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.

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

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Why do AI company logos look like buttholes?

You won’t be able to unsee this. It’s like the FedEx logo …if the arrow was an anus.

  1. Circular shape (often with a gradient)
  2. Central opening or focal point
  3. Radiating elements from the center
  4. Soft, organic curves

Sound familiar? It should, because it’s also an apt description of… well, you know.

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AI ambivalence | Read the Tea Leaves

Here’s the main problem I’ve found with generative AI, and with “vibe coding” in general: it completely sucks out the joy of software development for me.

I hate the way they’ve taken over the software industry, I hate how they make me feel while I’m using them, and I hate the human-intelligence-insulting postulation that a glorified Excel spreadsheet can do what I can but better.

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Another uncalled-for blog post about the ethics of using AI | Clagnut by Richard Rutter

This is a really thoughtful piece by Rich, who’s got conflicted feelings about large language models in the design process. I suspect a lot of people can relate to this.

What I do know is that I find LLMs useful on occasion, but every time I use one I die a little inside.

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Build It Yourself | Armin Ronacher’s Thoughts and Writings

We’re at a point in the most ecosystems where pulling in libraries is not just the default action, it’s seen positively: “Look how modular and composable my code is!” Actually, it might just be a symptom of never wanting to type out more than a few lines.

It always amazes me when people don’t view dependencies as liabilities. To me it feels like the coding equivalent of going to a loan shark. You are asking for technical debt.

There are entire companies who are making a living of supplying you with the tools needed to deal with your dependency mess. In the name of security, we’re pushed to having dependencies and keeping them up to date, despite most of those dependencies being the primary source of security problems.

But there is a simpler path. You write code yourself. Sure, it’s more work up front, but once it’s written, it’s done.

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Hallucinations in code are the least dangerous form of LLM mistakes

The moment you run LLM generated code, any hallucinated methods will be instantly obvious: you’ll get an error. You can fix that yourself or you can feed the error back into the LLM and watch it correct itself.

Compare this to hallucinations in regular prose, where you need a critical eye, strong intuitions and well developed fact checking skills to avoid sharing information that’s incorrect and directly harmful to your reputation.

With code you get a powerful form of fact checking for free. Run the code, see if it works.

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