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Cake day: June 29th, 2023

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  • The go-to monkey paw scenario could work, where they receive immortality, but have to serve the summoned entity forever as deformed (and perhaps always hurting) “things”? However, that is maybe a bit too predictable. What if they mess up the spell and instead mark themselves for sacrifice?

    They slowly realise this due to a symbol appearing on them, which could slowly spread further on their body (maybe hurting too). You could make it more interesting by pitting them against one another by suggesting that only a few have to die for the sacrifice to be complete. If their characters are not that close to one another, it could lead to some interesting decisions on their part, haha.

    The process could span a week or more, where their body gets engulfed more and more by the mark. They could use this time to review what went wrong in the summoning process, and how to potentially reverse it. I’ll leave those details to you, if you’re interested in pursuing this idea :D



  • Piracy. I’d buy albums if I had money, though. I’ll slowly phase into getting them once I get some more cash.

    I can find most stuff I listen to, and I rarely grow my music library. I mostly listen to 20-30 albums, with some more mainstream music peppered in.

    My music library currently sits at 90 gigabytes (mostly flacs), so quite small compared to others I’ve seen around here. Still, I have plenty of variation to keep me entertained :D

    If you have Tidal, aren’t there some apps to rip the lossless audio from there? You could get most of the stuff that you need, and then cancel the subscription. If you feel bad, maybe order some merch from the band, haha.


  • Click for longer opinion

    If I remember correctly, even though Fuchsia is used in production, it is mainly targetting mobile or IoT devices. Nevertheless, the underlying micro-kernel, Zircon, is written in C/C++, which differs from Redox. Now, I’m not saying that Redox solves everything by writing the kernel in Rust. It will require plenty unsafe blocks to achieve what it needs, but it makes you aware beforehand that you should be careful about how you implement that bit of code. Having this clear marking could also make the kernel code review process more likely to catch issues.

    Disregarding this, if I am not mistaken, Redox aims to be a drop-in replacement for Linux one day, both for desktop and server, while Fuchsia only wishes to be integrated in/replace Android. Linux is perfectly fine for most use cases, I am not suggesting otherwise! However, given how many issues resulted from overflow/memory corruption issues that could have been potentially easier to identify if Rust (or any other memory safe language) was used, you’d think that there is incentive to rely on it for kernel development. Linus himself made this decision as well when allowing Rust to be used in the Linux kernel development (albeit perhaps a bit too early).

    The Linux kernel is not flawed, and Redox is probably years away from being even near it. However, having memory-safety from the get-go as a requirement for developing the kernel could lead to fewer exploits, compared to what we have today with Linux. Just as you’ve said, most users are not aware of it/they don’t care, but the big players will care about keeping information safe on their servers. Just to conclude, Redox OS is not just Linux rewritten in Rust, and could potentially have many other benefits that are particularly juicy for data centers. Too bad it’s not production ready yet :D



  • I see your point. However, integrating Rust properly in the Linux kernel is an uphill battle. Redox OS is not at all close to being stable, but it showcases that you can build a Rust kernel from scratch, and integrate it into an OS that meets some of the requirements of a modern one. Of course, considering it a toy project and glancing over its potential doesn’t help with adoption. They even mention in their description that currently they can only support a community manager and a student developer with the current donations. When you compare that to the amount of money and developers involved in the Linux kernel, it’s insignificant.

    I was not suggesting that the Rust For Linux devs jump ship, but it could be beneficial for the investors behind the project to look at alternatives. Heck, the Linux kernel started as a toy project itself. I believe that a team focused solely on such a Rust-only kernel could spearhead needed changes to reach something stable, as opposed to investing time and money into fighting established C developers to integrate a memory-safe language in the kernel fully.



  • If I am not mistaken, the difference was that the Internet Archive was distributing books with a DRM that would make the PDF unusable after a certain time. You could relate it to how a physical library offers books for a limited time, for free. Now, of course, one could bypass the DRM or copy the contents differently, but so can another person photocopy a book they borrowed physically. Meanwhile, other physical libraries are allowed to distribute e-books, but I’m not sure if that’s made possible due to licensing fees.

    I’m not saying that they approached this well, especially given the copyright laws in the US, but it was indeed a good thing for the normal person at the time. Too bad that the judicial system in the US is biased towards leeching companies. I really can’t wait to see the AI vs publishers fight, though. Let’s see who has deeper pockets and better plants in the courts :D




  • You’re right. I read past the “I want to learn ML” and went straight to “do something useful with the data”.

    If the goal is to understand how modern LLMs work, it’s also good to read up on RNNs and LSTMs. For this, 3Blue1Brown does an amazing job, and even posted an in-depth video about transformers. I’d watch that next, followed by implementing a simple transformer in PyTorch (perhaps using the existing blocks).

    You could argue that it’s important to design everything from scratch first, but it’s easier to first go high level, see how the network behaves, and then attempt to implement it yourself based on the paper. It is up to OP how comfortable he is with the topic though 😁


  • Depending on how much compute you have available, you can look into finetuning models from HuggingFace (e.g. Llama 3, or a smaller Phi model). Look into LoRA, and try to learn how the model you choose calculates the loss.

    There are various ways to train, and usually involves masking the input by replacing random input tokens with the mask token. I won’t go into too much detail with this, because it’s a lot to explain, and I suggest you read an article on this (link1 or link2)