7 ms·

I had a long ranting comment I deleted. I just don't like this trend of people presenting work in a way that makes you think some combo of 1) they discovered fr

by chermi 2mo ago

I had a long ranting comment I deleted. I just don't like this trend of people presenting work in a way that makes you think some combo of 1) they discovered from scratch themselves 2) it's new 3) they didn't try to cite or acknowledge where they learned it/point to good sources 4) they don't really care about trying to teach something deeply, they want shiny stuff that makes them seem deep. This post references specific parts/calculations, but you'd never know it was not news if you didn't know better.

TeMPOraL 2mo ago
And yet to this day, in AI threads, so many people act shocked and surprised if you dare follow the obvious implication and claim that understanding is a form of lossy compression.
- kazinator 2mo ago
  Shorter description isn't understanding, let alone of it is lossy. When you shorten a description in a lossy way, you are deciding a priori that some differences in the object don't matter, and it's not because you understand the object, but because it serves your goal of shortening the description.
  - Dylan16807 2mo ago
    Or you actually do understand it. You can't just assume smaller is better but it often is. And very often it's more information-dense.
    - kazinator 2mo ago
      You can compress syntax, losslessly even, with zero understanding of its semantics. Zero understanding not only imbued into the compressor/decompressor, but even the designer of the compressor doesn't require understanding the semantics. Actually, even of the syntax. A compression program can compress a book written in a language that the author of the program doesn't understand, on a topic he knows little about.
      - Dylan16807 2mo ago
        Finding common characters and building a list of words is a low level type of understanding. Doing it better does actually start directly representing syntax patterns and that's a less-low level of understanding. I think "losslessly even" is the wrong way to think about it. Lossless compression often requires less understanding than high quality lossy compression. If you can do a lossy compression that correctly decides what details are unimportant, that's a good sign of understanding.
        skinfaxi 2mo ago
        > I think "losslessly even" is the wrong way to think about it. Lossless compression often requires less understanding than high quality lossy compression. If you can do a lossy compression that correctly decides what details are unimportant, that's a good sign of understanding. This is the crux and reminds me of things like mp3 that exploit the nature of human hearing being limited to a frequency range.
    - vintermann 2mo ago
      We model the data. The model, hopefully, captures something real in the data. If it does, then it's fair to say that we understand the data better. But it's frankly a philosophical question what's real or not. No model is going to capture absolutely everything about the thing it models - at that point, it would be the thing. The best we can hope for is that it captures everything we care about. And no experiment or metric can tell you if you care about the right things. At best it can tell us if we care about a thing given other things we care about. "No cares in, no cares out". To make it a little more concrete: you could compress a string from back to front. You could build an LLM to help you do that. If you care about file size, that's almost certainly a bad idea, the forward LLM will be better for that purpose. But are there purposes for which the backward LLM might be better? I think that's not so hard to imagine. Often we wonder about "what came before".
      - Dylan16807 2mo ago
        A model will not capture every nuance of what it models, but it might capture every nuance you managed to measure properly.
        TeMPOraL 2mo ago
        Including the things you didn't even realize you measured, or even conceptualized at all.
  - adrianN 2mo ago
    I'm not aware of a better definition of "understanding" that would allow me to tell whether some system "understands" some other system. Do you happen to know one?
    - hnfong 2mo ago
      See: A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460. I mean, my interpretation is that the question Turing tried to answer is equivalent to "How can we determine whether machines understand humans/human thought?" This only works when both systems can talk about pretty much arbitrary things, but if you want a more general method for less complex systems, perhaps having one system simulate another system is sufficient. (Which is also another Turing invention)
      - adrianN 2mo ago
        There are many people who would claim that passing the Turing test is insufficient to show "understanding" (compare for example the Chinese Room thought experiment).
        hnfong 2mo ago
        Yes but it's (kind of?) a definition as you asked for. At this point, I am unaware of a better definition. I know the Chinese Room argument (and I disagree with it), but I'm not aware whether the proponents of that argument have a better definition of understanding other than "well, the Turing Test isn't enough"... --- PS: Interestingly the issue of compression is highly relevant regarding the Chinese Room argument -- the essential element in the Chinese Room argument is that the information is not compressed...
- ablob 2mo ago
  If you can predict other things by understanding something, is it truly lossy compression?
  - deleted 2mo ago
    [deleted]
- bonoboTP 2mo ago
  Of course it is but again "X is just Y" is often used to mislead. A brain is just neurons! A computer is just transistors! An LLM just predicts the next token! It's just like a parrot! It's just like a blurry jpeg of the internet! Kinda yes, but what do you use this for? It's a bad intuition pump is it leads people to conclude demonstrably false things about capabilities.
- qsera 2mo ago
  >understanding is a form of lossy compression.. No. Teaching is compression. Understanding and application is decompression.
  - Dylan16807 2mo ago
    I would roughly say that understanding is compression and application is decompression. You don't memorize the specific details, you memorize the flow of systems. But you get back to the details to put things into practice.
    - qsera 2mo ago
      I was speaking in the context of humans. When someone teaches you, the content coming from the teacher is very compressed. One decompress it when they can generalize and apply it. So understanding is compressed, but is not the act of compressing. I mean it is not compressed from a larger data or made by compressing a larger data. The larger data it represents never existed. It is like the definition of a fractal...
      - Dylan16807 2mo ago
        > I was speaking in the context of humans. So was I. > One decompress it when they can generalize and apply it. Generalizing is compression. Generalizing and applying are very different things. > So understanding is compressed, but is not the act of compressing. Understanding (noun) is compressed. Understanding (verb) is the act of creating understanding (noun) and is compression. > The larger data it represents never existed. The larger data is everything you take in about the subject. Experience and instruction.
        qsera 2mo ago
        >creating understanding (noun) and is compression. What is it compressing from?
        2mo ago
exe34 2mo ago
Reminds me of Stephen Wolfram "discovering" things in the sense that other people would say "today I learnt".
- jagged-chisel 2mo ago
  I find it bothersome that language works this way. You can spend your whole life discovering things that are well known by the rest of the world. But the minute that you mention to a large group that you “discovered” it, suddenly you’re taking credit for discovering it for all of mankind.
  - deleted 2mo ago
    [deleted]
- chermi 2mo ago
  Wolfram was my example in my original rant!
mpalmer 2mo ago
I'm glad to see someone feels similarly. There is nothing wrong with ignorance, but there's no excuse mistaking learning for invention. Especially from someone bearing the title "Developer Educator" I don't think it's the case here, but worth noting too that LLM-written blog posts adopt this tone seemingly by default. Never the least bit of surprise, wonder, doubt, or frustration to get in the way of the steady staccato beat of metaphors, conclusions... and three-item lists.
- bch 2mo ago
  > I'm glad to see someone feels similarly. There is nothing wrong with ignorance, but there's no excuse mistaking learning for invention. Especially from someone bearing the title "Developer Educator" >> a Developer Educator at ngrok with a passion for nerd-sniping developers. Maybe more the latter than former...
farfatched 2mo ago
I don't think this is a fair critique. The author of the post uses standard terminology like entropy coding and arithmetic coding, and cited a paper "in 2023, Google DeepMind released a paper arguing that language modeling and compression are two views of the same thing" which discusses it further. This blog post is great. Well explained, and clearly took a lot of effort. I don't interpret it as them claiming to have to discovered it independently.
- bonoboTP 2mo ago
  Citing 2023 makes it seem like this is newer than it is. Compression, prediction and intelligence have long been known to be deeply connected.
  - beagle3 2mo ago
    You expect every blog post to find the earliest relevant paper to cite, just so one could look at the year (without reading said paper - which would have made clear that the connection isn’t recent) to assess novelty? I don’t think that’s reasonable. It’s a blog post. If it was, say, a peer reviewed paper by Hinton or LeCunn that fails to cite Schmidhuber, that would be reasonable criticism in my opinion. (Spoiler: they fail to cite him)
    - hnfong 2mo ago
      Why would blog posts not be subject to such criticism? Either the author knew of prior work that argues the same thing and they ignored it, or they didn't know. And if one writes a 1000+ word article premised on this idea, wouldn't one be presumed to know at least in which century the idea originated from? Arguably these kind of blog posts should be more subject to such criticisms, because the blog posts purport to "teach" the general public about a concept in an authoritative tone (or at least the author seems to pose as knowledgeable in the subject), while for academic papers, everyone who actually reads the paper knows where the ideas came from anyway and it's mainly an issue of attribution (and maybe about fairly distributing the citation count...)
bonoboTP 2mo ago
I'm of two minds here. The pro is that the "you could have invented this" walkthrough from first principles is more engaging than "and then so and so introduced this term in 1972 and the definition is such and such". This style is a reaction to that boring and dry teaching style and tries to push towards what eg Feynman pointed at in the Brazil critique. The con is that you don't get to understand and see any of the history of the ideas or even the ballpark when it was discovered, you attribute it to the blog mentally and you don't know what is how new or old and can't reference it properly when talking to others.
- detourdog 2mo ago
  Disconnecting idea development from it's historic development is a disservice to the audience that may want to dig deeper.
  - wpietri 2mo ago
    If only there were some sort of way for a reader to dig deeper on a topic without a writer having to spoon-feed them the entire history of everything! It's wild to me what people here expect out of something they got for free and that was offered as a gift.
    - detourdog 2mo ago
      Mentioning any idea disconnected from its roots may not provide the terms needed to search. Snarky replies always appreciated
      - wpietri 2mo ago
        Your theory is that anybody who writes anything is obligated to make sure you can find any related information with one Google search? Again, to me that looks like wanting to be spoon fed. I have no idea why you think the world owes you endless 101-level discourse, but I hope you recognize you're setting yourself up for equally endless disappointment. If you take a little responsibility for your own education, you'll be happier.
hellohello2 2mo ago
You're reading this the wrong way I think, citations aren't given because its obviously a pedagogical article about well established stuff. Much like you wouldn't give citations in a blog post explaining calculus.
- detourdog 2mo ago
  One could give citations regarding calculus it's pretty interesting. Since it was done twice by both Newton and Liebniz. There must have been cultural developments in the 1660's that demanded calculus be invented.
  - hellohello2 2mo ago
    Yeah I agree, I was just saying I really doubt the author of that blog post was trying to take credit for the ideas they present.
    - detourdog 2mo ago
      Yes there is chasm between not citing references and stealing ideas. I assume no malice and just want contextual explanations.
teekert 2mo ago
The post says this is all part of gzip and LLMs, what are you saying? I’ve been using gzip my entire life. I read between the lines “this is common knowledge” throughout the piece. Throwing in some names and dates only makes this super clear story harder to read (and more like studying then the playful exploration this post was intended as).
- bonoboTP 2mo ago
  A short paragraph at the end on the origin of these ideas can be an easy way to dispel the misconception of potential beginner readers that the insights are novel.
lumost 2mo ago
I think you'll find that 95% of all academic presentations are telling stories out of other peoples work.
- chermi 2mo ago
  Yes, and it's made pretty obvious with all the background section and references.. which btw I'm not advocating for such baggage in a blog post. Just something.
jdthedisciple 2mo ago
I'm glad you're pointing this out because not only are these old insights, but I'm also pretty sure I've seen variations of this blog post years ago on even HN already. The author acting as if they discovered this independently had me feel the exact same way. Kinda irritating and almost ... disrespectful? Not sure of the right words to describe it tbh
- vasco 2mo ago
  Fabrice Bellard published this in 2023: https://bellard.org/ts_zip/ https://bellard.org/ts_zip/ > The ts_zip utility can compress (and hopefully decompress) text files using a Large Language Model. The compression ratio is much higher than with other compression tools. It's not only an old idea it's been totally done already.
userbinator 2mo ago
they discovered from scratch themselves If you followed the data compression scene in the 80s and early 90s, there were plenty of reinventions of LZ-ish and Huffman-ish algorithms (I also coded my own variant...), and people even tried to patent some of them, so at least for the basics I think it is something that many can discover independently; of course in these times, it's more likely they didn't.
ascorbic 2mo ago
The first sentence says she came across it when reading about compression. I didn't read that as her claiming to have discovered the idea or that it was a new idea, I read it as "today I learned". I think somebody who was unfamiliar with how compression algorithms or language models work would find this an approachable and interesting introduction. Not everybody studied information theory.
foo42 2mo ago
Perhaps tangential to your point, but I often write blog posts (although finish and publish far fewer than I start) where I write about something as it has occurred to me, informed by things I've absorbed no doubt, but without specific research. In such cases I explicitly avoid searching out prior work as a) seeing that something is well discussed and explored can take away the motivation to explore (in the same way reading puzzle solutions before starting might), and b) to avoid having green shoots of ideas shaped by the current of existing consensus. Now that doesn't mean I don't come back after doing my own thinking to see what the more well developed literature of people cleverer than me, who've thought far longer than me think; I just don't want to snuff out my own exploration at the start. As I say most of these I never publish as I'm mainly using writing as a vehicle for thought, but when I do I'm never sure how to flag them. I don't want (imaginary, lets be honest) readers thinking I'm deluded into thinking I've found something new. I want to come up with a tag I can put on them which adds a pithy disclaimer card at the top or something so I feel more comfortable publishing them.
jwr 2mo ago
Does everything have to be "news"?

by chermi 2mo ago

TeMPOraL 2mo ago

And yet to this day, in AI threads, so many people act shocked and surprised if you dare follow the obvious implication and claim that understanding is a form of lossy compression.

kazinator 2mo ago
Shorter description isn't understanding, let alone of it is lossy. When you shorten a description in a lossy way, you are deciding a priori that some differences in the object don't matter, and it's not because you understand the object, but because it serves your goal of shortening the description.
- Dylan16807 2mo ago
  Or you actually do understand it. You can't just assume smaller is better but it often is. And very often it's more information-dense.
  - kazinator 2mo ago
    You can compress syntax, losslessly even, with zero understanding of its semantics. Zero understanding not only imbued into the compressor/decompressor, but even the designer of the compressor doesn't require understanding the semantics. Actually, even of the syntax. A compression program can compress a book written in a language that the author of the program doesn't understand, on a topic he knows little about.
    - Dylan16807 2mo ago
      Finding common characters and building a list of words is a low level type of understanding. Doing it better does actually start directly representing syntax patterns and that's a less-low level of understanding. I think "losslessly even" is the wrong way to think about it. Lossless compression often requires less understanding than high quality lossy compression. If you can do a lossy compression that correctly decides what details are unimportant, that's a good sign of understanding.
      - skinfaxi 2mo ago
        > I think "losslessly even" is the wrong way to think about it. Lossless compression often requires less understanding than high quality lossy compression. If you can do a lossy compression that correctly decides what details are unimportant, that's a good sign of understanding. This is the crux and reminds me of things like mp3 that exploit the nature of human hearing being limited to a frequency range.
  - vintermann 2mo ago
    We model the data. The model, hopefully, captures something real in the data. If it does, then it's fair to say that we understand the data better. But it's frankly a philosophical question what's real or not. No model is going to capture absolutely everything about the thing it models - at that point, it would be the thing. The best we can hope for is that it captures everything we care about. And no experiment or metric can tell you if you care about the right things. At best it can tell us if we care about a thing given other things we care about. "No cares in, no cares out". To make it a little more concrete: you could compress a string from back to front. You could build an LLM to help you do that. If you care about file size, that's almost certainly a bad idea, the forward LLM will be better for that purpose. But are there purposes for which the backward LLM might be better? I think that's not so hard to imagine. Often we wonder about "what came before".
    - Dylan16807 2mo ago
      A model will not capture every nuance of what it models, but it might capture every nuance you managed to measure properly.
      - TeMPOraL 2mo ago
        Including the things you didn't even realize you measured, or even conceptualized at all.
- adrianN 2mo ago
  I'm not aware of a better definition of "understanding" that would allow me to tell whether some system "understands" some other system. Do you happen to know one?
  - hnfong 2mo ago
    See: A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460. I mean, my interpretation is that the question Turing tried to answer is equivalent to "How can we determine whether machines understand humans/human thought?" This only works when both systems can talk about pretty much arbitrary things, but if you want a more general method for less complex systems, perhaps having one system simulate another system is sufficient. (Which is also another Turing invention)
    - adrianN 2mo ago
      There are many people who would claim that passing the Turing test is insufficient to show "understanding" (compare for example the Chinese Room thought experiment).
      - hnfong 2mo ago
        Yes but it's (kind of?) a definition as you asked for. At this point, I am unaware of a better definition. I know the Chinese Room argument (and I disagree with it), but I'm not aware whether the proponents of that argument have a better definition of understanding other than "well, the Turing Test isn't enough"... --- PS: Interestingly the issue of compression is highly relevant regarding the Chinese Room argument -- the essential element in the Chinese Room argument is that the information is not compressed...
ablob 2mo ago
If you can predict other things by understanding something, is it truly lossy compression?
- deleted 2mo ago
  [deleted]
bonoboTP 2mo ago
Of course it is but again "X is just Y" is often used to mislead. A brain is just neurons! A computer is just transistors! An LLM just predicts the next token! It's just like a parrot! It's just like a blurry jpeg of the internet! Kinda yes, but what do you use this for? It's a bad intuition pump is it leads people to conclude demonstrably false things about capabilities.
qsera 2mo ago
>understanding is a form of lossy compression.. No. Teaching is compression. Understanding and application is decompression.
- Dylan16807 2mo ago
  I would roughly say that understanding is compression and application is decompression. You don't memorize the specific details, you memorize the flow of systems. But you get back to the details to put things into practice.
  - qsera 2mo ago
    I was speaking in the context of humans. When someone teaches you, the content coming from the teacher is very compressed. One decompress it when they can generalize and apply it. So understanding is compressed, but is not the act of compressing. I mean it is not compressed from a larger data or made by compressing a larger data. The larger data it represents never existed. It is like the definition of a fractal...
    - Dylan16807 2mo ago
      > I was speaking in the context of humans. So was I. > One decompress it when they can generalize and apply it. Generalizing is compression. Generalizing and applying are very different things. > So understanding is compressed, but is not the act of compressing. Understanding (noun) is compressed. Understanding (verb) is the act of creating understanding (noun) and is compression. > The larger data it represents never existed. The larger data is everything you take in about the subject. Experience and instruction.
      - qsera 2mo ago
        >creating understanding (noun) and is compression. What is it compressing from?
        2mo ago

exe34 2mo ago

Reminds me of Stephen Wolfram "discovering" things in the sense that other people would say "today I learnt".

jagged-chisel 2mo ago
I find it bothersome that language works this way. You can spend your whole life discovering things that are well known by the rest of the world. But the minute that you mention to a large group that you “discovered” it, suddenly you’re taking credit for discovering it for all of mankind.
- deleted 2mo ago
  [deleted]
chermi 2mo ago
Wolfram was my example in my original rant!

mpalmer 2mo ago

I'm glad to see someone feels similarly. There is nothing wrong with ignorance, but there's no excuse mistaking learning for invention. Especially from someone bearing the title "Developer Educator" I don't think it's the case here, but worth noting too that LLM-written blog posts adopt this tone seemingly by default. Never the least bit of surprise, wonder, doubt, or frustration to get in the way of the steady staccato beat of metaphors, conclusions... and three-item lists.

bch 2mo ago
> I'm glad to see someone feels similarly. There is nothing wrong with ignorance, but there's no excuse mistaking learning for invention. Especially from someone bearing the title "Developer Educator" >> a Developer Educator at ngrok with a passion for nerd-sniping developers. Maybe more the latter than former...

farfatched 2mo ago

I don't think this is a fair critique. The author of the post uses standard terminology like entropy coding and arithmetic coding, and cited a paper "in 2023, Google DeepMind released a paper arguing that language modeling and compression are two views of the same thing" which discusses it further. This blog post is great. Well explained, and clearly took a lot of effort. I don't interpret it as them claiming to have to discovered it independently.

bonoboTP 2mo ago
Citing 2023 makes it seem like this is newer than it is. Compression, prediction and intelligence have long been known to be deeply connected.
- beagle3 2mo ago
  You expect every blog post to find the earliest relevant paper to cite, just so one could look at the year (without reading said paper - which would have made clear that the connection isn’t recent) to assess novelty? I don’t think that’s reasonable. It’s a blog post. If it was, say, a peer reviewed paper by Hinton or LeCunn that fails to cite Schmidhuber, that would be reasonable criticism in my opinion. (Spoiler: they fail to cite him)
  - hnfong 2mo ago
    Why would blog posts not be subject to such criticism? Either the author knew of prior work that argues the same thing and they ignored it, or they didn't know. And if one writes a 1000+ word article premised on this idea, wouldn't one be presumed to know at least in which century the idea originated from? Arguably these kind of blog posts should be more subject to such criticisms, because the blog posts purport to "teach" the general public about a concept in an authoritative tone (or at least the author seems to pose as knowledgeable in the subject), while for academic papers, everyone who actually reads the paper knows where the ideas came from anyway and it's mainly an issue of attribution (and maybe about fairly distributing the citation count...)

bonoboTP 2mo ago

I'm of two minds here. The pro is that the "you could have invented this" walkthrough from first principles is more engaging than "and then so and so introduced this term in 1972 and the definition is such and such". This style is a reaction to that boring and dry teaching style and tries to push towards what eg Feynman pointed at in the Brazil critique. The con is that you don't get to understand and see any of the history of the ideas or even the ballpark when it was discovered, you attribute it to the blog mentally and you don't know what is how new or old and can't reference it properly when talking to others.

detourdog 2mo ago
Disconnecting idea development from it's historic development is a disservice to the audience that may want to dig deeper.
- wpietri 2mo ago
  If only there were some sort of way for a reader to dig deeper on a topic without a writer having to spoon-feed them the entire history of everything! It's wild to me what people here expect out of something they got for free and that was offered as a gift.
  - detourdog 2mo ago
    Mentioning any idea disconnected from its roots may not provide the terms needed to search. Snarky replies always appreciated
    - wpietri 2mo ago
      Your theory is that anybody who writes anything is obligated to make sure you can find any related information with one Google search? Again, to me that looks like wanting to be spoon fed. I have no idea why you think the world owes you endless 101-level discourse, but I hope you recognize you're setting yourself up for equally endless disappointment. If you take a little responsibility for your own education, you'll be happier.

hellohello2 2mo ago

You're reading this the wrong way I think, citations aren't given because its obviously a pedagogical article about well established stuff. Much like you wouldn't give citations in a blog post explaining calculus.

detourdog 2mo ago
One could give citations regarding calculus it's pretty interesting. Since it was done twice by both Newton and Liebniz. There must have been cultural developments in the 1660's that demanded calculus be invented.
- hellohello2 2mo ago
  Yeah I agree, I was just saying I really doubt the author of that blog post was trying to take credit for the ideas they present.
  - detourdog 2mo ago
    Yes there is chasm between not citing references and stealing ideas. I assume no malice and just want contextual explanations.

teekert 2mo ago

The post says this is all part of gzip and LLMs, what are you saying? I’ve been using gzip my entire life. I read between the lines “this is common knowledge” throughout the piece. Throwing in some names and dates only makes this super clear story harder to read (and more like studying then the playful exploration this post was intended as).

bonoboTP 2mo ago
A short paragraph at the end on the origin of these ideas can be an easy way to dispel the misconception of potential beginner readers that the insights are novel.

lumost 2mo ago

I think you'll find that 95% of all academic presentations are telling stories out of other peoples work.

chermi 2mo ago
Yes, and it's made pretty obvious with all the background section and references.. which btw I'm not advocating for such baggage in a blog post. Just something.

jdthedisciple 2mo ago

I'm glad you're pointing this out because not only are these old insights, but I'm also pretty sure I've seen variations of this blog post years ago on even HN already. The author acting as if they discovered this independently had me feel the exact same way. Kinda irritating and almost ... disrespectful? Not sure of the right words to describe it tbh

vasco 2mo ago
Fabrice Bellard published this in 2023: https://bellard.org/ts_zip/ https://bellard.org/ts_zip/ > The ts_zip utility can compress (and hopefully decompress) text files using a Large Language Model. The compression ratio is much higher than with other compression tools. It's not only an old idea it's been totally done already.

userbinator 2mo ago

they discovered from scratch themselves If you followed the data compression scene in the 80s and early 90s, there were plenty of reinventions of LZ-ish and Huffman-ish algorithms (I also coded my own variant...), and people even tried to patent some of them, so at least for the basics I think it is something that many can discover independently; of course in these times, it's more likely they didn't.

ascorbic 2mo ago

The first sentence says she came across it when reading about compression. I didn't read that as her claiming to have discovered the idea or that it was a new idea, I read it as "today I learned". I think somebody who was unfamiliar with how compression algorithms or language models work would find this an approachable and interesting introduction. Not everybody studied information theory.

foo42 2mo ago

Perhaps tangential to your point, but I often write blog posts (although finish and publish far fewer than I start) where I write about something as it has occurred to me, informed by things I've absorbed no doubt, but without specific research. In such cases I explicitly avoid searching out prior work as a) seeing that something is well discussed and explored can take away the motivation to explore (in the same way reading puzzle solutions before starting might), and b) to avoid having green shoots of ideas shaped by the current of existing consensus. Now that doesn't mean I don't come back after doing my own thinking to see what the more well developed literature of people cleverer than me, who've thought far longer than me think; I just don't want to snuff out my own exploration at the start. As I say most of these I never publish as I'm mainly using writing as a vehicle for thought, but when I do I'm never sure how to flag them. I don't want (imaginary, lets be honest) readers thinking I'm deluded into thinking I've found something new. I want to come up with a tag I can put on them which adds a pithy disclaimer card at the top or something so I feel more comfortable publishing them.

jwr 2mo ago

Does everything have to be "news"?