I studied Electrical and Electronic Engineering back when "coding" was just one of many tools engineers used—more for embedded systems than for building web apps. After years in product, operations, and tech leadership roles, I found myself gravitating back toward software. But this time, something had changed: AI was no longer theoretical. It was building things. Three years ago, when large language models (LLMs) started showing real-world potential, I had what I thought was a brilliant idea: why not automate the design-to-code process entirely? I imagined a future where developers could skip the tedious translation from Figma to React, and I figured—with enough AI—it would be a solved problem in no time. why not automate the design-to-code process entirely? Spoiler: it’s… not quite that simple. Back when I was still writing JavaScript and wrestling with CSS, "automated UI engineering" wasn’t even a concept. Fast forward to today, and we’ve got powerful frameworks like React, design systems in Figma, and AI tools everywhere. And yet, translating a polished design into scalable, production-ready code is still frustratingly manual—riddled with edge cases, responsiveness hacks, and grunt work. That gap is what led me to build Bitloops. And while we’ve come further than most in bridging it, let’s just say I have a lot more empathy now for every frontend dev who’s tried to “automate” anything. It’s not magic—it’s work. What’s broken in Design-to-Code today We’ve seen plenty of tools promise one-click design conversion. Figma plugins, Sketch integrations, even some promising browser extensions. But let’s be real: most of them generate code that no professional developer would ship. We're talking about: Inline CSS everywhere
Duplicated, unstructured components
No sense of responsiveness
Zero test coverage
And no support for developer workflows or design systems Inline CSS everywhere Duplicated, unstructured components No sense of responsiveness Zero test coverage And no support for developer workflows or design systems It’s like getting a 3D-printed part that looks like the prototype but crumbles on use. Why full automation Is harder than It looks Let’s break down why automating design-to-code is still one of the hardest problems in AI-assisted development: 1. Responsiveness Isn’t Just Media Queries Most tools guess breakpoints or ignore them altogether. Bitloops analyzes element relationships and builds rules around what collapses, stacks, or disappears on smaller viewports. You can’t hardcode this stuff. 2. Code Quality Still Matters Just because a machine can generate code doesn’t mean it should. We’ve all tried pasting ChatGPT-generated snippets into our apps—only to spend more time refactoring than writing from scratch. Bitloops prioritizes maintainable, professional-grade output. can 3. Testing Can’t Be an Afterthought Code is only as good as its testability. That’s why our generated components include the scaffolding for testing. You can plug them into your existing test suite or run them in isolation using Storybook. 4. The Grunt Work Is Real Developers spend hours exporting SVGs, organising font files, and copy-pasting CSS. Bitloops handles this—all of it. We don’t just want to save you time; we want to keep you in flow. So, can AI fully automate Design-to-Code? Not yet. But we’re getting close. Soon we’ll have tools that are able to convert 80–90% of the frontend surface area of a new product—cleanly, scalably, and in a way that fits into your existing project. But that final 10–20%? It still needs you. Why? Because LLMs can’t yet reason about edge cases, data states, or UX logic like a human developer can. They don’t know that a button might trigger a modal under certain conditions, or that a component should be reused across multiple screens with minor variations. The road to full automation is a marathon, not a sprint. What’s missing isn’t just better AI models. It’s structured workflows, exception handling, and the ability to reason about context. That’s what Bitloops is building toward. context Bitloops’ approach: Translate Intent, not just Layout Intent Intent Layout Bitloops, the company I founded with Vasilis Danias, doesn’t just generate code—it replicates how a senior engineer would think about that code. We go beyond the pixel-level mapping. Instead, we interpret the relationships, hierarchies, and functional intent behind your Figma design. think What you get isn’t just JSX—it’s: Modular components that match your design system
Responsive layouts with proper breakpoint logic
Organized folder structures
Semantic markup
Tests (yes, actual Storybook or unit test templates)
And automation of all the grunt work like asset exports and style extraction Modular components that match your design system Responsive layouts with proper breakpoint logic Organized folder structures Semantic markup Tests (yes, actual Storybook or unit test templates) And automation of all the grunt work like asset exports and style extraction This isn't about replacing the developer. It’s about letting devs skip the drudgery and jump straight into the real engineering work. The bottom line: You still need to know how to code Yes, AI tools like Bitloops are revolutionising the way we write frontend code. But they’re not replacing software engineering anytime soon. What they are doing is removing the repetitive, low-leverage parts of the job—so you can spend more time architecting systems, solving real problems, and building features that matter. are So here’s the real takeaway:AI can now help you go from design to code in minutes.But to turn that code into a real, valuable product?You still need to think like a developer. Let me know what you think. What’s the hardest part of design-to-code in your workflow today?What would a real Frontend Copilot look like for you? Let me know what you think.

This story contains new, firsthand information uncovered by the writer.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

AI Can't Automate Design-to-Code, so Don't Quit Learning to Code Just Yet!

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