Elizabeth Goodspeed on the importance of taste – and how to acquire it

AI image generation is essentially a truncated exercise in taste; a product of knowing which inputs and keywords to feed the image-mashup machine, and the eye to identify which outputs contain any semblance of artistry. All that is to say: AI itself can’t generate good taste for you.

Elizabeth Goodspeed on the importance of taste – and how to acquire it

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Let’s reinvent the wheel ⚒ Nerd

Vasilis gives the gist of his excellent talk at the border:none event that just wrapped up in Nuremberg. The rant at the end chimed very much with my feelings on this topic:

I showed a little interaction experiment that one of my students made, with incredible attention to detail. Absolutely brilliant in so many ways. You would expect that all design agencies would be fighting to get someone like that into their design team. But to my amazement she now works as a react native developer.

I have more of these very talented, very creative designers who know how to code, who really understand how the web works, who can actually design things for the web, with the web as a medium, who understand the invisible details, who know about the UX of HTML, who know what’s possible with modern HTML and CSS. Yet when they start working they have to choose: you either join our design team and are forced to use a tool that doesn’t get it, or you join the development team and are forced to use a ridiculous framework and make crap.

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The machines won’t save your design system — Hey Jovo Design

Every day, a new marketing email, Medium post, or VC who will leave Twitter when they’re cold in a body bag tells us that machine learning (ML, which they call AI because it sounds more expensive) is going to change the way we work. Doesn’t really matter what your job is. ML is going to read, write, code, and paint for us.

Naturally, the excitement around ML has found its way into the design systems community. There’s an apparent natural synergy between ML and design systems. Design systems practitioners are tantalized by the promise of even greater efficiency and scale. We wish a machine would write our docs for us.

We are all, every single one of us, huge fucking nerds.

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A visual introduction to machine learning

I like the split-screen animated format for explaining this topic.

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Copying is the way design works || Matthew Ström: designer & developer

A people’s history of copying, from art to software.

Designers copy. We steal like great artists. But when we see a copy of our work, we’re livid.

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How can designers get better at learning from their mistakes?

Jon has seven answers:

  1. Build a culture to learn from mistakes
  2. Embrace healthy critique
  3. Fail little and often
  4. Listen to users
  5. Design. Learn. Repeat
  6. Create a shared understanding
  7. Always be accountable

It’s gratifying to see how much of this was informed by the culture of critique at Clearleft.

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