Recently, I found myself sitting in front of my system, exploring something new. I had heard about Claude before, mostly as a conversational AI chat tool and as a Figma MCP Server model, but this time I decided to go deeper into what is called Claude Code, especially through the terminal.
I must confess, I was blown away.
What fascinated me was not just what it could do, but how it changed the way I worked. I was no longer switching between design tools, documentation, and code editors in a fragmented way. Instead, I found myself designing and coding almost simultaneously, with a flow that felt natural and surprisingly efficient.
From my understanding, Claude exists in different forms, including chat-based interaction and collaborative environments, but what stood out to me was the coding experience itself. Claude Code, powered by advanced large language models, allows developers and designers to interact with code through natural language, generate functions, debug issues, and even reason through complex logic directly within the terminal.
Anthropic, the company behind Claude, designed it to be helpful, harmless, and honest, with a strong focus on safety and usability. This makes it not just powerful, but also reliable for real-world tasks.
The Shift I Experienced
As a digital product designer, I have always separated design and development in my workflow. I would design first, then hand over to development or switch contexts myself.
Claude Code changed that as now,
I could think about an interface, describe it, and immediately see how it translates into code. I could refine logic while still thinking about user experience. It felt like the gap between idea and implementation was shrinking in real time.
This is not just a productivity improvement. It is a shift in how we create.
A Real-World Example
A similar shift has been observed with tools like GitHub Copilot, which has been widely adopted by developers to accelerate coding tasks. Adopting tools like GitHub Copilot has transformed software development by enabling engineers to accelerate coding tasks and streamline their workflows. Recent empirical research confirms that integrating AI-powered coding assistants like GitHub Copilot into software development significantly enhances developer efficiency (Stray et al., 2025).
Specifically, controlled experiments have demonstrated that developers using these tools can complete complex tasks, such as implementing an HTTP server, roughly 55.8% faster than those using traditional manual methods (Bono & Xu, 2024; Stray et al., 2025). Furthermore, these productivity gains are often most pronounced among less experienced developers, helping to bridge skill gaps and maintain a sense of continuous progress during development (Alenezi & Akour, 2025; Stray et al., 2025).
Claude Code builds on this idea by going further into reasoning, context awareness, and conversational coding, making it especially powerful for complex workflows.
What This Means for UX and AI
This experience made me realize something important.
The future of UX is not just about designing interfaces. It is about designing systems where thinking, building, and refining happen together.
When designers can interact with code more naturally, they gain more control over the final experience. When AI supports that process, it reduces friction and accelerates creativity.
However, this also comes with responsibility. We must ensure that speed does not replace thoughtful design and that human judgment remains at the centre of what we build.
Conclusion
Working with Claude code or vibe-coding with other AI tools usually gives me this feeling of not just designing products but shaping ‘em in real time, from idea to execution.
That feeling is always powerful.
And it makes me believe that the future of design and development is not separate.
It is collaborative, fluid, and deeply human, even when powered by AI.
References
Alenezi, M., & Akour, M. (2025). AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions. Applied Sciences, 15(3), 1344. https://doi.org/10.3390/app15031344
Bono, J., & Xu, A. (2024). Randomized controlled trials for security copilot for IT administrators. arXiv. https://doi.org/10.48550/arxiv.2411.01067
Stray, V., Brandtzæg, E. G., Wivestad, V. T., Barbala, A., & Moe, N. B. (2025). Developer productivity with and without GitHub Copilot: A longitudinal mixed-methods case study. arXiv. https://doi.org/10.48550/arxiv.2509.20353
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