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Standard.Site aims to do something similar for any platform powered by the AT Protocol. The Standard.Site schema provides you with a set of lexicons you use to describe your website and the individual pages and posts on it in a way where they can be parsed by a service that runs on the AT Procolo — an “App View,” in the language of their standard — which would allow users to subscribe, via that service, to your domain. The most obvious use case is allowing users on a platform like Bluesky to subscribe to an RSS-like feed of, say, blog posts, the way they would follow any other account on the service.

A compiler does not feel humiliated when I swear at it, a car does not suffer when I call it a shitbox and a power drill is not oppressed by being handled roughly. An LLM is more complicated than those things, and the interactions you can have with them can be truly uncanny, but a moral status does not appear just because the machine can emit text in the first person.

This change maps to a brewing theory among economists: that one consequence of automation via AI is that humans move to figuring out how to validate the outputs and price the operational risks of AI systems. That increasingly seems to me to be what we’re doing inside the company. The more we add AI automation, the more humans move to some “verification layer” that sits atop it. The verification layer sits atop of a much larger “virtual organization” which consists of increasingly large quantities of AI systems working on behalf of humans.

The most frustrating failure mode right now is that people submit issues that are not in their own voice. They contain an observed problem somewhere, but it has been thrown into a clanker and the clanker reworded it and made a huge mess of it. Typically, it was prompted so badly that the conclusions produced are more often than not inaccurate but always full of confidence. The result is complete guesswork on root causes, fake-minimal repros, suggested implementation strategies, analogies to adjacent but often the wrong code, and long lists of error classes that might or might not matter.

That is worse than no diagnosis.

In this sense, prompts are a worse form of technical debt than code. When technical debt blows up, it usually causes errors or a tangible slowdown as you try to understand the code. Prompts will decay silently. Also, even janky code tends to be relatively stable when untouched, but every single model upgrade could turn a functional prompt into a non-functional one.

Earth is not metaphysically different from the heavens, living beings are not metaphysically different from inanimate matter, humans are not metaphysically different from other animals. The soul is not metaphysically different from the body. We are all parts of nature, like anything else in this sweet world.

Anthropic likely banned xAI to stop Claude from being potentially distilled while it tried to improve Grok’s coding capability. Meanwhile, Musk called Anthropic “misanthropic and evil” earlier this year, and said the new tenant “hates Western civilization”. But both parties seem happy to put that behind them and strike a deal, so perhaps there’s something else at play.

I have written this essay in an attempt to coldly and analytically wrestle with something that for decades has seemed like a science fiction ghost story. Upon looking at the publicly available data, I’ve found myself persuaded that what can seem to many like a fanciful story may instead be a real trend. If this trend continues, we may be about to witness a profound change in how the world works

Even if agents do more of the work, the human still needs understanding to direct them. You need to know what is worth building, what question matters, what result is suspicious, and what tradeoff is acceptable.

This is probably the most useful explanation of how to create a skill that I've seen. I have definitely fallen into giving step by step instructions trap. I am definitely going to take some of this advice.