guitars are real

Do you also hand out copies of your car and house keys to strangers you meet in parking lots?

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Fuck it, let’s all just start winding our own magnetic core memory arrays.

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Open source is just another commons, and companies have a way of uncontrollably exploiting common resources until they collapse.

In the case of open source, it’s healthy in the sense that money is flowing, we have companies sponsoring projects, tons of code is available for inspection and reuse, etc. Very nice. But if you go back to the original concepts of free software, in many cases we struggle with actually exercising the four freedoms. Red Hat has engineered an EULA that basically lets them ban practices that had been thought protected by the GPL for at least a generation, and so on and so forth. So is the open source community healthy or dying? Doesn’t the answer to that depend on your priorities?

I think it would make a lot of sense to try to create an economic model that can fund open source software development without relying on corporate injections of cash. It’s not that they don’t pay for it ever, they just pay for it to the bare minimum extent. IE, the heartbleed fiasco – tons of companies were freeloading off one guy and like half the Internet’s security got fucked for it. Imagine if OpenSSL had had some kind of economic support structure in place to allow for, uh, more than one guy to manage the encryption library for like half the Internet before something insanely stupid and predictable like that happened. Well, we can never have that with corporate-controlled open source.

With the SSD’s I can afford, there are what you might call “net negative savings” when I save maybe a couple dollars in power a month but have to replace them every few months. We can’t all afford EVO’s.

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I mean, with stuff like ZFS, it’s a little hard to justify the outlay for all solid-state disk storage when I can build out a large storage array using HDD’s and use one mid-size SSD for ZIL and then L2ARC to provide read/write speedups. Who actually cares what the underlying storage mechanism is as long as the dataset is backed up and the performance is good?

Hi, sorry I just saw this. “SFF” is short for “small form factor.” It’s just industry jargon for “a small PC.” They tend to be designed to use less power which makes them a good fit for home servers. Pretty much any line of PC sold to businesses, like Dell Optiplex or HP EliteDesk, will have small form factor variants.

It’s more about the imbalance caused by algae blooms. They breed prolifically, and die off en masse more or less constantly as they bloom. When they die, they decompose and release carbon dioxide back into the water. So algae blooms hoover up carbon dioxide and concentrate it in a specific spot of ocean water, which can cause problems regarding anoxia and also ocean acidication.

The issue is that after a couple hundred years of intentionally eating literally everything in the ocean and dumping tons of our garbage and industrial waste there, oceanic ecosystems are even more fragile than usual and we don’t exactly have the ecological spare room to tinker with wild algae blooms on a scale large enough to make an impact on climate change. It would be trivial to ruin oceanic ecosystems, and by extension, many land-based ecosystems, with a megascale algae bloom.

Vats of algae in controlled environments might be a way to go, though?

Everyone likes to trash machine learning because the power requirements are high, but what they don’t realize is that we’re in the very first days of this technology (well, first couple decades of the technology being around, first few years of it being advanced enough to have anything to show off). Every technology that got bundled together into your phone was equally as useless when it was first invented. Honestly, compared to the development of most other technologies I’ve looked at, the pace of development in AI has been shocking.

Literally once a week, I see some news story about AI researchers delivering an order of magnitude speedup in some aspect of AI inference. The technique described here apparently allows for a 20x speedup on GPU’s.

Yeah, but I don’t know any other language where the fact a program is written in that language is used as a selling point. I never cared that Linux was written in C, I cared that it does its job. I’ve heard about Redox many times, yet never once has there ever been anything said about it other than “it’s written in Rust! :D” Literally, the fact that it’s a UNIXY operating system written in Rust is the first thing about the OS on their home page.

Hey, Linux started as a learning project, you learn more about programming by writing code, so I’m not saying it’s bad, I just can’t understand why I’d care about something that at this stage seemingly is just a learning project.

Shout out for ODROID, their product revision cycles take too long (lmao why are they still selling a 32-bit chip that was an iffy investment back in 2013), but when they drop new stuff, it tends to be great.

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Bonus: there is a literally endless supply of used x86 SFF hardware from large institutions, so unlike SBC’s, there’s no special, weird supply chain managed by an English educational nonprofit that could just suddenly decide to not sell to the public for years at a time.

Would you also agree it’s rude to imply that this group of researchers, who actually advanced the state of the art in machine learning, are just a bunch of ChatGPT jockeys who don’t deserve credit for their work?

I’m not against Rust, per se, the idea of a systems programming language with some more advanced memory safety features sounds nice, but what is with the emphasis on creating a Rust version of everything? Like why should we want an OS written only in Rust?

You don’t get to the point of solving scaling problems without having something to scale first.

Yeah, so the actual law is that if you didn’t do any work and just gave ChatGPT or Midjourney a prompt and it shat out a picture and then brag to the copyright office in your application that you didn’t do diddly squat, the work effectively had no human authors. If, instead, you build a new machine learning model, tune it for your specific problem, analyze the results, and furthermore, break new ground understanding how it solved your problem, and then you write the paper, in fact, you have tons of ownership over the work.

The fact people can’t tell the difference between the two and are actually upvoting you kind of says a lot about how little most people understand this stuff.

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