When evaluating whether public money is well spent on education it must be more important how valuable it is to the public, not what the price for the work is to the individuals.
I like the "what if these workers stopped today" test:
Pick a profession. For example pick from 'trader', 'dentist', 'cleaner', 'sales person' or 'nurse'. Then imagine that all people in that profession stop working today.
How bad would it be for society? Is it better or worse than some other profession? Compare this to how well-payed the profession is.
I think this is a much better test for value to society than looking at what people get payed.
For example, I think it would be much worse if all nurses stop working than if all bankers stop working. Yet bankers tend to get paid more.
A letter from me addressed to the agent, describing me and the goals of the project. Not AI-generated, no code or commands.
Nothing specific to the technology, because that gets out of sync fast, or might steer the agent in the wrong direction.
Smart models can figure these things out themselves.
If for example the agent repeatedly wrongly guesses the command to run tests, I prefer to change the code so the wrong guess will actually work the next time. Or I will find what in the project lead to the wrong assumption and fix that.
This is like saying "A slow leak is cheaper than a burst pipe"
Yes, okay. But with both you will have a bad time cleaning up.
There is a third option: good abstractions.
I did see this pattern described in the blog in practice a lot (and fell victim to it myself) and I think that in general this comes down to inexperienced programmers. Object oriented programming makes it worse.
Teaching these programmers that they should not abstract is not the solution. It is blocking their growth.
> But, at the same time, the user experiences it as a graphic. An animated GIF, for example, contains a small amount of code-like data to say how long each frame should last for and when to stop running. Is a GIF software? Is the basic circle above software? How much code do you need before something becomes software?
To me, from an ontological perspective, it's software if you wrote it like software: starting from the text form in a text editor. But it's an image if you made it in a graphical editor like Inkscape, just like how you could make an image in GIMP. The format just happens to be SVG.
I think this might also make sense from a legal perspective, as copyright law seems more focussed on the creation (how it is made / who made it / what was the purpose), and less on how it is used.
I wonder what is the difficulty in rewriting strncpy uses that makes it take six years? Was it widespread? Or was it more of a long going effort, where it was only changed if there were some changes in the same file? Or is there some other thing that makes it difficult?
Other advantages to generating content: (1) fewer copyright issues. (2) No creators to pay, just GPU bills scaling with the use of the platform. (3) a much smaller critical mass.
Given these advantages I expect the current "social media" to be replaced with a new one, rather than them pivoting. The next big thing after tiktok might be something that only has generated content, where a last final bit of "social" is taken out of "social media".
"Let it crash" is a sentence that gets attention. It makes a person want to know more about it, as it sounds controversial and different. "Let it heal" doesn't have that.
One of the biggest usability problems with Python dependencies is that the name you import might be different from the name that you use to install the package.
So if you find some script on the web that has an `import foo` at the top, you cannot just `pip install foo`. Instead, you'll have to do some research into which package was originally used. Maybe it's named `pyfoo` or `foolib`.
Compare that to for example Java, which does not have that problem, thanks to Reverse Domain Name Notation. That is a much better system.
I don't think becoming vegetarian helps. From the article:
> The study found evidence that food processing is a likely source of microplastic contamination, as highly processed protein products (like fish sticks, chicken nuggets, tofu, and plant-based burgers, among others) contained significantly more microplastics per gram than minimally processed products (items like packaged wild Alaska pollock, raw chicken breast, and others).