AI-pril Stuff

This is me in this time and age 👇 This is everyone.

I'm always trying to stay balanced and keep a fair perspective on things, so this meme doesn't mean that I haven't found several successful use cases for injecting AI in the design process.

That said, when it comes to UI, I feel that the "AI is faster, it's about speed" argument doesn't really hold up. Most designers still end up spending more time fixing AI-generated UI than just designing it themselves.

With this relatable + venting over the AI hype meme unpacked, let’s go explore:

💡 Key articles to read

⚒️ Tools to experiment with (along with scenarios and use case recommendations)

🎙️ Podcasts and talks that matter

📰 Relevant news

🧠 Notable ideas to reflect on (monthly key takeaways)

📚 AI Dictionary: Word of the Month - explanations of essential AI concepts

🙋‍♀️ Personal news & updates - what I’m up to

💡 Key articles to read

I’m turning this Instagram page on a prompt library for designers. You might want to follow if you want bite-sized prompts + outcomes on a variety of design related creations: from customer journey maps, to interview questions, to visual experiments, mockups, and so on. I dream that this will become the largest coolest prompt library for UX & Product Designers in the whole wide world. 🥹

And a bonus written by yours truly to help you cut through the noise with the same (smart & informed!) sources that I use. 😎

⚒️ Tools to experiment with

Also, Framer is moving quite quickly in the direction of replacing Figma, so if you haven’t played around with it yet, start now 💙

🎙️ Podcasts and talks that matter

Well, this edition we have a list of articles that matter from Luke Wroblewski. A wealth of knowledge right here! (doesn’t look great but the insights are great, I promise)

📰 Relevant news

And I’ll stop here because I don’t want to burn everyone out. 😅

🧠 Notable ideas to reflect on

Two must reads 👇

Also, this article.

📚 AI Dictionary: Word of the Month

🪙 Token = the basic units of input and output in AI models, particularly in natural language processing (NLP). They can represent individual words, sub-words, or even characters. Tokenization is the process of breaking down text into these smaller units, enabling the model to process and understand the input more effectively. For instance, consider the word “unbelievable.” A word-level tokenizer would treat this as a single token, whereas a sub-word tokenizer might break it down into smaller units like “un”, “believ”, and “able”. This allows the model to handle out-of-vocabulary words more effectively by recognizing familiar sub-word components. For most LLMs, on average 75 words will be converted to approximately 100 tokens.

🍔 Hungry for more AI vocabulary? Here’s the source.

🙋‍♀️ Personal news & updates

Yesterday I recorded a podcast with one of the designers I’ve admired most throughout my career - Michael Riddering (Ridd) - for his Dive Club podcast, and I just want to tell the world that this was my favourite design conversation ever, so stay tuned for it! ❤️‍🔥

*Ridd is the creator of Figma Academy, yes.

✈️ Update on my next travel plans! The events I’m attending in the next couple of weeks are (1) this amazing conference happening in Amsterdam on May 1st to May 3rd - hope to meet you all there ❤️‍🔥

and (2) in the second half of May 😱 I’m flying to Canada 🇨🇦 to speak as a keynote guest at Interface Quebec and I couldn’t be more excited for my Canadian trip (Montreal, Ottawa, Quebec City, nature, Twin Peaks, more) ❤️‍🔥

_{a lot in here, I know. Take it easy.}

_{If you don’t want to take it easy and in fact YOU WANT MORE}

_{you can explore the future of AI x Design take my course on Interaction Design Foundation} 💋

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AI & Design Love, Ioana ♥️