Cool Links

This great article goes into how Apple went from being kind of an underdog to becoming the biggest company in the world, and how that made its loyal fans from back in the 2000s kinda loathe the company nowadays, with its greedy actions and overall abuse of market power.

I never had access to any Apple products until ~4 years ago, so I never really got caught in the Apple hype train. Still, that’s what I mostly use nowadays and while I like their products, it’s really hard to have any kind of sympathy for them (which is honestly okay; companies should never be your friends). So, it’s nice reading what someone that was in that hype train feels about all the negative Apple news that are all around us all the time.

This fantastic article talks about how we’re often chasing down big goals as a form of reaching happiness, but those big highs often come with big lows right after. What if we aim to make the baseline higher instead of reaching higher highs all the time?

It kinda goes along with what I wrote about longterm goals a few months ago. Not to brag, but I think that article came out really great ;)

I read the Brazilian Portuguese version of this article, and it kind of goes into the same idea as the last one. It talks about how perfectionism can be harmful to one’s self-esteem and how accepting being average allows us to try more things and to live life to the fullest. Perfectionism has nothing to do with achieving success, only with avoiding doing things we’re afraid of not being good at.

The MacStories team was able to put into words what a lot of people (including me) are feeling. AI companies that scrape content on the web (ignoring its licenses) pose a big threat to websites that need the pageviews to keep the lights on. Right now, that is all done under the claim that they’re using the data to train their models and not reproducing the content directly, which would fit into “fair use”. But there are good arguments that it’s not true.

This study shows that, predictably, generative AIs or LLMs tend to decline in quality as they start feeding on content that was generated by other LLMs instead of humans.

Considering much of the web is now getting polluted by this LLM-generated slop and the web is a big source of data for their training, it seems that future models will likely regress in quality. Doesn’t seem like a very sustainable model, does it?

This article talks about how users are growing increasingly indifferent to elements that “pop” on websites and apps. Because “⁠if a pop-up or any element of a user interface for that matter looks too different from the rest of the design, people will often perceive it as something that doesn’t belong (like an ad) and dismiss it.”.

Instead, making the important elements feel like the core flow tends to yield better results.

Another case study; this one shows how apps that are great ideas can become a disappointment when they don’t know how to display those good ideas correctly. And how good (and fun) design can make all the difference on getting users on board.

This case study (in the form of a slide presentation) has amazing insight into how having too many features can backfire. It goes through many design concepts and practical examples onto how you can make users take notice and actually use the features you work so hard to build.