I enjoyed, and found a kind of solace, in All Human Systems are Enormous Trash Fires.

Realizing this can be revelatory. Once you recognize that all human systems are enormous trash fires, you stop trying to figure out how to switch to a system that isn’t an enormous trash fire, since they don’t exist. Instead, you ask better questions about your current trash fire. Like, “Am I doing everything I can to contain this enormous trash fire, even though I know it will never go out?”; “Do the people in charge recognize that this whole place is an enormous trash fire?”; and, most importantly, “Am I surrounded by a team of firefighters or a team of arsonists?”

We’re imperfect beings in the extreme, and the organisations we create are as often the sum of the imperfections as they are of our better attributes. But that doesn’t mean we can’t look to have left things a little better each time we step away.

Leaving the enormous trash fire functioning better, just a bit.

When using ChatGPT, I had an idea to ask it to summarise an article. Seeing it do well, I wondered about other uses of summarisation. One item that struck me is using generative AI to improve how we interact with search, for example in apps like Obsidian or Evernote.

It went like this. Search hasn’t changed much in a long time. We’ve got a bit better at ranking results, but the experience of search is a list of results. Each must be examined to see if it answers the question. What if instead search results could be presented in a summarised way? This would be particularly useful for queries whose underlying goal was “tell me what I know about X?”.

For the longest time – well, since before 2010, so over thirteen years, which is pretty much forever in internet time – this site has used extremely old-school CSS. Almost everything in the stylesheet would’ve likely been recognisable to anyone visiting the CSS Zen Garden back in in 2003. I think float was about the most modern directive. The primary CSS file, centred.css, has been adapted a couple of times to tweak the design, notably to create a mobile version of the site in 2011, but the core has remained static for a long time.

I found The micromanager’s dilemma a fascinating and valuable read. Matthew Ström applies game theory to explain micromanagement as, potentially, a vital strategy for one’s leadership toolbox.

In this essay, I’ll show you that micromanagement isn’t just a nagging habit; it’s an inevitability. That’s the paradox: micromanagement is both bad management practice and a key component of the best management strategies.

The analysis is by no means exhaustive, and I found some of the inferences less than watertight. It’s certainly not a mathematical proof of an optimal management style! But the article is a great example of applying a mental model from one’s library to a problem and using it to great effect. While talking more specifically about management, I feel there are still lessons that I can draw for my own, more technical, leadership.

In my last post, I wondered about when we would be able to have a conversation with AI that we could trust. While that’s an interesting question, I skipped over the more immediately arresting questions of what we can do today and in the near future with generative AI (which is the genre of AI of which ChatGPT is a part).

In this vein, I found How Might Generative AI Change Programming? a fascinating read. It discusses what aspects of programming feel amenable to this type of AI and how we might come to view it as a just another tool over the next decade or so. I found many insights.

I noted in my post that generative AIs appear not to be trustworthy. In the programing context, this means that if you ask them to write code, you have the problem of figuring out whether the code is correct. Worse, it’ll often look plausible but still contain major bugs. The article goes beyond this to present a convincing case for why generating more than trivial production code is currently fanciful. It then looks further into where we might find real utility. The thought that if we look beyond the obvious idea of the AI writing production code we can find deep utility engaged me, and I agree that generative AI feels well suited to fuzz and property testing.

Navel gazing a moment here, it is the piece I wish I’d written: taking a subject I know well and applying a novel, interesting and timely angle to it. But then I realised that the author has been involved deeply in programming languages for many years, and perhaps it’s not so awful that I wasn’t able to write anything in the same ballpark. I just have to keep writing, and thinking, and this practice may enable more original thoughts in the long term.