When Andrew Liptak writes about Lord of the Rings, I pay attention. This is one of his best yet, drawing a line from Tolkien to our present-day world in an incredible way:

A critical theme throughout Tolkien’s work is the decline of a once-great people, with weak men failing to live up to the lives and stories of their predecessors.

But we should not despair:

Tolkien isn’t throwing up his hands and pointing out that the world is terrible: he’s explaining that there are ways to avoid falling into despair: sticking to one’s morals, distinguishing the things that are objectively good and bad in the world, and recognizing how to move on those instincts to do good in the world. It’s an inherently optimistic story that serves as an excellent guide for us in the dark times ahead of us.

Also see his piece Corruptibility for more thoughts in the same vein:

The core thing that I take away from Tolkien’s work is that power is dangerous to work with, and that very few who encounter it come away unscathed.

Interesting theory by Casey Newton that good Design helped Deepseek to become popular so quickly:

Both models “thought” for about 13 seconds. ChatGPT showed me a handful of two- or three- word snippets to tell me what it was doing during this time: “comparing protocols,” for example. For the most part, though, I was in the dark about what it was up to.

DeepSeek, on the other hand, shared more than 500 words about its process. I found it disarmingly humble. “Let me start by recalling what I know about these two technologies,” it wrote. “First, ActivityPub. I remember it’s a W3C standard, so it’s widely adopted in the Fediverse. Mastodon uses it, right?” (Right.) As the model continues, it eventually stops to review its work for errors. (“But I should check if I’m mixing things up.”) And 13 seconds after starting—the same time that ChatGPT took—it offered me its full answer.

This is what Jakob Nielsen—back in 1994!—called “Visibility of System Status” as part of his 10 usability heuristics for design:

The design should always keep users informed about what is going on, through appropriate feedback within a reasonable amount of time.

Whether or not Casey’s theory about Deepseek is correct, I find it remarkable that over 30 years after those 10 heuristics were defined we are still seeing examples of their effectiveness on a large scale today.

I like this idea from Lorin Hochstein about focusing more on the almost-incidents in our products:

Because most of our incidents are novel, and because near misses are a source of insight about novel future incidents, if we are serious about wanting to improve reliability, we should be treating our near misses as first-class entities, the way we do with incidents.

I imagine that a culture of “post near-incident reviews” could be really beneficial for the resiliency of our products—and our ability to predict and avoid some of the really catastrophic incidents.

Michael Lopp has a great post about “managing up”, including how problematic that term is:

To me, “Managing Up” has that “your boss’s job is more important than yours” feel, which pisses me off. Your boss’s job isn’t more important than yours; it’s different.

He goes on to share some great advice for what to share with your manager, and when. For instance, when to share something immediately:

Unexpected developments. A situation appears in front of you, a non-threatening one but unexpected. Strange. Something is up, but you can’t discern the backstory story or the intent. It is unfamiliar. Tell your manager. Now. Just a brief note. A heads up. It’s probably nothing—it usually is—but there is a chance your manager’s context plus your suspicions equals additional clarity.

I finally had a chance to make my way through Liz Pelly’s Spotify exposé that’s been making the rounds, and it is so infuriating. Definitely worth reading the whole thing, but the short version is that Spotify is seeding their most popular playlists with generic “background music” that they pay even lower royalties for. A good summary of the issue:

A model in which the imperative is simply to keep listeners around, whether they’re paying attention or not, distorts our very understanding of music’s purpose. This treatment of music as nothing but background sounds—as interchangeable tracks of generic, vibe-tagged playlist fodder—is at the heart of how music has been devalued in the streaming era. It is in the financial interest of streaming services to discourage a critical audio culture among users, to continue eroding connections between artists and listeners, so as to more easily slip discounted stock music through the cracks, improving their profit margins in the process. It’s not hard to imagine a future in which the continued fraying of these connections erodes the role of the artist altogether, laying the groundwork for users to accept music made using generative-AI software.

I’ve been on the fence about streaming services for a while, but I think going forward I want to use both my Kindle and Spotify in the same way. Sample a book/album to see if I like it, and then buy it in physical form (or Bandcamp!) if I do. Like when we used to listen to CDs in the record store to decide if it’s worth spending that precious music budget on.