Guillaume Molter

A new guide called "Using GitHub Repositories with a Screen Reader" has just launched on our GitHub accessibility website 🚀 GitHub repositories are essential for collaborating on code, tracking issues, and managing projects. This guide provides step-by-step instructions to help screen reader users navigate and interact with repos efficiently 💖 Check it out and share with your network 🤩 https://lnkd.in/gU8YzSz9

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My first ever post on AI...and its not about the tech at all. It now seems plausible to me that within three years, one "AI assisted knowledge worker" will be able to do the work of ten traditional knowledge workers, across most industries. It also seems unlikely that demand for knowledge work will grow at the same pace. So shareholders will be looking to increase operating margins by reducing the number of employees. The first place to take the hit will be outsourced service jobs - as they are already set up with clearcut inputs and outputs, they'll be very easy to convert to AI. The most resilient job markets will be manual workers in primary and secondary industries, and care workers. A handful of tech giants in the US and China (is anyone else in the game?) will control the means of production for the global knowledge economy. I don't know if the scale factor will be 10x, or 2x, or 50x - and if the timeframe is 2027, or 2026, or 2030...but an increase in productivity on this scale will completely reshape work - painfully for many. If (like most people in tech) you're in a fully or partially automatible role, it may be the right time to start preparing to either (a) be part of the 10% who run the AI workplace or (b) get into a more resilient industry. (Personally I'm aiming to be part of that 10%, but there are a lot of smart people competing for my spot - so I'm keeping my woodworking shop and vegetable garden as back-up). To be clear, I am not "for" or "against" this outcome - I just think its the most likely one and it's better to be prepared – as individuals, as companies, as nations – for the consequences.

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I had the incredible opportunity to present at the Online Learning Consortium's Innovate Conference today! We delved into the essential foundations of building a user experience (UX) research and design team within higher education. Here’s a quick recap of the key takeaways: ✨ Cultivating a user-centered culture to ensure students' needs are at the forefront of product development ✨ Implementing effective UX research methods that provide actionable insights ✨ Designing solutions that elevate student engagement, success, and overall learning experiences. We explored how to create a thriving UX team that drives meaningful change in education settings. 🌍 Grateful to have had the chance to help unlock the power of UX in education and inspire others to elevate the student experience! 🙌 #HigherEd #UXDesign #UserExperience #StudentEngagement #EdTech #OnlineLearning #Leadership #UXInHigherEd

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Heads up: if you ever get interviewed by me, there’s a high chance I’ll ask: What is your unique design superpower? Last week I had to explain my own. My usual answer is: problem reframing. Over the years, I’ve learned how to help designers get “unstuck” by reframing problems and surfacing the assumptions hidden in their thinking. But today I realized something new: one of my so-called weaknesses is actually at the root of that superpower. I am the queen of diverging. 👍 Give me one problem, and I’ll generate twenty related issues and follow-up questions. 👍 Give me one solution, and I’ll open up a whole set of alternative paths—asking whether we’re even exploring the right direction in the first place. This tendency to dwell in the wide, messy space of issues and opportunities (sometimes for too long!) is what makes reframing possible. I don’t get attached to one explanation or answer, I keep the space open. So next time you’re stuck, try two things: Drop me a note and let’s see if my superpower can help. Or, experiment with AI. Ask it to reframe your problem from the perspective of different stakeholders or offer alternative solution spaces. See what new doors open up.

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We've been adding examples to the HAX Toolkit about mitigating overreliance on GenAI (use the filters GenAI and "Appropriate reliance"). I'll be highlighting a few in posts here, in no particular order: AI critiques might increase user vigilance and motivate users to double-check AI responses, by pointing out how a response might be incorrect. https://lnkd.in/gup6kf7e

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Engineers and product teams are already achieving unprecedented output with just copilots.  This trend is undeniable and will only accelerate.  Soon, designers won't just use AI tools; they'll orchestrate entire teams of UX Agents. To prepare for this future and stay relevant, I suggest you learn a few things: 1. How to move faster with AI 2. Improve design quality with AI 3. Designing intuitive UX for AI-driven applications. 4. Emerging design patterns and industry standards AI has influenced 5. Navigating the ethical responsibilities and boundaries of AI use. I am adding some classes, notes and guides discussing AI in UX for my students at SJSU. If you want to follow along and get the guide please check out: https://lnkd.in/gQn3n3yh and signup.

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