I recently put Alpine with i3 on a Raspberry Pi 4 Model B and I'm super impressed with how snappy it is. I find it much better even than Raspberry Pi OS Lite.
Same here, I put it on two very old RPi 1 and was amazed at how low the footprint is. I wish there were images available for other SBCs as well, mostly Allwinner based ones (OrangePi, NanoPi, etc); probably I did something wrong but building them from scratch turned out more complicated than expected.
Yeah, either this guy's totally insane or it could even be somebody who's an AI skeptic who's just flooding projects with really dumb PRs just to show the risks and get people skeptical about the use of AI in open source (Takes on my folie hat)
that's a grifter doing grifting. there was a thread on /g/ about this guy the other day, anons digged out much of its past as a failure / grifter in many areas, running away with the money at the first problem
When looking at this history here on HN, he started out in the poker world. I'm not sure if he played, but he wrote a poker engine or something. In my experience, the venn diagram for professional poker players, crypto enthusiasts and grifters have a lot of overlap.
But for this guy specifically there's practically complete radio silence during the crypto era. It's only recently with all the AI noise that he's become active here on HN again.
I was a bit disappointed to discover that this was essentially an R vs. Python article, which is a data science trope. I've been in the field for 20+ years now and while I used to be firmly on team R, I now think that we don't really have a good language for data science. I had high hopes for Julia and even Clojure's data landscape looks interesting, but given the momentum of Python I don't see how it could be usurped at this point.
It is EVERYWHERE. I recently had to interview a bunch of data scientists, and only one of them knew SQL. Surely, all of then worked with python. I bet none of them even heard of R.
The focus of SAS and R were primarily limited to data science-related fields; however, Python is a far more generic programming language, thus the number of folks exposed to it is wider and thus the hiring pool of those who come in exposed to Python is FAR LARGER than SAS/R ever were, even when SAS was actively taught/utilized in undergraduate/graduate programs.
As a hiring leader in the Data Science and Engineering space, I have extensive experience with all of these + SQL, among others. Hiring has become much easier to go cross-field/post-secondary experience and find capable folks who can hit the ground running.
It was a great language, but it was/is extremely cost-prohibitive plus it simply fell out of favor in academia, for many of the same reasons, and thus was supplanted by free alternatives.
Experienced Data Scientists and/or those straight out of school are EXTREMELY lacking in valuable SQL experience and always have been. Take a DS with 25 years experience in SAS, many of them are great with DATAstep, but have far less experience using PROC SQL for querying the data in the most effective way--even if they were pulling the data down with pass-through via SAS/ACCESS.
Often they'd be doing very simplistic querying and then manipulating via DATAstep prior to running whatever modeling and/or reporting PROCs later, rather than pushing it upstream into a far faster native database SQL pull via pass-through.
Back in 2008/2009, I saved 30h+ runtime on a regular report by refactoring everything in SQL via pass-through as opposed to the data scientists' original code that simply pulled the data down from the external source and manipulated it in DATAstep. Moving from 30h to 3m (Oracle backend) freed up an entire FTE to do more than babysit a long-running job 3x a week to multiple times per day.
But that's what religion is, wisdom and nonsense mixed together by people who didn't yet have the benefit of the great filter to separate the wisdom from the nonsense: science.
The sheer number of annoying twats who tell me that The Book of I Ching holds great secrets has defnitely helped obscure any great secrets it contains.
When I tried using Gleam, I loved that it came with all the basic tooling I needed and that's what I think is so wonderful about Lux. I don't want to spend my time fiddling around with setting up all the individual tools — I just want to write code. For me, Lux makes the broader experience around building Lua projects a lot more enjoyable.
If I can get lux to deal with the package management scenarios around a few turboLua projects, I’m pretty sure I’m going to ship much more Lua code next year.
Out of curiosity, what in a broad sense is configured in those 10 lines? (For context: I'm a Helix user trying out terminal-based vim at the moment and I'd like to know what the most important tweaks are.)
Found it last month, don’t remember how, but I did share it with the rest of my team. Excellent talk, it does have many "eternal" points to rebound on the essay vocabulary.
This was exactly like my experience, although mine was in the UK maybe 15 years earlier. I went to university and studied a particular subject so that I'd be employable and didn't enjoy what I was learning. (It's debatable whether I learnt much at all, though.) Like the author, many years later I also came across computer science and found it exciting and engrossing, but I have a slightly different take on that:
> Absolute joy turned into anger, and anger into resentment, as I wondered how different my life might have been if I’d been taught subjects I actually cared about by professors who cared too.
For me, I'm not sure that hypothetical alternative path was ever available. I really admire university students who are passionate about what they are learning, but I doubt that could've been me regardless of the subject (unless that subject was beer). I simply wasn't in the right headspace for that.
Perhaps I needed to grind out a dull degree as it ultimately set me on a path to a time/place/subject that I really do enjoy. My interests now have been shaped by my journey and if you'd tried to teach me computer science at 18 I'm sure I would've hated that, too.
Same. I didn't know how to express my passions, and I didn't know that I didn't know. I've since been (re?)discovering this. I'm still working on it.
I wish that we didn't talk down to kids to teach them, and instead approached them as equals, so that they wouldn't think that their passions and interests are below that of "grown ups". I recently learned that there's a term for that. Well sort of. It's "andragogy", which directly translates as education for adults, contrasting with "pedagogy" which is education for kids.
Historically universities began as church schools, and (more or less) existed to educate priests in persuasion and nobility in leadership.
You might think that's archaic, but that's still the primary purpose of the Ivy League in the US and the older equivalents around the world. The caste system works slightly differently, with priests (persuaders and marketers) replaced by economists, lawyers, and politicians, and nobility (doers with financial/political agency) by CEOs, financiers, and oligarchs. But it's recognisably the same idea.
There are also supporting castes - the military, which has its own pipeline, and researchers/technicians, which are a weird hybrid caste. Some have limited political agency - which peaked around fifty years ago, and has been declining since - but most are just worker bees.
The idea that universities are there for personal and cultural intellectual development is relatively recent, and much more tentative. There's still a lot of hostility to it because the primary purpose of the system is to maintain power differentials, not to erode them.
The point being that the modern system is the vector sum of at least four different competing trends. There's political hierarchy, there's increasing financialisation of assets and processes (which actually conflicts with research and education), there's a need for workers who are accredited and educated enough, but not too educated and independent-minded, and there are the personal expectations of students, which depend on personality, talent, and acculturation.
There isn't a stable solution for this problem.
A recent trend is the availability of university-level teaching outside of universities. Textbook piracy, YouTube videos, and AI are all making it much easier for motivated people to learn - pretty much anything.
I'm not convinced the formal system is sustainable. But it's clear current ideas about employment aren't sustainable either. So there's going to be a period of complete chaos, and - at best - some new system of semi-formal self-motivated open education is going to replace what we have now, perhaps with some kind of external testing and accreditation for specific skills and abilities.
I agree with all of what you said, and my personal twist is that this weird, archaic, self-preserving hierarchy is a slightly-lagging expression of our collective mindset. We're changing how we think, and that is indirectly eroding away at this status quo. I see this most vividly with how we treat kids and how fast that's changing: every generation has a much better relationship with their kids than the previous generation.
And, more to the point, that they'd hire a _better_ imperfect candidate by taking those four months doing tough interviews with lots of imperfect candidates (rather than hiring one in desperation later).
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