As a software developer, you must never subject your team mates to a PR that you yourself believe to be low quality. The point of code review by others is to catch things that you missed.

There are multiple lines of defense for quality. Yes, automation can and should be one of them, but your own self-review always has to come before review by your team mates.

It is the responsibility of the person running the coding agent to make sure the resulting PRs are high quality. Putting that on your team mates, or worse, random open source project maintainers on the internet, is the definition of an extractive contribution.

In reality, being superwealthy absolutely comes with a tremendous amount of power.

It is also pretty much the opposite of anarchic, given effects like regulatory capture and politicians pandering to the desires of the wealthy.

That said, it is fundamentally important that nobody has too much power, and that power changes hands on a regular basis.

At a global scale, this necessitates taking power away from the capitalist class.

Ideally that power just doesn't go to anybody, but to the extent that it has to go somewhere, it almost doesn't matter where. Or perhaps it's better to say that there are many options that are acceptable and better than allowing power to continue to accumulate unchecked.

That seems like a large number, until you realize that OpenAI claims to have almost a billion weekly users. And OpenRouter shows many models at over a trillion tokens per week.

So in pure token terms, I'd say it is in fact extremely plausible that inference dominates, at least for the popular models.

Normal vs uniform is less clear, but also not as much of a difference. The arguments about signs isn't just about a signs bit, though. The way you negate during accumulation is that you flip all the bits. Only the final float representation is sign+magnitude, the accumulation itself has two's complement steps. I don't actually know the analysis here, just pointing out that it's not that simple.

Dehumanizing is treating other humans as not, or not fully, human. For example, the term "human resources" is dehumanizing because it puts humans on the same level as other resources. If you're treating humans like you'd treat, I don't know, lithium or the ocean, you're dehumanizing them.

The more humans are treated as numbers on spreadsheets and other forms of computation, the more humans are dehumanized.

So both can be true: we're more dehumanized than in 1900, but while that does impact quality of life negatively, the overall quality of life may still be better than back then.

The question should be whether and how we can have both: overall quality of life without being dehumanized.

For example, Android already allows you to give apps restricted access to your media. My understanding of the way it works is that the resulting interface for picking photos etc. is not under the control of the app. The app only receives whatever file you picked.

I rarely see people complain about sandboxing.

What people complain about is when devices are locked down in a way where you are only allowed to install software that is approved by a central gatekeeper, even though sandboxing is in place that should make it far safer to run arbitrary safer than on traditional desktop systems.

Most of us humans inherently value each other. There are exceptions, and small communities can get nasty. But for the most part, small human communities tend to be supportive and valuing each other.

This really only stops being the case when you get large-scale societies that allow humans to view others through an overly abstract lens. Combine that with an unchecked accumulation of power, and you have the potential for those in power to view the rest as without value.

That said, if/when stacked PRs become a first-class citizen in GitHub, more projects will see the benefit of this approach (though they'll probably mostly get there through squash-merges).

People always say you have to know git internals in order to use it, but that's just not true. Git has the right data model and has always been about empowering users to edit their data. Which makes the data model be "in your face" compared to the alternatives (and I think that's what people latch on to when they talk about "internals"), but it ultimately makes for a better tool.

This claim deserves teasing apart.

Clearly, training is a Red Queen's race today. If a model provider were to unilaterally decide to stop training, they would very quickly lose market share to competitors with better models.

On the other hand, what if market and investment conditions change such that everybody has to stop training?

In that case, the models are still there and still as useful as they were the day before. So why wouldn't there still be an inference product?