I disagree with the author at a surface level; we can retain much more than 90% of what we read. The curious can look up deep reading strategies, e.g., those summarized by Benjamin Keep.
At a deeper level, though, there’s truth that we have limited time here; we can’t read everything.
I got much better answers with this prompt: “ Jokes are funny precisely because they play on knowledge on two poles: (i) at first listen, they’re surprising, and (ii) upon review, they’re obvious.
Let’s think through many many options to answer this joke that only focus on surprising the listener in section 1. And in section 2 we’ll focus on finding/filtering for the ones that are obvious in hindsight.
“Why did the sun climb a tree?”
In this case, let’s note that the sun doesn’t climb anything, so there’s two meanings at play here: one is that the sun’s light seems to climb up the tree, and the other is an anthropomorphization of the sun climbing the tree like an animal. So, to be funny, the joke should play on the second meaning as a surprise, but have the first meaning as answer with an obviousness to it. Or vice versa.”
Here’s a descent ones:
- to leaf the ground behind
- because it heard the leaves were throwing shade
Sim Daltonism lets you see through the eyes of someone with a color blindness. While the colors shown are a good approximation of what a color blind person would see, you should not expect them to be perfect.
Everyone has his own perception of colors that differs slightly from other people, and color blindness are often partial at different degrees. More importantly, cameras do not have the same spectral response as cones in your eyes, so the simulation has to make some assumptions about the frequency composition of the colors.
I’m colorblind and haven’t found a simulator that comes close to what it’s like for me. This app doesn’t do it either.
What would “close to what it’s like” entail exactly?
Would it mean that when you look at a simulation of the effects of your colorblindness, you see zero change from the unaltered view?
Or would it mean that it looks absolutely nothing like what you see because it’s transforming the base image by clamping the input colors to what you can see, and stretching that decimated color space out over the entire range of normal sensitivity?
Sometimes I suspect that the range of color qualia the human mind experiences is the same regardless of what actual color receptors one has; the sensation we call “red” is assigned to the lowest end of the input scale, regardless of whether or not the lowest end is at the normal wavelength, and that every filter that just removes color and provides a duller image is doing completely the wrong thing. But it’s a much simpler transformation to implement.
(I think the key to checking this would involve violently clashing colors. Or a way to make someone start growing new cone cells in their eyes.)
Also if you have had entirely too many conversations with the normies about “what does it look like for you” then please just ignore this, my SO is partially colorblind and gets that a lot!
> Would it mean that when you look at a simulation of the effects of your colorblindness, you see zero change from the unaltered view?
Ideally, yes. Although it's unlikely to match any one person's exact colour vision.
If you look at filtered images side-by-side, say from this collection on bored panda[1], to me the deutran images and the normal image are pretty much indistinguishable, while the protan image is close but slightly too green.
> Or would it mean that it looks absolutely nothing like what you see because it’s transforming the base image by clamping the input colors to what you can see, and stretching that decimated color space out over the entire range of normal sensitivity?
That's how most "colour blind filters" look in practice, yes. I don't think a lot of folks are setting up the transform correctly (or they are just straight-up using a colourblindness preview filter as if it were a colourblindness correction filter).
Part of it may be the display technology, rather than what the software thinks should look right.
Those RGB pixels are chosen and tuned to trick a certain homo sapiens baseline setup of chemical sensors neurological weighing of sensor inputs. Light from natural source is dramatically more-varied.
I really appreciate what Johnny Decimal is trying to solve - we're all struggling with digital organization and the appeal of a clean, simple system is undeniable.
Having implemented similar approaches across several teams, I can say it works beautifully for personal projects or well-defined small team efforts. But here's the challenge: most real-world information refuses to fit into single categories. A technical spec might be simultaneously system architecture, compliance documentation, etc. While the Johnny Decimal strength is its rigid simplicity, that's also its weakness when facing actual organizational complexity.
Rather than fighting these natural interconnections, I've found more success embracing them - using approaches that allow documents to exist in multiple contexts while maintaining the Johnny Decimal core goal of findability/searcability. The solution to chaos might not be enforcing a decimal hierarchy, but rather building systems that match how information actually flows in modern organizations.
For me, that is the value of tags. No need to have duplicates to have items represented in multiple categories, yet each appropriate category gets a nod about the particular item.
