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Joined 2 years ago
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Cake day: July 14th, 2023

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  • only a tool

    “The essence of technology is by no means anything technological”

    Every tool contains within it a philosophy — a particular way of seeing the world.

    But especially digital technologies… they give the developer the ability to embed their values into the tools. Like, is DoorDash just a tool?






  • To solve climate change, we need two fundamental beliefs:

    • There is an urgent problem
    • We are capable of taking meaningful action

    This graph proves that we can take meaningful action. That proof is essential to our success.

    I don’t understand the people who insist that while there is an urgent problem, we have never done anything to address it, we’re currently doing nothing to address, and we will never do anything to address it.

    What is the point of that belief?

    Perhaps the certainty of failure is more comforting than the vulnerability of working towards a success that isn’t guaranteed.


  • To add to the other replies: This is what AI is for. Not to replace labor, but to enhance the ruling class’ ability to exploit labor.

    As a convenient side effect: If you use AI to spam people with bug reports, you’re basically DDoSing them… unless they then decide to use AI to help triage the avalanche. And wouldn’t you know it, Google just happens to sell AI to help you solve this problem they made for you!

    “Nice FOSS project you got there. It’d be a shame if something happened to it.”

    And also also: If FOSS in general turns into a ghost town… where are you gonna turn to get that boilerplate code you need to do a common task? That’s right, AI baby! All roads lead to boiling the Great Lakes so Nvidia can pay itself back.







  • The original source was much more sensible.

    The comparison makes sense for evaluating whether you’re over-invested in something. Like, if Nvidia suddenly poofed out of existence, would it seriously be worth 16% of everything the whole country makes in a year to get it back?

    Owning a car that’s worth 16% of your yearly income sounds reasonable, no matter what your actual income is. A Pokemon card collection that’s 16% of your income is probably too risky, no matter what your actual income is.

    Also, GDP is a decent scale to use for charting investment in a productivity tool, because if GDP ramped up at the same time as investment then it looks less like a bubble, even if they both ramp up quickly.

    But that’s not what we see. We see a sudden and volatile shift, nothing like the normal pattern before the hype.


  • I think maybe the biggest conceptual mistake in computer science was calling them “tests”.

    That word has all sorts of incorrect connotations to it:

    • That they should be made after the implementation
    • That they’re only useful if you’re unsure of the implementation
    • That they should be looking for deviations from intention, instead of giving you a richer palette with which to paint your intention

    You get this notion of running off to apply a ruler and a level to some structure that’s already built, adding notes to a clipboard about what’s wrong with it.

    You should think of it as a pencil and paper — a place where you can be abstract, not worry about the nitty-gritty details (unless you want to), and focus on what would be right about an implementation that adheres to this design.

    Like “I don’t care how it does it, but if you unmount and remount this component it should show the previous state without waiting for an HTTP request”.

    Very different mindset from “Okay, I implemented this caching system, now I’m gonna write tests to see if there are any off-by-one errors when retrieving indexed data”.

    I think that, very often, writing tests after the impl is worse than not writing tests at all. Cuz unless you’re some sort of wizard, you probably didn’t write the impl with enough flexibility for your tests to be flexible too. So you end up with brittle tests that break for bad reasons and reproduce all of the same assumptions that the impl has.

    You spent extra time on the task, and the result is that when you have to come back and change the impl you’ll have to spend extra time changing the tests too. Instead of the tests helping you write the code faster in the first place, and helping you limit your tests to only what you actually care about keeping the same long-term.


  • No apps, no code, just intent and execution.

    So the only problems you’re left with are:

    • Making a precise description of what you want, at high and low levels of detail with consistent terminology
    • Verifying that the system is behaving as you expect, by exercising specific parts of it in isolation
    • The ability to make small incremental steps from one complete working state to the next complete working state, so you don’t get stuck by painting yourself into a corner

    Problems which… code is much better than English at handling.

    And always will be.

    Almost like there’s a reason code exists other than just “Idk let’s make it hard so normies can’t do it mwahaha”.