Re 7 (Test your code. Future you will thank you): Also leave a file of notes. Future you will thank you.
I mean, as you work on a system, write down how it works in a text file. Something surprises you, doesn't work like you expect? Write it down. You finally understand why it does things a certain way? Write it down. Things that you see that might become problems that you don't have time to fix now? Make a note.
Keep it loosely organized, at more or less up to date. Check it in at the relevant level of the code repository. Future you will thank you.
Re 10: The perfect job doesn't exist. If your job is pretty good, sane management, interesting work, decent pay, good coworkers, do not lightly walk away. Those are not easy to find. Ride it as long as you can unless you have a really good reason to do something else.
But jobs are only good until they're not. The magic can go out, slowly or quickly. When it does, don't hold on to "it was really good once". It was, and that was wonderful, but it's not anymore, and that means it's likely time to at least start looking around.
I've seen Douglas Adams use almost exactly this in Dirk Gently's Holistic Detective Agency. A professor (IIRC) is talking to a former student. Quoting from memory
"Did you ever actually finish any papers?"
"No. But the reasons why not were always absolutely fascinating."
So, yeah, for me personally it's not an AI tell. I've seen it in the wild before AI.
LLMs write like some people cook, adding bespoke artisanal Belgian sea salt, truffle oil and weirdly specific cheese without any thought given to how the result will taste.
And just to spell it out, since it looks like HackerNews is flooded by people who are new to science these days: even if a result doesn't come with a price, scholarly peer review is the norm across all of science: https://en.wikipedia.org/wiki/Scholarly_peer_review
Being normal doesn't necessarily mean it isn't gatekeeping- gatekeeping is also quite "normal" in many cases.
That being said, I think there needs to be some standard, and peer review seems like the best we have come up with. But is the current status quo for scientific publication the best we can do? I think that is an open question and we should be able to openly discuss alternatives.
"Is it the best we can do?" is a completely different question from "given that it's the current standard, should it be applied to this new claim that is happening now?"
Depends. What replaces it? Does that replacement do better at keeping false claims out, or worse? Does it do better at letting true claims through, or worse?
Real true or false - which I define as correspondence (or lack thereof) with reality - does not depend on convincing people. Reality does not change when people become convinced; ideas correspond to reality whether people are convinced or not.
But perceived truth and falsehood depend on convincing people - either convincing them one by one, or else convincing some gatekeeper, whose word will convince those who accept the gatekeeper.
Instead of a committee of subject matter experts they should use a more rigorous and trustworthy standard, like passing the solution to ChatGPT with the prompt "did this win?"
Which is completely insane. The Venezuelan ship attacks have zero to do with what force was authorized for. The AUMF was for the use of force against those responsible for the 9/11 attacks. Were people on the boats in Venezuela? No. Then it's not force authorized by that AUMF.
But Trump found a smokescreen to hide behind. And Congress is too spineless to impeach him for unauthorized use of military force. And here we are.
Involution in the context of China laying flat means you work harder and harder only to stay in place with no chance of getting ahead. In China they work 12h day 6 day a week and still have 0 change to buy a home, start a family, etc. The lying flat movement is about giving up on the rat race and working just enough to get by because going above and beyond only get you working yourself to death for no tangible benefit.
> I’m more and more convinced that all of AI engineering is Neijuan (内卷, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The English term for Neijuan is “Involution” from the book Agricultural Involution. Agricultural involution describes the intensification of farming that raises productivity per square meter while leaving productivity per head unchanged.
Let's not be so naive about this, it happens all the time and humanity is not permanently lost. Example USA vs Indians: At first you come up with some justifications for the bad stuff you are doing, then you kind of forget about it over the centuries.
I still think that killing anything that lives out there "just in case" is a terrible idea, mind you.
I mean, as you work on a system, write down how it works in a text file. Something surprises you, doesn't work like you expect? Write it down. You finally understand why it does things a certain way? Write it down. Things that you see that might become problems that you don't have time to fix now? Make a note.
Keep it loosely organized, at more or less up to date. Check it in at the relevant level of the code repository. Future you will thank you.
Re 10: The perfect job doesn't exist. If your job is pretty good, sane management, interesting work, decent pay, good coworkers, do not lightly walk away. Those are not easy to find. Ride it as long as you can unless you have a really good reason to do something else.
But jobs are only good until they're not. The magic can go out, slowly or quickly. When it does, don't hold on to "it was really good once". It was, and that was wonderful, but it's not anymore, and that means it's likely time to at least start looking around.
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