They/Them, agender-leaning scalie.

ADHD software developer with far too many hobbies/trades: AI, gamedev, webdev, programming language design, audio/video/data compression, software 3D, mass spectrometry, genomics.

Learning German (B2), Chinese (HSK 3-4ish), French (A2).

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Joined 1 年前
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Cake day: 2023年6月18日

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  • Western companies no longer operating in the Russian market, but still producing desirable content. … Western companies have ‘legalized’ piracy in Russia.

    100% this.

    Media is culture, and IMO people have a right to participate in culture. If it’s excessively difficult or impossible to legitimately access culture, one has the moral right to illegitimately access culture, and share it so others also have access.

    It’s inexcusable to refuse to directly sell media. The internet has made it easier than ever to trade access to media for money. Geo-restricted subscription services should be a nice add-on option for power-consumers, not the only way to get access to something.


  • There’s a weird divide between self-determined identity and external classifications. Often, a culture forms around the label and the external label stops being relevant because the term has more social/cultural implications than practical implications. Some people internalize the label as that’s how they wish to steer their future interactions, and others ignore the label and move on with their lives.

    You can watch all of Star Trek, and some parts of society will label you a Trekkie if they find out, but it’s up to you whether you choose to identify as a Trekkie, or just go about your life not making a big deal about it.


  • Assuming enthusiastic consent, good faith, and that you meant “sex/body they want” instead of “gender they want” (because gender is just a social construct):

    On another hand, it would erase their identity as trans people.

    I don’t think it would. Identities are built from life experiences, and having lived through transition they’d still be trans even if there were no traces of it on their body. A war veteran doesn’t stop being a veteran just because the war ended.

    consider it a genocide

    The definition of genocide depends on intent! Even in wars, etc. It’s only genocide if you’re specifically trying to erase/displace people/culture.

    • Trying to cure gender dysphoria: it’s not genocide, it’s medical treatment.

    • Trying to “fix” people to make them fit into society: it’s genocide.

    turning them into what they want would mean there is no more trans people

    There are identities that don’t stop being trans even if you give them the body they want:

    • A non-binary person’s desired sex/body and social gender might not match. Even with the perfect body (if one exists), they might still identify as trans because that body doesn’t match their social gender.

    • For genderfluid people, there might not be one singular perfect body. Even if their body constantly updated to suit them, they’d probably still identify as trans because they’d be constantly transitioning…




  • The website does a bad job explaining what its current state actually is. Here’s the GitHub repo’s explanation:

    Memory Cache is a project that allows you to save a webpage while you’re browsing in Firefox as a PDF, and save it to a synchronized folder that can be used in conjunction with privateGPT to augment a local language model.

    So it’s just a way to get data from browser into privateGPT, which is:

    PrivateGPT is a production-ready AI project that allows you to ask questions about your documents using the power of Large Language Models (LLMs), even in scenarios without an Internet connection. The project provides an API offering all the primitives required to build private, context-aware AI applications.

    So basically something you can ask questions like “how much butter is needed for that recipe I saw last week?” and “what are the big trends across the news sites I’ve looked at recently?”. But eventually it’ll automatically summarize and data mine everything you look at to help you learn/explore.

    Neat.


  • I agree that older commercialized battery types aren’t so interesting, but my point was about all the battery types that haven’t had enough R&D yet to be commercially mass-produced.

    Power grids don’t care much about density - they can build batteries where land is cheap, and for fire control they need to artificially space out higher-density batteries anyway. There are heaps of known chemistries that might be cheaper per unit stored (molten salt batteries, flow batteries, and solid state batteries based on cheaper metals), but many only make sense for energy grid applications because they’re too big/heavy for anything portable.

    I’m saying it’s nuts that lithium ion is being used for cases where energy density isn’t important. It’s a bit like using bottled water on a farm because you don’t want to pay to get the nearby river water tested. It’s great that sodium ion could bring new economics to grid energy storage, but weird that the only reason it got developed in the first place was for a completely different industry.





  • Honestly, I don’t think that there’s room for a competitor until a whole new paradigm is found. PyTorch’s community is the biggest and still growing. With their recent focus on compilation, not only are TF and Jax losing any chance at having an advantage, but the barrier to entry for new competitors is becoming much higher. Compilation takes a LOT of development time to implement, and it’s hard to ignore 50-200% performance boosts.

    Community size tends to ultimately drive open source software adoption. You can see the same with the web frameworks - in the end, most people didn’t learn React because it was the best available library, they learned it because the massive community had published so many tutorials and driven so many job adverts that it was a no-brainer to choose it over Angular, Vue, etc. Only the paradigm-shift libraries like Svelte and Htmx have had a chance at chipping away at React’s dominance.


  • The easiest way to get the basics is to search for articles, online courses, and youtube videos about the specific modules you’re interested in. Papers are written for people who are already deep in the field. You’ll get there, but they’re not the most efficient way to get up to speed. I have no experience with textbooks.

    It helps to think of PyTorch as just a fancy math library. It has some well-documented frameworky structure (nn.Module) and a few differentiation engines, but all the deep learning-specific classes/functions (Conv2d, BatchNorm1d, ReLU, etc.) are just optimized math under the hood.

    You can see the math by looking for projects that reimplement everything in numpy, e.g. picoGPT or ConvNet in NumPy.

    If you can’t get your head around the tensor operations, I suggest searching for “explainers”. Basically for every impactful module there will be a bunch of “(module) Explained” articles or videos out there, e.g. Grouped Convolution, What are Residual Connections. There are also ones for entire models, e.g. The Illustrated Transformer. Once you start googling specific modules’ explainers, you’ll find people who have made mountains of them - I suggest going through their guides and learning everything that seems relevant to what you’re working on.

    If you’re not getting an explanation of something, just google and find another one. People have done an incredible job of making this information freely accessible in many different formats. I basically learned my way from webdev to an AI career with a couple years of casually watching YouTube videos.