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Joined 11 months ago
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Cake day: August 2nd, 2023

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  • Some people play games to turn their brains off. Other people play them to solve a different type of problem than they do at work. I personally love optimizing, automating, and min-maxing numbers while doing the least amount of work possible. It’s relatively low-complexity (compared to the bs I put up with daily), low-stakes, and much easier to show someone else.

    Also shout-out to CDDA and FFT for having some of the worst learning curves out there along with DF. Paradox games get an honorable mention for their wiki.


  • The argument is that processing data physically “near” where the data is stored (also known as NDP, near data processing, unlike traditional architecture designs, where data is stored off-chip) is more power efficient and lower latency for a variety of reasons (interconnect complexity, pin density, lane charge rate, etc). Someone came up with a design that can do complex computations much faster than before using NDP.

    Personally, I’d say traditional Computer Architecture is not going anywhere for two reasons: first, these esoteric new architecture ideas such as NDP, SIMD (probably not esoteric anymore. GPUs and vector instructions both do this), In-network processing (where your network interface does compute) are notoriously hard to work with. It takes CS MS levels of understanding of the architecture to write a program in the P4 language (which doesn’t allow loops, recursion, etc). No matter how fast your fancy new architecture is, it’s worthless if most programmers on the job market won’t be able to work with it. Second, there’re too many foundational tools and applications that rely on traditional computer architecture. Nobody is going to port their 30-year-old stable MPI program to a new architecture every 3 years. It’s just way too costly. People want to buy new hardware, install it, compile existing code, and see big numbers go up (or down, depending on which numbers)

    I would say the future is where you have a mostly Von Newman machine with some of these fancy new toys (GPUs, Memory DIMMs with integrated co-processors, SmartNICs) as dedicated accelerators. Existing application code probably will not be modified. However, the underlying libraries will be able to detect these accelerators (e.g. GPUs, DMA engines, etc) and offload supported computations to them automatically to save CPU cycles and power. Think your standard memcpy() running on a dedicated data mover on the memory DIMM if your computer supports it. This way, your standard 9to5 programmer can still work like they used to and leave the fancy performance optimization stuff to a few experts.



  • Is there a specific reason you’re looking at shadowsocks? The original developer has been MIA for years. People who used it in the past largely consider it insecure for its original stated purpose

    trojan-gfw is a better modern replacement. However that requires a certificate in order to work. You can easily get one via lets encrypt.

    At this point, let Shadowsocks, obfs, and kcp die a graceful death like GoAgent before it did.


  • I don’t think either of us is the target audience here. I can see a “cheaper” (questionable) Pro laptop being useful for students going into college with a limited budget. An undergrad CS/graphic design degree shouldn’t tax an 8gb machine too much, assuming students shut down everything else when doing their once-a-semester major rendering/compiling/model training. If people just want Macbook pro software with more ports, a “cheaper” machine is better than none. Personally, I would still get a used/refurbished machine though.

    That being said, my current laptop workload tends to be emacs, qpdfview, Firefox, and tmux on EL9. For the remaining stuff, I usually just spin up a VM then ssh/xrdp into it. As for slack, teams, jabber, etc, I’m happy to report I’ve been out of industry/IT for 1+ years and don’t plan on going back anytime soon. For all I care, Apple can call their models unicorn edition. As long as it sells it’s not stupid.




  • 8gb RAM and 256 gb storage is perfectly fine for a pro-ish machine in 2023. What’s not fine is the price point they are offering it (but if idiots still buy that, that’s on them and not apple). I’ve been using a 8gb ram 256 gb storage Thinkpad for lecturing, small code demos, and light video editing (e.g. zoom recordings) this past year, it works perfectly fine. But as soon as I have to run my own research code, back to the 2022 Xeon I go.

    Is it Apple’s fault people treat browser tabs as a bookmarking mechanism? No. Is it unethical for Apple to say that their 8GB model fits this weirdly common use case? Definitely.