Emma Sarappo has a fascinating review of a new book about the Challenger space disaster (gift article). It is the first global disaster I was old enough to witness and experience in real time, and I’ve never been able to get those images out of my head. This review (and book) shines a horrifying light on the many human failures (mainly due to hubris) that resulted in this tragedy:

These issues—faulty O-rings, foam strikes—were understandable. Theoretically, with study and ingenuity, they were fixable. The problem was not really a lack of technical knowledge. Instead, human fallibility from top to bottom was at issue: a toxic combination of financial stress, managerial pressure, a growing tolerance for risk, and an unwillingness to cause disruption and slow down scheduled launches.

(Side note, tell me that last sentence doesn’t remind you of software development sometimes…)

Also, the astronauts knew what was happening:

The astronaut assisting them into place and finishing final preflight checks “looked down into her face and saw that her Girl Scout pluck had deserted her,” he writes. “In her eyes he saw neither excitement nor anticipation, but recognized only one emotion: terror.” She would fly for 73 seconds before the shuttle broke apart in a fireball and a cloud of smoke. After that gut-wrenching instant, and more seconds of stunned silence, a NASA public-affairs officer would speak the understatement that would become famous: “Obviously a major malfunction.”

Yes, I’m a relentless fanboy of whatever Paul Ford writes, but this is a truly wonderful post about what makes AI so addictive and impossible to look away from. He frames AI as a technology that truly has no shame because “it possesses an absolute willingness to spout foolishness, balanced only by its carefree attitude toward plagiarism.” And so:

By aggregating the world’s knowledge, chomping it into bits with GPUs, and emitting it as multi-gigabyte software that somehow knows what to say next, we’ve made the funniest parody of humanity ever. These models have all of our qualities, bad and good. Helpful, smart, know-it-alls with tendencies to prejudice, spewing statistics and bragging like salesmen at the bar. They mirror the arrogant, repetitive ramblings of our betters, the horrific confidence that keeps driving us over the same cliffs. That arrogance will be sculpted down and smoothed over, but it will have been the most accurate representation of who we truly are to exist so far, a real mirror of our folly, and I will miss it when it goes.

I finally read this very long essay about Rewilding the Internet that’s been making the rounds. It’s about 30 mins of your time and in my opinion it’s time well spent.

It’s about what internet-builders can learn from the field of ecology, where the word “rewilding” has a very specific meaning. It’s essentially about systems thinking, which I know a lot of us care about deeply.

Rewilding the internet is not a nostalgia project for middle-aged nerds who miss IRC and Usenet. For many people across the generations today, platforms like Facebook or TikTok are the internet. They’ve long dwelled in walled gardens they think are the world. Concentrated digital power produces the same symptoms that command and control produces in biological ecosystems; acute distress punctuated by sudden collapses once tipping points are reached. Rewilding is a way to collectively see the counterintuitive truth; today’s internet isn’t too wild, even if it feels like that. It’s simply not wild enough.

In the end, I can’t help but think that though I love these ideas, it’s just… too late. I hope I’m wrong though.

I recently mentioned how I like to draw it until it works when I’m ramping up on a new system. Clint Byrum says it so much better in his post System Diagrams are Performance Caches for Cognitive Load. First, this bit resonated with me because it’s exactly the situation I currently find myself in:

Having joined just a few months ago, I was overwhelmed about 5 minutes into the meeting. The individual words and concepts all made sense. JSON parsing slow. Network transit treacherous. Changing things at the source hard. I got all of those components of the discussion, but through the whole thing I was just barely able to follow the overall system conversation and ask very basic questions to understand what was going on. I came away with a bunch of exploratory personal action items, and a very clear hole in my mental model of the system that needs to be filled.

Clint goes on to use a systems analogy for the individual people that make up a team—people and knowledge as components of caching, computation, and storage. This leads to a perfect explanation for why system diagrams are so important:

A single system diagram is where those primed nodes can push the most relevant bits of their information out of their local brain-caches, and into a high-performance distributed cache from which everyone can read. This will preserve precious cognitive load for those critical low-latency tasks. Of course, all of these caches may be stale. The local in-memory ones are particularly hard to test, but at least the system diagram is observable. Everyone can look at it, and if there are nodes with updates, they can update the cache.

So, prime those caches. Draw it until it works!

This isn’t entirely surprising but it’s a sad state of affairs, and it’s worth highlighting not just how, but also where LLMs are being trained:

Hundreds of thousands of hours of work goes into providing enough feedback to turn an LLM into a useful chatbot, and that means the large AI companies outsource the work to parts of the global south, where anglophonic knowledge workers are cheap to hire.

I know it’s too dismissive to call chatbots “fancy autocomplete” like many do, but we have to remember that this isn’t magic. The words the bots use come from somewhere. And in the case of “delve”…

I said “delve” was overused by ChatGPT compared to the internet at large. But there’s one part of the internet where “delve” is a much more common word: the African web. In Nigeria, “delve” is much more frequently used in business English than it is in England or the US. So the workers training their systems provided examples of input and output that used the same language, eventually ending up with an AI system that writes slightly like an African.