Textual modelling VS visual.
Have you ever come across infographics that are not just pictures but truly provide a comprehensive understanding of the system being modeled? 🤔
The kind of representation that’s easy to edit, maintain, and upgrade, and that a new engineer on the team can grasp without any huge additional explanations or clarifications from more experienced colleagues — and without formal text additions.
Personally, I’ve never encountered such diagrams. In fact, I believe it’s probably impossible to create them.
What I have seen are either poorly designed, ugly, and confusing diagrams or very beautiful and impressive visuals that evoke a good first gasp but quickly lead to a choke.😶
These pictures may capture your admiration for 10 minutes, but as soon as you dive deeper, they raise a bunch of questions. Questions that can only be expressed in text to be properly addressed and further processed.
Text carries far more meaning per unit of space than a picture, and it’s much easier to correct and expand. A picture, on the other hand, can only be redrawn.
Take, for example, a flowchart representing a loop versus just the word “loop” itself. Both convey the same meaning, especially to someone who already knows what a loop is.
You might argue that a flowchart makes it easier to explain the concept of a loop to someone with no programming background. But that’s not true.🙅♂️
Show a passerby a flowchart of a loop without annotations — they likely won’t understand a thing and will fall into fantasies and assumptions, depending on their background.
However, a concise text description of a loop, with a couple of examples and analogies, can communicate the concept to almost anyone.
From that moment on, the word “loop” will carry the same meaning for them as the clunky, angular flowchart with all its blocks, enabling its use to explain more complex matters. The word becomes a powerful container of essential meaning, staying flexible and applicable.
This is a simple example, but it extends seamlessly to diagrams and models of any size and nature — from the smallest to the largest. The bigger the system, the more relevant our problem becomes. Imagine a large system and the challenge of modeling its concepts and domain entities using both pictures and text. The latter approach feels promising and applicable. Whether it involves formal natural language specifications or specifications through common programming languages, it remains, ultimately, just text.
🔗
Formal textual specifications are easy to write. I mean, even if it requires a lot o mental efforts of course, it is easier to reflect in text, than in rock painting. Because text carry more meaning in any sense, be the end of the day - even per square centimeter, and they are much easier to work with and moving forward.
We can even write multiple versions of the same system specifications for different stakeholders, using their “native languages.”
Good luck doing that with pictures. You will end up redrawing them constantly or struggling to find a way to communicate with others and share knowledge, instead of doing actual work.
Systems thinking and engineering are collaborative efforts. A picture alone does not effectively transfer domain-specific information from one person to another. Textual specifications excel at this.
Domain-specific languages are built from textual entities, not emojis.
It feels to me that there’s really nothing to argue about😞
Have you ever come across infographics that are not just pictures but truly provide a comprehensive understanding of the system being modeled? 🤔
The kind of representation that’s easy to edit, maintain, and upgrade, and that a new engineer on the team can grasp without any huge additional explanations or clarifications from more experienced colleagues — and without formal text additions.
Personally, I’ve never encountered such diagrams. In fact, I believe it’s probably impossible to create them.
What I have seen are either poorly designed, ugly, and confusing diagrams or very beautiful and impressive visuals that evoke a good first gasp but quickly lead to a choke.
These pictures may capture your admiration for 10 minutes, but as soon as you dive deeper, they raise a bunch of questions. Questions that can only be expressed in text to be properly addressed and further processed.
Text carries far more meaning per unit of space than a picture, and it’s much easier to correct and expand. A picture, on the other hand, can only be redrawn.
Take, for example, a flowchart representing a loop versus just the word “loop” itself. Both convey the same meaning, especially to someone who already knows what a loop is.
You might argue that a flowchart makes it easier to explain the concept of a loop to someone with no programming background. But that’s not true.
Show a passerby a flowchart of a loop without annotations — they likely won’t understand a thing and will fall into fantasies and assumptions, depending on their background.
However, a concise text description of a loop, with a couple of examples and analogies, can communicate the concept to almost anyone.
From that moment on, the word “loop” will carry the same meaning for them as the clunky, angular flowchart with all its blocks, enabling its use to explain more complex matters. The word becomes a powerful container of essential meaning, staying flexible and applicable.
This is a simple example, but it extends seamlessly to diagrams and models of any size and nature — from the smallest to the largest. The bigger the system, the more relevant our problem becomes. Imagine a large system and the challenge of modeling its concepts and domain entities using both pictures and text. The latter approach feels promising and applicable. Whether it involves formal natural language specifications or specifications through common programming languages, it remains, ultimately, just text.
However, it is obviously not easy to reflect all domain intricacies in the code itself. That’s the point I was referring to here
Formal textual specifications are easy to write. I mean, even if it requires a lot o mental efforts of course, it is easier to reflect in text, than in rock painting. Because text carry more meaning in any sense, be the end of the day - even per square centimeter, and they are much easier to work with and moving forward.
We can even write multiple versions of the same system specifications for different stakeholders, using their “native languages.”
Good luck doing that with pictures. You will end up redrawing them constantly or struggling to find a way to communicate with others and share knowledge, instead of doing actual work.
Systems thinking and engineering are collaborative efforts. A picture alone does not effectively transfer domain-specific information from one person to another. Textual specifications excel at this.
Domain-specific languages are built from textual entities, not emojis.
It feels to me that there’s really nothing to argue about
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Hello,
Recently, I had a great time exploring Meta Threads.
The recommendation algorithms are fantastic, and the community is surprisingly positive.
It is much easier to grow audience in Threads than here.
So I’ll be posting frequently in my Threads feed, feel free to subscribe if you using it.
I’m not sure if it makes sense to replicate all the messages from Threads here,
but I’ll definitely continue to announce new blog posts in this channel and share what I find to be the most interesting news and topics.
Just like now — I’m sharing my new post with you!
🔗 The global state is not a Monster 🔗
Recently, I had a great time exploring Meta Threads.
The recommendation algorithms are fantastic, and the community is surprisingly positive.
It is much easier to grow audience in Threads than here.
So I’ll be posting frequently in my Threads feed, feel free to subscribe if you using it.
I’m not sure if it makes sense to replicate all the messages from Threads here,
but I’ll definitely continue to announce new blog posts in this channel and share what I find to be the most interesting news and topics.
Just like now — I’m sharing my new post with you!
Please open Telegram to view this post
VIEW IN TELEGRAM
🌭2
Programming can make a machine from you. I have seen so many guys who were diving too deep and too much into Silicon World, completely ignoring the value of social communication.
It is a dead end leading your brain to forget about what empathy is, so you might end up as “this stupid grumpy engineer”, even if you are smart in the dry residue.
All this is invented by people and for people.
It is a dead end leading your brain to forget about what empathy is, so you might end up as “this stupid grumpy engineer”, even if you are smart in the dry residue.
All this is invented by people and for people.
❤🔥2❤1🌭1
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The Obsolete Newsletter
Is Deep Learning Actually Hitting a Wall?
And is there a “basically clear” path to AGI?
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Code is a representation of some content that is formal in terms of semantics and syntax of the used language, regardless of whether it is serialized into a string of characters or left in the form of a graph, tables, or triples. It is the text.
Simply put, text is a representation of language.
How does informatics look through such a prism of reasoning?
Informatics-in-small is still about scale where work with texts and codes is performed by a single agent, silicon or carbon - it doesn't matter.
Informatics-in-large is when multiple agents participate in working with potentially vast texts and codes, and it's a rabbit hole in terms of disciplines.
Through the "language prism", there are informatics disciplines that work with and study language in these ways:
• Philosophical logic, which focuses on finding the most compact descriptions for connecting texts and codes with the real world, as well as expressing the connection between formal and informal languages—in which texts and codes are represented—with reality. Philosophy of language can be considered part of this discipline as well.
• Cognitive science, which focuses on finding the most compact descriptions for understanding—translating texts and codes into internal representations in the human mind, and writing—generating texts and codes from internal representations in the human mind.
• Linguistics, which focuses on finding the most compact descriptions for text encoding and code textualization. Yes, it is linguistics.
• Computer science, which focuses on finding the most compact descriptions for transcoding these codes.
Semiotics studies can certainly be included here as well.
These disciplines are mature in their own right, but the cutting-edge AI field represents a synergy of all of them, often manifesting in unexpected ways and sometimes devolving into hype-driven ventures.
Regardless of AI hype, this subject is fascinating. The way text and language function as bridges, extracting abstract meanings from the amodal "thought space" and translating them into relative reality, is truly remarkable and captivating.
Simply put, text is a representation of language.
How does informatics look through such a prism of reasoning?
Informatics-in-small is still about scale where work with texts and codes is performed by a single agent, silicon or carbon - it doesn't matter.
Informatics-in-large is when multiple agents participate in working with potentially vast texts and codes, and it's a rabbit hole in terms of disciplines.
Through the "language prism", there are informatics disciplines that work with and study language in these ways:
• Philosophical logic, which focuses on finding the most compact descriptions for connecting texts and codes with the real world, as well as expressing the connection between formal and informal languages—in which texts and codes are represented—with reality. Philosophy of language can be considered part of this discipline as well.
• Cognitive science, which focuses on finding the most compact descriptions for understanding—translating texts and codes into internal representations in the human mind, and writing—generating texts and codes from internal representations in the human mind.
• Linguistics, which focuses on finding the most compact descriptions for text encoding and code textualization. Yes, it is linguistics.
• Computer science, which focuses on finding the most compact descriptions for transcoding these codes.
Semiotics studies can certainly be included here as well.
These disciplines are mature in their own right, but the cutting-edge AI field represents a synergy of all of them, often manifesting in unexpected ways and sometimes devolving into hype-driven ventures.
Regardless of AI hype, this subject is fascinating. The way text and language function as bridges, extracting abstract meanings from the amodal "thought space" and translating them into relative reality, is truly remarkable and captivating.
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A week ago, I promised on Threads to compile and share a top-notch self-stud guide for wannabe software engineers.
I'm not sure if anyone here will find it useful, since you're all seasoned professionals who have already learned it all.
But I'd love you to help me spread the word, or read it and give feedback on its structure and message.
Thank you!
https://guide.ivanzakutnii.com
I'm not sure if anyone here will find it useful, since you're all seasoned professionals who have already learned it all.
But I'd love you to help me spread the word, or read it and give feedback on its structure and message.
Thank you!
https://guide.ivanzakutnii.com
Ivanzakutnii
Hello Friend, Start here! | The Hard Way to SWE Excellence v1.2
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@ me: hardly registered in the content management iOS app for microblogs, developer on threads was advertising and released in app store.
@ me: trying to link my threads to it — does not work in. giving the dude feedback
@ the dev dude: saying - “bro you got to wait, I know it is annoying, but it is what it is”
Delightful.
@ me: trying to link my threads to it — does not work in. giving the dude feedback
@ the dev dude: saying - “bro you got to wait, I know it is annoying, but it is what it is”
Delightful.
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People's belief that thinking is inherently visual seems to stem from our ability to think subjectively through visuals, assuming that we have more visual content in the flows of our minds than auditory or kinesthetic.
Followers of this belief lump everything else into one heap - either as abstractions, mere narratives, or don't consider it thinking at all.
This leads to the desire to communicate through images and better express these images.
Text is something else - first we perceive it visually, then we pronounce it audially in our mind, after which this text can bloom into various synesthetic representations for all senses.
Mind and thinking are inherently synesthetic by nature, and text, not without reason, better allows expressing this synesthesia, extracting formalities from it, and then these texts allow rendering it back into synesthetic representation.
—
Picture from dall-e prompt “modern systems thinking practitioner”
Not a hobo!
Followers of this belief lump everything else into one heap - either as abstractions, mere narratives, or don't consider it thinking at all.
This leads to the desire to communicate through images and better express these images.
Text is something else - first we perceive it visually, then we pronounce it audially in our mind, after which this text can bloom into various synesthetic representations for all senses.
Mind and thinking are inherently synesthetic by nature, and text, not without reason, better allows expressing this synesthesia, extracting formalities from it, and then these texts allow rendering it back into synesthetic representation.
—
Picture from dall-e prompt “modern systems thinking practitioner”
Not a hobo!
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I am working a lot on media presence now, and keep building community on Threads (blue sky does not kick in at all.)
The next step is a substack newsletter that I called “The Recursive Mind”
What about the naming?
The next step is a substack newsletter that I called “The Recursive Mind”
What about the naming?
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Oh no, no way :)
It is the textbook of courses, not much sense to just read it without joining.
Well then, I will focus on finishing current courses on distributed computation models and then will jump into it ^ fully 🤷♂️
It is the textbook of courses, not much sense to just read it without joining.
Well then, I will focus on finishing current courses on distributed computation models and then will jump into it ^ fully 🤷♂️
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If you work/stress yourself to apathy, obviously you need some help.
But you folks, who can afford or don't trust therapy exists, I know.
So this is for you — if you find yourself in such state, don't be stupid — DO NOTHING.
Literally. As much nothing as possible. No cheap dopamine, no use, no stupid kick-in engine parachute jumps.
Just nothing. Idk lay. Sleep. Walk? Read as silly book as possible.
Let the poor you rest at last.
But you folks, who can afford or don't trust therapy exists, I know.
So this is for you — if you find yourself in such state, don't be stupid — DO NOTHING.
Literally. As much nothing as possible. No cheap dopamine, no use, no stupid kick-in engine parachute jumps.
Just nothing. Idk lay. Sleep. Walk? Read as silly book as possible.
Let the poor you rest at last.
🌭1
I don't know how to feel about it and think, haven't tried it yet. At first glance, it seems to me as a route to degradation.
Here it the thing he is talking about:
https://github.com/paralleldrive/sudolang-llm-support
It just feel so lame to me:
So what? We can finally tell AI how to properly render 3 classes fir crud boxing and their methods?
Pathetic.
Here it the thing he is talking about:
https://github.com/paralleldrive/sudolang-llm-support
It just feel so lame to me:
For most simple prompts, natural language is better. Use it. But if you need the AI to follow a program, obey constraints, keep track of complex state, or implement complex algorithms, SudoLang can be extremely useful.
So what? We can finally tell AI how to properly render 3 classes fir crud boxing and their methods?
Pathetic.
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Btw, I have just dropped new blogpost about good and bad programming habits.
Take a look: https://ivanzakutnii.com/blog/the-good-and-bad-habbits
Take a look: https://ivanzakutnii.com/blog/the-good-and-bad-habbits
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