A lot of people were sick of the status quo. For better or worse, Trump represented change. Obviously, there are many more factors that contributed, but in my opinion, this is where the momentum was.
The idea is that performance isn’t a reason not to do it. Other considerations may cause you to choose inline, but performance shouldn’t be one of them.
This was a well-made video. I haven’t validated any of their claims, but as someone not particularly familiar with nuclear technology, this was pretty helpful.
It’s worth considering if these package managers would have taken off if they didn’t use git. You get a bunch for free, why not use it while you’re small?
I would think Venus, since it has the second smallest orbit. If that’s the case, I’m wondering if there’s some mathematical theorem that proves the “closest” planet is always the one with the next smallest orbit, regardless of speed or how closely the two objects’ orbits align in size.
I’m not sure if it’s going to be a better world for humans, but roads so crowded with wheels that I don’t want to leave my house sounds like an economy where a lot of wheels are being sold.
Best I can tell, it’s resulting in less churn, which isn’t the same as work getting done faster. Maybe it’s a phenomenon unique to engineering, but what I’m observing isn’t necessarily work getting done faster — it’s that a smaller number of people are able to manage a much larger footprint because AI tools have gotten really good at relaying existing knowledge.
Little things that historically would get me stuck as I switch between database work, front-end, and infrastructure are no longer impeding me, because the AI tools are so good at conveying the existing knowledge of each discipline. So now, with a flat org, things just get done — there’s no need for sprint masters, knowledge-sharing sessions, or waiting on PR reviews. More people means more coordination, which ultimately takes time. In some situations that’s unavoidable, but in software engineering, most of the patterns, tools, and practices are well established; it’s just a matter of using them effectively without making your head explode.
I think this relay of knowledge is especially evident when I can’t tell an AI comment from a human one in a technical discussion — a kind of modern Turing Test, or Imitation Game.
What is the economic value of a wheel? If we flood the market with wheels, we’re going to need far fewer sleds and horses. Pretty soon, no one might need horses at all — can you imagine that?
I can’t help but think a lot of these comments are actually written by AI — and that, in itself, showcases the value of AI. The fact that all of these comments could realistically have been written by AI with what’s available today is mind-blowing.
I use AI on a day-to-day basis, and by my best estimates, I’m doing the work of three to four people as a result of AI — not because I necessarily write code faster, but because I cover more breadth (front end, back end, DevOps, security) and make better engineering decisions with a smaller team. I think the true value of AI, at least in the immediate future, lies in helping us solve common problems faster. Though it’s not yet independently doing much, the most relevant expression I can think of is: “Those who cannot do, teach.” And AI is definitely good at relaying existing knowledge.
The weird thing to me is how little concern there is for sale of data by any social media company. Very intimate details about many Americans lives can easily be purchased by US adversaries. That’s reportedly the motivation for the bill but effectively it really only change who is profiting from the data by forcing a bargain price for a foreign company and directing the ongoing revenue to us company vs a Chinese company.
I suppose it would be easier to regulate a us based company should the rules around user data sale change but this bill alone effectively does nothing to advance the security principles the bill is sold on beside make it slightly more expensive for the Chinese government to use the data.
I had a professor freshman year of college who offered the question “How many times can you split the distance between me and you in half?”. Ultimately I arrived at the conclusion infinity which forever more made the general idea of calculus stick.
> We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%
The author of the post fails to articulate how we strike a healthy balance and instead comes up with contrived examples to prove points that only really apply to contrived examples.
Idk, this doesn’t strike me as news. Google just missed a vulnerability.