My thought as well, but the question is: does it matter for what the survey is trying to achieve?
Some people will interpret it one way, some a subtly different way, but is there a reason that people's interpretation changes over time in a way that is more rapid and more significant than the underlying question of how good their life is broadly? Probably not.
There may be cultural differences that make it tricky to do comparisons between cultures / countries, but it should give something useful when looking at the same culture / country over time.
In particular, the bulk of the substantial text of the order has a pretty clear culture war bend with all the talk about how truthful AI is. This is in large part a fight over the political leaning of AI models.
That's the whole point. They aren't law, and they were (probably) never meant to be so far-reaching, and yet the clear purpose of this Executive Order is to tell the states what laws they can enact. The EO doesn't have the legal power to do that directly, but it clearly outlines the intention to withdraw federal funding from states that refuse to toe the line.
That's a fair and good interjection. The truth is probably that at society scale, both approaches are traditional.
The open sharing approach is traditional for research and academia, while the information restricting approach is traditional for business-oriented thinking.
So, a young field will typically start out fairly open and then get increasingly closed down. The long-term trajectory differs by field, and the modern open-source landscape shows that there can be a fair bit of oscillation.
We're seeing the same basic shape of story play out in generative AI.
It is quite interesting to ponder these usage statistics, isn't it?
According to their charts they're at a throughput of something like 7T tok/week total now. At 1$/Mtok, that's 7M$ per week. Less than half a billion per year. How much is that compared to the total inference market? And yet again, their throughput went like 20x in one year, who knows what's to come...
If you'll look at the Guidelines for HN linked at the bottom of the page, you'll note that whether a submission is productive is not a criterion.
You could perhaps make an argument that among the flood of AI-related submissions, this one doesn't particularly move the needle on intellectual curiosity. Although satire is generally a good way to allow for some reflection on a serious topic, and I don't recall seeing AI-related satire here in a while.
Others have given some answer to who was made poorer by Ballmer holding Microsoft shares, but I'd argue that this is the wrong question. Instead of looking at a specific individual, we should look at systems.
A system that allows this kind of extreme wealth accumulation is quite fundamentally at odds with democracy because extreme wealth can be and is in practice used to influence politics in a way that undermines democracy.
Some people might not care about that, but if your goal is improving the outcomes of the largest number of people, then pretty much everything else is secondary to having a functioning democracy.
Given two version (old and new) of a Git change (i.e., individual commit or patch series from a pull request) it produces a diff that is actually useful for reviewing purposes, assuming you've already reviewed the old version of the change.
It's sort of like `git range-diff`, but where `git range-diff` produces a "diff of diffs" that is very hard to impossible to read, this tool gives you a direct diff between old and new versions, but filters out any irrelevant changes that were introduced because the author rebased on a more recent version of the target branch.
I hope that makes sense - I never know quite how to put it into words for somebody to understand who isn't intimately familiar with Git. It is very powerful though if you combine it with a minimal amount of setup e.g. for fetching all PR branches from a GitHub repository. I use it almost daily as part of my code review workflow.
The first paragraph is probably intended to establish the author as somebody who isn't crazy.
The disconnect between the majority beliefs about Marxism and its actual contents (as well as the beliefs of those who actually follow in Marx's footsteps today) is gigantic. I perfectly understand that the author felt they needed some way to bridge that gap without turning the vast majority of readers away at first sight.
Good engineering requires that you still pay attention to the result produced by the agent(s).
Bad engineering might skip over that part.
Therefore, via Amdahl's law, LLM-based agents overall provide more acceleration to bad engineering than they do to good engineering.