixel-perfect exports, we focus on semantic code structure and intent.

Hey everyone, I'm Marcello Cultrera - co-founder building CanvasEight.io

Our platform converts autonomously Figma designs into production ready code in seconds.

What we do: Drop in a clean Figma landing page design and we interpret the semantic intent to generate structured code in real-time. No more playing telephone between design and development.

AI coding tools seem to come in two main flavors: IDE-based, like @Cursor and @GitHub Copilot, and terminal-based setups, like using @Claude Code to generate commands, scripts, or entire files. Both have their fans, but which one actually helps you move faster?

Curious what flow people are sticking with long term, and where you see the most gains (or frustrations).

AI coding tools seem to come in two main flavors: IDE-based, like @Cursor and @GitHub Copilot, and terminal-based setups, like using @Claude Code to generate commands, scripts, or entire files. Both have their fans, but which one actually helps you move faster?

Curious what flow people are sticking with long term, and where you see the most gains (or frustrations).