I really enjoy reading about the two real-world examples the author gives here for the "And-But-Therefore" template. Seeing how it applied to both a service and a product makes the concept make sense for me in a way the theory alone didn't. I could actually picture how I might use it in a real project. It is also interesting when it is said that these examples aren't the "public-facing language," but the "DNA of your core narrative." That made me think more deeply about how much thought has to go into the foundation of a campaign, not just the fancy sentences we put in ads. It just made me realize that marketing isn’t just about listing features but actually about telling a story that shows how a problem is solved.

Reading this made me realize how critical dataset composition is for AI performance. I think it is alarming that a model’s accuracy can vary so drastically simply because of the demographic skew in its training data. I think the call for intersectional bias audits is a very important step for improving not just fairness but also the reliability of ML systems in real-world applications. I also really admire the work of Buolamwini and Gebru here. How they create a whole new dataset just to show these biases is a really powerful and necessary intervention. This makes me even more committed to thinking critically about the ethical side of tech my own life and studies. I want to be intentional about the data I use, the assumptions I make, and the potential impacts my work could have on marginalized communities.

I think usability tests are very useful for identifying interface breakdowns. However, they can’t show whether a design truly provides value in real life. Designers often focus on whether users can complete tasks in a controlled setting. They assume that success there means success in the real world. As Ko points out, task completion alone doesn’t measure whether someone would actually want to use the product or integrate it into their routine. I’ve seen prototypes work perfectly in tests but fail when deployed because the tasks felt artificial or didn’t meet real user needs. That’s why real-world testing is so important. It’s absolutely harder and takes a lot more time, but it truly shows how people actually use a design in their daily lives.

This part really changed my mind. I agree that prototyping isn’t about making something perfect, but about learning through testing. For example, in a project last quarter, I spent hours creating a polished mockup for a class app prototype without testing it with anyone. When I finally received feedback, I realized some of my design assumptions were completely wrong, and much of my work went to waste. Ko’s point makes me see that starting with quick sketches or paper prototypes can be much more effective, even if they look messy. It also reminds me to focus on what questions I want answered before building anything, so I can learn as much as possible from each test. For current group project in class and my future projects, I believe this approach will absolutely save a lot of time and also improve my design decisions,.

I more than resonated with this point because I’ve seen how easy it is to fall into the trap of copying competitors, especially when people are under time pressure or unsure about their design choices. In my experience, copying rarely leads to a product that feels unique or truly user-centered. It can even make the experience worse if the original design has flaws. I like that the author focuses on using competitor research as inspiration rather than a blueprint, because it encourages critical thinking and creativity. Reading this makes me want to approach UX research more intentionally, asking not just “what did they do?” but “why did they do it, and how can I make it better for my users?” This mindset is exactly what I want to achieve, and I believe this really helps me create designs that stand out but are still practical and effective.

I feel this “hamburger” rule seems a little formulaic at first, but I realize it does work. I agree with Ko that it forces critics to find good aspects, even when a design appears weak overall. I know from my own experience that if I only receive negative comments, I will not have the motivation to solve that. However, if that is mentioned well, I'm much more open to hearing the criticism. This approach makes people slow down and actually notice the good parts of a design, instead of just tearing it apart.

I agree with this idea because I believe everyone can be creative in their own way. Creativity doesn’t have to mean inventing something completely new, but it can come from noticing problems or ways things could be improved. I realize that I’ve sometimes doubted my ideas because they felt small or “obvious,” but this reading helped me see that even those ideas have value.

I agree with Ko’s point here that personas are only valuable if they are grounded in real data. I’ve seen how easy it is to make up a persona that sounds “real,” but doesn’t actually match people’s lives. When that happens, the design feels off because it’s built on assumptions instead of facts. I believe that "good design" isn’t just about collecting data, but also about respecting and reflecting people’s actual lives.