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Joined 2 years ago
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Cake day: June 10th, 2023

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  • You do need great hardware, but it depends on your use case. If you want the full 671 billion parameter R1, you need to run it on specialized hardware that has enough RAM.

    If you want to run R1 on a phone, you could get the 1.5B parameter R1 running as well. But the quality of results and the speed of response diminish significantly depending on the model and the hardware you use.

    In Iceland they run their Bitcoin Mining facilities fully on geothermal energy. I wouldn’t be surprised to find the EU exploring there options regarding new data centers built on renewable energy for quite some time. For now it is a lot faster to train the models within existing data centers that already have the hardware while everyone is actively competing.

    Meanwhile governments and corporations are trying to pull money out their ass (cutting important programs) to move mountains and create AGI, of which we have no evidence this is the way to accomplish that.



  • As someone who has a formal education in Computer Engineering, I can attest that the degree is essentially a combination of modern Electrical Engineering and Computer Science degrees. In other words it is a dual major without any of the benefits.

    Not all Software Engineers do actual engineering and that’s okay. The only problems I’ve seen with this in my time in the tech industry is when you have someone who can talk the talk, but when it comes time to do the difficult mental work, they fold like a deck of cards, or worse release a product that’s half-baked. You will see this a lot when a boot camp churns out talent hoping to make a quick buck and then they are given a truly important and hard problem to solve, such as healthcare or military applications.

    For that reason, many SWE roles require education to be specified on resumes, rather than certifications as a hoop you have to jump through. If your job did not question your education when you were interviewing then that is usually a good indicator of the kinds of people you will be working with. With all of that said I’ve worked with many engineers that did not have a formal education and were very talented, some of which lied about their education to get where they are today. This happens frequently across all industries however, and isn’t unique to software.





  • Why does the article make it sound like cooling a data center results in constant water loss? Is this not a closed loop system?

    I’m imagining a giant reservoir heat sink that runs throughout a complex to pull heat out of the surrounding environment where some liquid evaporates and needs to be replenished. But first of all we have more efficient liquid coolants, and second that would be a very lazy solution.

    I wonder if they’ve considered geothermal for new data centers. You can run a geothermal loop in reverse and use the earth as a giant heat sink. It’s not water in the loop, it’s refrigerant, and it only needs to be replaced when you find the efficiency dropping, which can take decades.






  • To be fair to all those people that misunderstand it, they are marketing it as Artificial Intelligence, which it isn’t. So one could argue it is in fact a lie, as most marketing seems to be these days. It’s difficult for us humans to see the difference between intelligence and an “alright prediction of what might come next”. Such as when we struggle to tell the difference between the truth and a lie someone told us. It can be deceiving.

    Since marketers have bastardized the term, and we’ve begun using AGI in place of the old meaning, confusion is only going to get worse until existing LLMs become somewhat boring, and marketing latches onto some other trend.

    With that said, I find the utility of this thing we now call AI to be pretty useful for my own needs, but that’s not stopping people from trying to fit this square shaped solution into circle shaped holes.