#design-validation

#design-validation

The /insights command analyzes the history of your commands in the last 30 days and generates a detailed HTML report with key findings and insights.

Voice interaction with Claude Code significantly enhances the user experience by allowing for faster input. Speaking is often 2-3 times quicker than typing, which can streamline the process of giving commands.

Santa Cruz de Tenerife is one of the most idyllic cities in the Canary Islands. At its heart stands the jewel - the Auditorio. It's a place where talent from both worlds, New and Old, comes together. A theatre, opera, dance, and music heaven.

Capacity Planning is the process of right-sizing the 'Total Project Demand' with the forecasted Team Capacity. Most UX teams have no idea what their capacity is. Fewer still have a process for calculating it and using it during quarterly planning activities with their counterparts in Product Management & Engineering to ensure teams don't commit to more work than they can handle.

Instructions I created. Instructions I am continuing to hone - instructions that required me to study my own old essays, identifying what I do when I write. The sentence rhythms. The way I move between timescales. The zooming in and out from concept to detail. The instructions tell Claude how I would like ideas composed. I pull together concepts and experiences from my lived expertise to formulate a point of view - in this case, on this new AI technology.

Her payment form wasn't connecting to the payment processor, and every attempt ended in an error message that made no sense. I understood her frustration. As a founder myself, I was acutely aware of the pain of trying to run a business and feeling like nothing was going your way. When I dug into her form, I found the problem a few minutes later: a mismatch between test mode and live credentials.

It's been almost 20 years since I started my career in product design, and, as you might imagine, many things have changed dramatically since then. One of the main characteristics of the technology industry is the constant evolution of its dynamics, roles, processes, technologies, experiences, and even business models. Those changes are inevitable and will continue. In retrospect, I see that there is one reality that has not changed much over the last 20 years and remains a constant issue to this day: building technology products can sometimes be a discouraging and exhausting process, from junior positions to senior management levels. Why do we suffer every time we need to build something? Why is there so much burnout among today's tech professionals? Why is it that, regardless of the industry, company, or technology, we always hear the exact phrases: "I'm exhausted, I feel drained by this job."? Well, those are valid questions that still haunt me 20 years after my first web design job. It seems like there's no choice in this environment but to suffer.

Maybe this stake is more prominent in start-up environments, where new ideas surface every day and the opportunity for growth is potentially wider. Philosophies like "Build fast, fail fast" are at the core of an agile mindset, helping us determine whether an idea is viable in the early stages or whether a pivot is necessary to achieve the desired numbers and experience.

The normative form for interacting with what we think of as "AI" is something like this: there's a chat you type a question you wait for a few seconds you start seeing an answer. you start reading it you read or scan some more tens of seconds longer, while the rest of the response appears you maybe study the response in more detail you respond the loop continues

Model Context Protocol (MCP) is a technology that enables AI models to connect with external tools and data sources (such as GitHub, Slack, databases, and documentation systems). In this article, I want to explore my top 7 favorite MCPs you can use in your design process. I will cover not only benefits but also limitations of the each MCP so you will have a clear idea about what you can & cannot do with it.

AI is disrupting more than the software industry, and is doing so at a breakneck speed. Not long ago, designers were deep in Figma variables and pixel-perfect mockups. Now, tools like v0, Lovable, and Cursor are enabling instant, vibe-based prototyping that makes old methods feel almost quaint. What's coming into sharper focus isn't fidelity, it's foresight. Part of the work of Product Design today is conceptual: sensing trends, building future-proof systems, and thinking years ahead.

Something's been slowly shifting in the design zeitgeist. I've been watching my feed on X and the vibe has changed. More and more, I see designers sharing finished experiments or prototypes they coded themselves, rather than static Figma files. Moving from working on a canvas to talking to an LLM. The conversation isn't "here's a design I made" anymore... it's "here's something I shipped this afternoon."

"You know, having those conversations early on, reaching out to people in different departments ...that was really hard when I didn't have much confidence.” A VP of Design brought this up recently, reflecting what many designers are facing. There's been a crisis of confidence in design, and it's happening all across the career ladder. Due to shrinking budgets and layoffs, more designers are being forced to work solo.

During my eight years working in agile product development, I have watched sprints move quickly while real understanding of user problems lagged. Backlogs fill with paraphrased feedback. Interview notes sit in shared folders collecting dust. Teams make decisions based on partial memories of what users actually said. Even when the code is clean, those habits slow delivery and make it harder to build software that genuinely helps people.

AI design tools are everywhere right now. But here's the question every designer is asking: Do they actually solve real UI problems - or just generate pretty mockups? To find out, I ran a simple experiment with one rule: no cherry-picking, no reruns - just raw, first-attempt results. I fed 10 common UI design prompts - from accessibility and error handling to minimalist layouts - into 5 different AI tools. The goal? To see which AI came closest to solving real design challenges, unfiltered.