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Joined 3 years ago
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Cake day: July 18th, 2021

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  • Either ranked-choice voting or majority judgement.

    Here's why

    Majority Judgment:

    1. Voters grade each candidate on a scale (e.g. Excellent, Good, Fair, Poor, Reject)
    2. The winner is determined by the highest median grade
    3. Ties are broken by measuring how many voters gave grades above and below the median

    Ranked Choice Voting:

    1. Voters rank candidates in order of preference
    2. If no candidate has >50%, the lowest-ranked candidate is eliminated
    3. Their votes transfer to those voters’ next choices
    4. Process repeats until someone has majority

    Majority Judgment optimizes for:

    1. Consensus/Compromise.

    By using median grades, it finds candidates who are “acceptable” to a broad swath of voters. A candidate strongly loved by 40% but strongly disliked by 60% will typically lose to someone viewed as “good enough” by most. This pushes politics toward centrist candidates who may not be anyone’s perfect choice but whom most find acceptable. The grading system lets voters express “this candidate meets/doesn’t meet my minimum standards” rather than just relative preferences

    2. Merit-based evaluation

    Voters judge each candidate against an absolute standard rather than just comparing them. This can help identify when all candidates are weak (if they all get low grades) or when multiple candidates are strong. It moves away from pure competition between candidates toward evaluation against civic ideals

    Ranked Choice Voting optimizes for:

    1. Coalition building

    By eliminating lowest-ranked candidates and redistributing votes, it rewards candidates who can be many voters’ second or third choice. This encourages candidates to appeal beyond their base and build broader coalitions. Unlike MJ, it’s more focused on relative preferences than absolute standards

    2. Elimination of “spoiler effects”

    Voters can support their true first choice without fear of helping their least favorite candidate win. This allows multiple similar candidates to run without splitting their shared base. The system is built around the idea that votes should transfer to ideologically similar alternatives


    Both systems optimize for honest voting more than plurality voting, but in different ways:

    MJ encourages honest evaluation because exaggerating grades can backfire if too many others don’t follow suit RCV encourages honest ranking because putting your true preference first doesn’t hurt your later choices

    The key philosophical difference is that:

    • MJ asks “What level of support does each candidate have across the whole electorate?”
    • RCV asks “Which candidate has the strongest coalition of support when similar preferences are consolidated?”

    This means MJ tends to favor broad acceptability while RCV tends to favor strong but potentially narrower bases of support that can build winning coalitions. Neither approach is inherently more democratic - they just emphasize different aspects of democratic decision-making. </details>


  • Thanks for sharing your method.

    As to your take on Anki, I think it’s fair and accurate. I agree with you in that the learning curve is not in the features or the interface, but as you said: in the pacing. I really hope I can try to space the cards as much as possible, so that a regular practice doesn’t become burdensome.


  • I’m generally skeptical of comments on the internet, so almost every time I have read comments like this one that you’re reading right now, I’ve been like “yeah right”. Kinda like how “lol” means “laughing out loud” but when you read it online you don’t really expect whoever wrote “lol” to have laughed out loud? Anyway, I was drinking coffee, I read your comment, I snorted in laughter, and now my white shirt is full of coffee.

    I guess I’m also kinda mad at myself for laughing so hard at such a silly joke. Regardless, have an updoot 👍



  • and Bostrom’s simulation hypothesis and Pascal’s wager, all subject to serious validity threats. All of these thought experiments are unfalsifiable. They can all be explained with different theories. They all rely on circular reasoning. They all anthropomorphize entities that maybe don’t resemble humans at all. They all fall for the mind projection fallacy. They all are prey to selection bias, because they cherry-pick scenarios among countless alternatives.





  • My brother has a Framework 13 and mainly uses a combination of NixOS and Windows. Most of the time he uses NixOS, but sometimes the software he needs is broken on Nix. When that happens, he reverts to a previous version of Nix or he boots onto Windows. He has Windows installed in one of the external-drive socket thingies that he keeps plugged in at all times in case he needs Windows.

    Apart from the occasional broken Nix package, he has had issues with the hyper-sensitive two-finger scrolling in Gnome (which I would say is not directly a Framework or Nix problem). Also, a while back, when I bought the computer with him, we bought Oloy RAM because it was fast and cheap, but that lead to weird crashes. Framework support helped us test the sticks and eventually we sold those sticks and got the Framework-tested Crucial sticks, which solved the problem. Finally, I remember he had to be careful about not just closing the laptop but actually clicking “sleep” and then closing it, because otherwise it would get super hot and lose a lot of battery.

    Despite these struggles, he recently told my Mac-loving girlfriend that he will not get a “disposable” computer. I take this to mean he will keep using his Framework laptop.


  • Professionals have large networks of neurons. They are sturdy and efficient from repeated use. Memory palaces help to start the construction of these large networks of neurons. Afterwards, as another commenter noted, the knowledge is deeply processed. Mnemonics are replaced by networks of meaning. It is no longer “This algorithm rhymes with tomato”, but “This algorithm is faster if the data is stored in faster hardware, but our equipment is old so we better use this other algorithm for now”.

    Broadly, the progression of learning is: superficial learning, deep learning, and transfer. Check out Visible Learning: The Sequel by John Hattie for more on this.

    Edit: To directly answer your question, experts have so many sturdy neural hooks on which to hang new knowledge that mnemonics become less and less necessary. Mnemonics may be particularly helpful when first learning something challenging, but are less necessary as people learn.

    You could also check out a paradox called the expert paradox. We used to think memory is boxes that get filled. This idea was directly challenged by Craik and Lockhart’s Levels of Processing. Levels of processing supports the idea that “the more you know, the faster you learn”. Note that this is domain-specific. In other words, an expert in dog training won’t learn quantum mechanics faster than anyone else.




  • How do you choose what facts matter? How do you choose how to communicate them? Who do you communicate them to? What does news reporting mean to you? What about news reporting makes it worth your precious time alive? What purpose do the people around you have when they amplify, ignore, or quiet your facts? These are all questions that are answered, explicitly or not, by everyone who communicates or relates to facts.

    We could play the impossible “no agenda” game. We could lie to ourselves and to others. Or, we could notice that whenever we are dealing with the truth, we have a point of view. We stand here and not there. We can learn to travel around the mountain of truth, so that we mitigate our blindspots. We can be explicit about where in the mountain we are standing (The north base? The vegetated slope? The summit?).

    Instead of playing the “god trick”, we can situate our knowledge. That’s the best we can do. Check out this article by Donna Haraway on situated knowledge. It changed my life. https://philpapers.org/archive/harskt.pdf