#fallacy — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #fallacy, aggregated by home.social.
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rot13 rot_gag.nfo
install rot13 for your OS
I've always liked playing with rot13
ROT13 is a simple letter substitution cipher that replaces a letter with the 13th letter after it in the Latin alphabet. It is a special case of the Caesar cipher which was developed in ancient Rome, and used by Julius Caesar in the 1st century BC[1] (see timeline of cryptography).
Usage
rot13 filename- Sbe lbhe frphevgl, guvf cbfg unf orra rapelcgrq jvgu EBG-13, gjvpr.
- ZvyvgnelTenqrRapelcgvba
sources:
man rot13(1)
man tr(1)
https://en.wikipedia.org/wiki/ROT13
https://en.wikipedia.org/wiki/Timeline_of_cryptography
#programming #ROT13 #encryption #fallacy #fun #joke #mathematics
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Once a clan agrees on a #fallacy, it must protect that fallacy with behavior. #Projection alone is unstable. Belief alone is fragile. #Doctrine alone is brittle.
https://survivorliteracy.com/2026/04/24/chapter-11-ritual-taboo-and-enforcement/
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I'd like to propose a new category of logical fallacy called the hallucinated herring. It's like a red-herring in that it distracts you from the correct logical argument except the herring doesn't even exist, it was just an AI hallucination. #logic #reason #fallacy #TheFishThatWasntThere
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Taken on Monday, 5 January, 2026 at 13:06 with Samsung Galaxy A05s at North Wenang Sub-district, Wenang District, Manado, North Sulawesi, Indonesia.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong #iknowright
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Taken on Monday, 5 January, 2026 at 13:06 with Samsung Galaxy A05s at North Wenang Sub-district, Wenang District, Manado, North Sulawesi, Indonesia.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong #iknowright
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You know that I always try to be balanced. For example, if the government implements a good policy then I praise it and if it is bad then I criticise it. I won't follow people's words that easily, especially if they are not my friends, you know.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong #iknowright
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You know that I always try to be balanced. For example, if the government implements a good policy then I praise it and if it is bad then I criticise it. I won't follow people's words that easily, especially if they are not my friends, you know.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong #iknowright
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I don't care of course, whatever. My mother is like that too, although in her case it happens a lot at her workplace. Many leaders are wrong, they must be told that they are wrong, they must be criticised. One of the reasons my mother was often discriminated against, sad.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality
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I don't care of course, whatever. My mother is like that too, although in her case it happens a lot at her workplace. Many leaders are wrong, they must be told that they are wrong, they must be criticised. One of the reasons my mother was often discriminated against, sad.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality
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Honestly, I'm often disliked because people say I like to argue with people who other people consider influential or powerful. When I was at school, I used to argue with my teachers a lot, of course, because most of them were not so smart enough to be my teachers.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality
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Honestly, I'm often disliked because people say I like to argue with people who other people consider influential or powerful. When I was at school, I used to argue with my teachers a lot, of course, because most of them were not so smart enough to be my teachers.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality
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Sorry for those of you who are offended, it's the fact. Not everything that the authorities say must be accepted or obeyed at face value. It's surprising how many people can easily believe anything the government says. I mean, at least check the truth first, be critical.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality
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Sorry for those of you who are offended, it's the fact. Not everything that the authorities say must be accepted or obeyed at face value. It's surprising how many people can easily believe anything the government says. I mean, at least check the truth first, be critical.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality
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There are still quite a lot of people who think that whatever an authority, such as the government, says is accepted as true. Argumentum ad verecundiam or appeal to authority. Thankfully I'm not like that, especially since I'm knowledgeable and well educated.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong
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There are still quite a lot of people who think that whatever an authority, such as the government, says is accepted as true. Argumentum ad verecundiam or appeal to authority. Thankfully I'm not like that, especially since I'm knowledgeable and well educated.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong
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There are still quite a lot of people who think that whatever an authority, such as the government, says is accepted as true. Argumentum ad verecundiam or appeal to authority. Thankfully I'm not like that, especially since I'm knowledgeable and well educated.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong
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There are still quite a lot of people who think that whatever an authority, such as the government, says is accepted as true. Argumentum ad verecundiam or appeal to authority. Thankfully I'm not like that, especially since I'm knowledgeable and well educated.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong
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There are still quite a lot of people who think that whatever an authority, such as the government, says is accepted as true. Argumentum ad verecundiam or appeal to authority. Thankfully I'm not like that, especially since I'm knowledgeable and well educated.
#sofiaflorina #ソフィアフロリナ #authority #authorities #becritical #government #governments #thegovernment #appealtoauthority #logicalfallacy #fallacy #fallacies #itstrue #itistrue #thatstrue #thatistrue #reality #thereality #provemewrong
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I saw this on Mastodon and almost had a stroke.
@davidgerard wrote:
“Most of the AI coding claims are conveniently nondisprovable. What studies there are show it not helping coding at all, or making it worse
But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.
These guys had one good experience with the bot, they got one-shotted, and now if you say “perhaps the bot is not all that” they act like you’re trying to take their cocaine away.”
First, the term is falsifiable, and proving propositions about algorithms (i.e., code) is part of what I do for a living. Mathematically human-written code and AI-written code can be tested, which means you can falsify propositions about them. You would test them the same way.
There is no intrinsic mathematical distinction between code written by a person and code produced by an AI system. In both cases, the result is a formal program made of logic and structure. In principle, the same testing techniques can be applied to each. If it were really nondisprovable, you could not test to see what is generated by a human and what is generated by AI. But you can test it. Studies have found that AI-generated code tends to exhibit a higher frequency of certain types of defects. So, reviewers and testers know what logic flaws and security weaknesses to look for. This would not be the case if it were nondisprovable.
You can study this from datasets where the source of the code is known. You can use open-source pull requests identified as AI-assisted versus those written without such tools. You then evaluate both groups using the same industry-standard analysis tools: static analyzers, complexity metrics, security scanners, and defect classification systems. These tools flag bugs, vulnerabilities, performance issues, and maintainability concerns. They do so in a consistent way across samples.
A widely cited analysis of 470 real pull requests reported that AI-generated contributions contained roughly 1.7 times as many issues on average as human-written ones. The difference included a higher number of critical and major defects. It also included more logic and security-related problems. Because these findings rely on standard measurement tools — counting defects, grading severity, and comparing issue rates — the results are grounded in observable data. Again, I am making a point here. It’s testable and therefore disproveable.
This is a good paper that goes into it:
In this paper, we present a large-scale comparison of code authored by human developers and three state-of-the-art LLMs, i.e., ChatGPT, DeepSeek-Coder, and Qwen-Coder, on multiple dimensions of software quality: code defects, security vulnerabilities, and structural complexity. Our evaluation spans over 500k code samples in two widely used languages, Python and Java, classifying defects via Orthogonal Defect Classification and security vulnerabilities using the Common Weakness Enumeration. We find that AI-generated code is generally simpler and more repetitive, yet more prone to unused constructs and hardcoded debugging, while human-written code exhibits greater structural complexity and a higher concentration of maintainability issues. Notably, AI-generated code also contains more high-risk security vulnerabilities. These findings highlight the distinct defect profiles of AI- and human-authored code and underscore the need for specialized quality assurance practices in AI-assisted programming.
https://arxiv.org/abs/2508.21634
Something I’ve started to notice about a lot of the content on social media platforms is that most of the posts people are liking, sharing, and memetically mutating—and then spreading virally—usually don’t include any citations, sources, or receipts. It’s often just some out-of-context screenshot with no reference link or actual sources.
A lot of the anti-AI content is not genuine critique. It’s often misinformation, but people who hate AI don’t question it or ask for sources because it aligns with their biases. The propaganda on social media has gotten so bad that anything other than heavily curated and vetted feeds is pretty much useless, and it’s filled with all sorts of memetic contagions with nasty hooks that are optimized for you algorithmically. I am at the point where I will disregard anything that is not followed up with a source. Period. It is all optimized to persuade, coerce, or piss you off. I am only writing about this because this I’m actually able to contribute genuine information about the topic.
That they said symbolic propositions written by AI agents (i.e., code) are non-disprovable because they were written by AI boggles my mind. It’s like saying that an article written in English by AI is not English because AI generated it. It might be a bad piece of text, but it’s syntactically, semantically, and grammatically English.
Basically, any string of data can be represented in a base-2 system, where it can be interpreted as bits (0s and 1s). Those bits can be used as the basis for symbolic reasoning. In formal propositional logic, a proposition is a sequence of symbols constructed according to strict syntax rules (atomic variables plus logical connectives). Under a given semantics, it is assigned exactly one truth value (true or false) in a two-valued logic system.
They are essentially saying that code written by AI is not binary, isn’t symbolically logical at all, and cannot be evaluated as true or false by implying it is nondisproveable. At the lowest level, compiled code consists of binary machine instructions that a processor executes. At higher levels, source code is written in symbolic syntax that humans and tools use to express logic and structure. You can also translate parts of code into formal logic expressions. For example, conditions and assertions in a program can be modeled as Boolean formulas. Tools like SAT/SMT solvers or symbolic execution engines check those formulas for satisfiability or correctness. It blows my mind how confidently people talk about things they do not understand.
Furthermore that they don’t realize the projection is wild to me.
@davidgerard wrote:
“But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.”
They are presenting a story—i.e., saying that the studies are not disprovable—and accusing computer scientists of using anecdotal evidence without actually providing evidence to support this, while expecting people to take it prima facie. You’re doing what you are accusing others of doing.
It comes down to this: they feel that people ought not to use AI, so they are tacitly committed to a future in which people do not use AI. For example, a major argument against AI is the damage it is doing to resources, which is driving up the prices of computer components, as well as the ecological harm it causes. They feel justified in lying and misinforming others if it achieves the outcome they want—people not using AI because it is bad for the environment. That is a very strong point, but most people don’t care about that, which is why they lie about things people would care about.It’s corrupt. And what’s really scary is that people don’t recognize when they are part of corruption or a corrupt conspiracy to misinform. Well, they recognize it when they see the other side doing it, that is. No one is more dangerous than people who feel righteous in what they are doing.
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I saw this on Mastodon and almost had a stroke.
@davidgerard wrote:
“Most of the AI coding claims are conveniently nondisprovable. What studies there are show it not helping coding at all, or making it worse
But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.
These guys had one good experience with the bot, they got one-shotted, and now if you say “perhaps the bot is not all that” they act like you’re trying to take their cocaine away.”
First, the term is falsifiable, and proving propositions about algorithms (i.e., code) is part of what I do for a living. Mathematically human-written code and AI-written code can be tested, which means you can falsify propositions about them. You would test them the same way.
There is no intrinsic mathematical distinction between code written by a person and code produced by an AI system. In both cases, the result is a formal program made of logic and structure. In principle, the same testing techniques can be applied to each. If it were really nondisprovable, you could not test to see what is generated by a human and what is generated by AI. But you can test it. Studies have found that AI-generated code tends to exhibit a higher frequency of certain types of defects. So, reviewers and testers know what logic flaws and security weaknesses to look for. This would not be the case if it were nondisprovable.
You can study this from datasets where the source of the code is known. You can use open-source pull requests identified as AI-assisted versus those written without such tools. You then evaluate both groups using the same industry-standard analysis tools: static analyzers, complexity metrics, security scanners, and defect classification systems. These tools flag bugs, vulnerabilities, performance issues, and maintainability concerns. They do so in a consistent way across samples.
A widely cited analysis of 470 real pull requests reported that AI-generated contributions contained roughly 1.7 times as many issues on average as human-written ones. The difference included a higher number of critical and major defects. It also included more logic and security-related problems. Because these findings rely on standard measurement tools — counting defects, grading severity, and comparing issue rates — the results are grounded in observable data. Again, I am making a point here. It’s testable and therefore disproveable.
This is a good paper that goes into it:
In this paper, we present a large-scale comparison of code authored by human developers and three state-of-the-art LLMs, i.e., ChatGPT, DeepSeek-Coder, and Qwen-Coder, on multiple dimensions of software quality: code defects, security vulnerabilities, and structural complexity. Our evaluation spans over 500k code samples in two widely used languages, Python and Java, classifying defects via Orthogonal Defect Classification and security vulnerabilities using the Common Weakness Enumeration. We find that AI-generated code is generally simpler and more repetitive, yet more prone to unused constructs and hardcoded debugging, while human-written code exhibits greater structural complexity and a higher concentration of maintainability issues. Notably, AI-generated code also contains more high-risk security vulnerabilities. These findings highlight the distinct defect profiles of AI- and human-authored code and underscore the need for specialized quality assurance practices in AI-assisted programming.
https://arxiv.org/abs/2508.21634
The big problem in discussions about AI in programming is the either-or thinking, when it’s not about using it everywhere or banning it entirely. Tools like AI have specific strengths and weaknesses. Saying ‘never’ or ‘always’ oversimplifies the issue and turns the narrative into propaganda that creates moral panic or shills AI. It’s a bit like saying you shouldn’t use a hammer just because it’s not good for brushing your teeth.
AI tends to produce code that’s simple, often a bit repetitive, and very verbose. It’s usually pretty easy to read and tweak. This helps with long-term maintenance. But AI doesn’t reason about code the way an experienced developer does. It makes mistakes that a human wouldn’t, potentially introducing security flaws. That doesn’t mean we shouldn’t use for where it works well, which is not everywhere.
AI works well for certain tasks, especially when the scope is narrow and the risk is low. Examples include generating boilerplate code, internal utilities, or prototypes. In these cases, the tradeoff is manageable. However, it’s not suitable for critical code like kernels, operating systems, compilers, or cryptographic libraries. A small mistake memory safety or privilege separation can lead to major failures. Problems with synchronization, pointer management, or access control can cause major problems, too.
Other areas where AI should not be used include memory allocation handling, scheduling, process isolation, or device drivers. A lot of that depends on implicit assumptions in the system’s architecture. Generative models don’t grasp these nuances. Instead of carefully considering the design, AI tends to replicate code patterns that seem statistically likely, doing so without understanding the purpose behind them.
Yes, I’m aware that Microsoft is using AI to write code everywhere I said it should not be used. That is the problem. However, political pundits, lobbyists, and anti-tech talking heads are discussing something they have no understanding of and aren’t specifying what the problem actually is. This means they can’t possibly lead grassroots initiatives into actual laws that specify where AI should not be used, which is why we have this weird astroturfing bullshit.
They’re taking advantage of the reaction to Microsoft using AI-generated code where it shouldn’t be used to argue that AI shouldn’t be used anywhere at all in any generative context. AI is useful for tasks like writing documentation, generating tests, suggesting code improvements, or brainstorming alternative approaches. These ideas should then be thoroughly vetted by human developers.
Something I’ve started to notice about a lot of the content on social media platforms is that most of the posts people are liking, sharing, and memetically mutating—and then spreading virally—usually don’t include any citations, sources, or receipts. It’s often just some out-of-context screenshot with no reference link or actual sources.
A lot of the anti-AI content is not genuine critique. It’s often misinformation, but people who hate AI don’t question it or ask for sources because it aligns with their biases. The propaganda on social media has gotten so bad that anything other than heavily curated and vetted feeds is pretty much useless, and it’s filled with all sorts of memetic contagions with nasty hooks that are optimized for you algorithmically. I am at the point where I will disregard anything that is not followed up with a source. Period. It is all optimized to persuade, coerce, or piss you off. I am only writing about this because this I’m actually able to contribute genuine information about the topic.
That they said symbolic propositions written by AI agents (i.e., code) are non-disprovable because they were written by AI boggles my mind. It’s like saying that an article written in English by AI is not English because AI generated it. It might be a bad piece of text, but it’s syntactically, semantically, and grammatically English.
Basically, any string of data can be represented in a base-2 system, where it can be interpreted as bits (0s and 1s). Those bits can be used as the basis for symbolic reasoning. In formal propositional logic, a proposition is a sequence of symbols constructed according to strict syntax rules (atomic variables plus logical connectives). Under a given semantics, it is assigned exactly one truth value (true or false) in a two-valued logic system.
They are essentially saying that code written by AI is not binary, isn’t symbolically logical at all, and cannot be evaluated as true or false by implying it is nondisproveable. At the lowest level, compiled code consists of binary machine instructions that a processor executes. At higher levels, source code is written in symbolic syntax that humans and tools use to express logic and structure. You can also translate parts of code into formal logic expressions. For example, conditions and assertions in a program can be modeled as Boolean formulas. Tools like SAT/SMT solvers or symbolic execution engines check those formulas for satisfiability or correctness. It blows my mind how confidently people talk about things they do not understand.
Furthermore that they don’t realize the projection is wild to me.
@davidgerard wrote:
“But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.”
They are presenting a story—i.e., saying that the studies are not disprovable—and accusing computer scientists of using anecdotal evidence without actually providing evidence to support this, while expecting people to take it prima facie. You’re doing what you are accusing others of doing.
It comes down to this: they feel that people ought not to use AI, so they are tacitly committed to a future in which people do not use AI. For example, a major argument against AI is the damage it is doing to resources, which is driving up the prices of computer components, as well as the ecological harm it causes. They feel justified in lying and misinforming others if it achieves the outcome they want—people not using AI because it is bad for the environment. That is a very strong point, but most people don’t care about that, which is why they lie about things people would care about.
It’s corrupt. And what’s really scary is that people don’t recognize when they are part of corruption or a corrupt conspiracy to misinform. Well, they recognize it when they see the other side doing it, that is. No one is more dangerous than people who feel righteous in what they are doing.
It’s wild to me that the idea that if you cannot persuade someone, it is okay to bully, coerce, harass them, or spread misinformation to get what you want—because your side is right—has become so normalized on the Internet that people can’t see why it is problematic.
That people think it is okay to hurt others to get them to agree is the most disturbing part of all of this. People have become so hateful. That is a large reason why I don’t interact with people on social media, really consume things from social media, or respond on social media and am writing a blog post about it instead of engaging with who prompted it.
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I saw this on Mastodon and almost had a stroke.
@davidgerard wrote:
“Most of the AI coding claims are conveniently nondisprovable. What studies there are show it not helping coding at all, or making it worse
But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.
These guys had one good experience with the bot, they got one-shotted, and now if you say “perhaps the bot is not all that” they act like you’re trying to take their cocaine away.”
First, the term is falsifiable, and proving propositions about algorithms (i.e., code) is part of what I do for a living. Mathematically human-written code and AI-written code can be tested, which means you can falsify propositions about them. You would test them the same way.
There is no intrinsic mathematical distinction between code written by a person and code produced by an AI system. In both cases, the result is a formal program made of logic and structure. In principle, the same testing techniques can be applied to each. If it were really nondisprovable, you could not test to see what is generated by a human and what is generated by AI. But you can test it. Studies have found that AI-generated code tends to exhibit a higher frequency of certain types of defects. So, reviewers and testers know what logic flaws and security weaknesses to look for. This would not be the case if it were nondisprovable.
You can study this from datasets where the source of the code is known. You can use open-source pull requests identified as AI-assisted versus those written without such tools. You then evaluate both groups using the same industry-standard analysis tools: static analyzers, complexity metrics, security scanners, and defect classification systems. These tools flag bugs, vulnerabilities, performance issues, and maintainability concerns. They do so in a consistent way across samples.
A widely cited analysis of 470 real pull requests reported that AI-generated contributions contained roughly 1.7 times as many issues on average as human-written ones. The difference included a higher number of critical and major defects. It also included more logic and security-related problems. Because these findings rely on standard measurement tools — counting defects, grading severity, and comparing issue rates — the results are grounded in observable data. Again, I am making a point here. It’s testable and therefore disproveable.
This is a good paper that goes into it:
In this paper, we present a large-scale comparison of code authored by human developers and three state-of-the-art LLMs, i.e., ChatGPT, DeepSeek-Coder, and Qwen-Coder, on multiple dimensions of software quality: code defects, security vulnerabilities, and structural complexity. Our evaluation spans over 500k code samples in two widely used languages, Python and Java, classifying defects via Orthogonal Defect Classification and security vulnerabilities using the Common Weakness Enumeration. We find that AI-generated code is generally simpler and more repetitive, yet more prone to unused constructs and hardcoded debugging, while human-written code exhibits greater structural complexity and a higher concentration of maintainability issues. Notably, AI-generated code also contains more high-risk security vulnerabilities. These findings highlight the distinct defect profiles of AI- and human-authored code and underscore the need for specialized quality assurance practices in AI-assisted programming.
https://arxiv.org/abs/2508.21634
Something I’ve started to notice about a lot of the content on social media platforms is that most of the posts people are liking, sharing, and memetically mutating—and then spreading virally—usually don’t include any citations, sources, or receipts. It’s often just some out-of-context screenshot with no reference link or actual sources.
A lot of the anti-AI content is not genuine critique. It’s often misinformation, but people who hate AI don’t question it or ask for sources because it aligns with their biases. The propaganda on social media has gotten so bad that anything other than heavily curated and vetted feeds is pretty much useless, and it’s filled with all sorts of memetic contagions with nasty hooks that are optimized for you algorithmically. I am at the point where I will disregard anything that is not followed up with a source. Period. It is all optimized to persuade, coerce, or piss you off. I am only writing about this because this I’m actually able to contribute genuine information about the topic.
That they said symbolic propositions written by AI agents (i.e., code) are non-disprovable because they were written by AI boggles my mind. It’s like saying that an article written in English by AI is not English because AI generated it. It might be a bad piece of text, but it’s syntactically, semantically, and grammatically English.
Basically, any string of data can be represented in a base-2 system, where it can be interpreted as bits (0s and 1s). Those bits can be used as the basis for symbolic reasoning. In formal propositional logic, a proposition is a sequence of symbols constructed according to strict syntax rules (atomic variables plus logical connectives). Under a given semantics, it is assigned exactly one truth value (true or false) in a two-valued logic system.
They are essentially saying that code written by AI is not binary, isn’t symbolically logical at all, and cannot be evaluated as true or false by implying it is nondisproveable. At the lowest level, compiled code consists of binary machine instructions that a processor executes. At higher levels, source code is written in symbolic syntax that humans and tools use to express logic and structure. You can also translate parts of code into formal logic expressions. For example, conditions and assertions in a program can be modeled as Boolean formulas. Tools like SAT/SMT solvers or symbolic execution engines check those formulas for satisfiability or correctness. It blows my mind how confidently people talk about things they do not understand.
Furthermore that they don’t realize the projection is wild to me.
@davidgerard wrote:
“But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.”
They are presenting a story—i.e., saying that the studies are not disprovable—and accusing computer scientists of using anecdotal evidence without actually providing evidence to support this, while expecting people to take it prima facie. You’re doing what you are accusing others of doing.
It comes down to this: they feel that people ought not to use AI, so they are tacitly committed to a future in which people do not use AI. For example, a major argument against AI is the damage it is doing to resources, which is driving up the prices of computer components, as well as the ecological harm it causes. They feel justified in lying and misinforming others if it achieves the outcome they want—people not using AI because it is bad for the environment. That is a very strong point, but most people don’t care about that, which is why they lie about things people would care about.It’s corrupt. And what’s really scary is that people don’t recognize when they are part of corruption or a corrupt conspiracy to misinform. Well, they recognize it when they see the other side doing it, that is. No one is more dangerous than people who feel righteous in what they are doing.
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I saw this on Mastodon and almost had a stroke.
@davidgerard wrote:
“Most of the AI coding claims are conveniently nondisprovable. What studies there are show it not helping coding at all, or making it worse
But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.
These guys had one good experience with the bot, they got one-shotted, and now if you say “perhaps the bot is not all that” they act like you’re trying to take their cocaine away.”
First, the term is falsifiable, and proving propositions about algorithms (i.e., code) is part of what I do for a living. Mathematically human-written code and AI-written code can be tested, which means you can falsify propositions about them. You would test them the same way.
There is no intrinsic mathematical distinction between code written by a person and code produced by an AI system. In both cases, the result is a formal program made of logic and structure. In principle, the same testing techniques can be applied to each. If it were really nondisprovable, you could not test to see what is generated by a human and what is generated by AI. But you can test it. Studies have found that AI-generated code tends to exhibit a higher frequency of certain types of defects. So, reviewers and testers know what logic flaws and security weaknesses to look for. This would not be the case if it were nondisprovable.
You can study this from datasets where the source of the code is known. You can use open-source pull requests identified as AI-assisted versus those written without such tools. You then evaluate both groups using the same industry-standard analysis tools: static analyzers, complexity metrics, security scanners, and defect classification systems. These tools flag bugs, vulnerabilities, performance issues, and maintainability concerns. They do so in a consistent way across samples.
A widely cited analysis of 470 real pull requests reported that AI-generated contributions contained roughly 1.7 times as many issues on average as human-written ones. The difference included a higher number of critical and major defects. It also included more logic and security-related problems. Because these findings rely on standard measurement tools — counting defects, grading severity, and comparing issue rates — the results are grounded in observable data. Again, I am making a point here. It’s testable and therefore disproveable.
This is a good paper that goes into it:
In this paper, we present a large-scale comparison of code authored by human developers and three state-of-the-art LLMs, i.e., ChatGPT, DeepSeek-Coder, and Qwen-Coder, on multiple dimensions of software quality: code defects, security vulnerabilities, and structural complexity. Our evaluation spans over 500k code samples in two widely used languages, Python and Java, classifying defects via Orthogonal Defect Classification and security vulnerabilities using the Common Weakness Enumeration. We find that AI-generated code is generally simpler and more repetitive, yet more prone to unused constructs and hardcoded debugging, while human-written code exhibits greater structural complexity and a higher concentration of maintainability issues. Notably, AI-generated code also contains more high-risk security vulnerabilities. These findings highlight the distinct defect profiles of AI- and human-authored code and underscore the need for specialized quality assurance practices in AI-assisted programming.
https://arxiv.org/abs/2508.21634
The big problem in discussions about AI in programming is the either-or thinking, when it’s not about using it everywhere or banning it entirely. Tools like AI have specific strengths and weaknesses. Saying ‘never’ or ‘always’ oversimplifies the issue and turns the narrative into propaganda that creates moral panic or shills AI. It’s a bit like saying you shouldn’t use a hammer just because it’s not good for brushing your teeth.
AI tends to produce code that’s simple, often a bit repetitive, and very verbose. It’s usually pretty easy to read and tweak. This helps with long-term maintenance. But AI doesn’t reason about code the way an experienced developer does. It makes mistakes that a human wouldn’t, potentially introducing security flaws. That doesn’t mean we shouldn’t use for where it works well, which is not everywhere.
AI works well for certain tasks, especially when the scope is narrow and the risk is low. Examples include generating boilerplate code, internal utilities, or prototypes. In these cases, the tradeoff is manageable. However, it’s not suitable for critical code like kernels, operating systems, compilers, or cryptographic libraries. A small mistake memory safety or privilege separation can lead to major failures. Problems with synchronization, pointer management, or access control can cause major problems, too.
Other areas where AI should not be used include memory allocation handling, scheduling, process isolation, or device drivers. A lot of that depends on implicit assumptions in the system’s architecture. Generative models don’t grasp these nuances. Instead of carefully considering the design, AI tends to replicate code patterns that seem statistically likely, doing so without understanding the purpose behind them.
Yes, I’m aware that Microsoft is using AI to write code everywhere I said it should not be used. That is the problem. However, political pundits, lobbyists, and anti-tech talking heads are discussing something they have no understanding of and aren’t specifying what the problem actually is. This means they can’t possibly lead grassroots initiatives into actual laws that specify where AI should not be used, which is why we have this weird astroturfing bullshit.
They’re taking advantage of the reaction to Microsoft using AI-generated code where it shouldn’t be used to argue that AI shouldn’t be used anywhere at all in any generative context. AI is useful for tasks like writing documentation, generating tests, suggesting code improvements, or brainstorming alternative approaches. These ideas should then be thoroughly vetted by human developers.
Something I’ve started to notice about a lot of the content on social media platforms is that most of the posts people are liking, sharing, and memetically mutating—and then spreading virally—usually don’t include any citations, sources, or receipts. It’s often just some out-of-context screenshot with no reference link or actual sources.
A lot of the anti-AI content is not genuine critique. It’s often misinformation, but people who hate AI don’t question it or ask for sources because it aligns with their biases. The propaganda on social media has gotten so bad that anything other than heavily curated and vetted feeds is pretty much useless, and it’s filled with all sorts of memetic contagions with nasty hooks that are optimized for you algorithmically. I am at the point where I will disregard anything that is not followed up with a source. Period. It is all optimized to persuade, coerce, or piss you off. I am only writing about this because this I’m actually able to contribute genuine information about the topic.
That they said symbolic propositions written by AI agents (i.e., code) are non-disprovable because they were written by AI boggles my mind. It’s like saying that an article written in English by AI is not English because AI generated it. It might be a bad piece of text, but it’s syntactically, semantically, and grammatically English.
Basically, any string of data can be represented in a base-2 system, where it can be interpreted as bits (0s and 1s). Those bits can be used as the basis for symbolic reasoning. In formal propositional logic, a proposition is a sequence of symbols constructed according to strict syntax rules (atomic variables plus logical connectives). Under a given semantics, it is assigned exactly one truth value (true or false) in a two-valued logic system.
They are essentially saying that code written by AI is not binary, isn’t symbolically logical at all, and cannot be evaluated as true or false by implying it is nondisproveable. At the lowest level, compiled code consists of binary machine instructions that a processor executes. At higher levels, source code is written in symbolic syntax that humans and tools use to express logic and structure. You can also translate parts of code into formal logic expressions. For example, conditions and assertions in a program can be modeled as Boolean formulas. Tools like SAT/SMT solvers or symbolic execution engines check those formulas for satisfiability or correctness. It blows my mind how confidently people talk about things they do not understand.
Furthermore that they don’t realize the projection is wild to me.
@davidgerard wrote:
“But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.”
They are presenting a story—i.e., saying that the studies are not disprovable—and accusing computer scientists of using anecdotal evidence without actually providing evidence to support this, while expecting people to take it prima facie. You’re doing what you are accusing others of doing.
It comes down to this: they feel that people ought not to use AI, so they are tacitly committed to a future in which people do not use AI. For example, a major argument against AI is the damage it is doing to resources, which is driving up the prices of computer components, as well as the ecological harm it causes. They feel justified in lying and misinforming others if it achieves the outcome they want—people not using AI because it is bad for the environment. That is a very strong point, but most people don’t care about that, which is why they lie about things people would care about.
It’s corrupt. And what’s really scary is that people don’t recognize when they are part of corruption or a corrupt conspiracy to misinform. Well, they recognize it when they see the other side doing it, that is. No one is more dangerous than people who feel righteous in what they are doing.
It’s wild to me that the idea that if you cannot persuade someone, it is okay to bully, coerce, harass them, or spread misinformation to get what you want—because your side is right—has become so normalized on the Internet that people can’t see why it is problematic.
That people think it is okay to hurt others to get them to agree is the most disturbing part of all of this. People have become so hateful. That is a large reason why I don’t interact with people on social media, really consume things from social media, or respond on social media and am writing a blog post about it instead of engaging with who prompted it.
-
I saw this on Mastodon and almost had a stroke.
@davidgerard wrote:
“Most of the AI coding claims are conveniently nondisprovable. What studies there are show it not helping coding at all, or making it worse
But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.
These guys had one good experience with the bot, they got one-shotted, and now if you say “perhaps the bot is not all that” they act like you’re trying to take their cocaine away.”
First, the term is falsifiable, and proving propositions about algorithms (i.e., code) is part of what I do for a living. Mathematically human-written code and AI-written code can be tested, which means you can falsify propositions about them. You would test them the same way.
There is no intrinsic mathematical distinction between code written by a person and code produced by an AI system. In both cases, the result is a formal program made of logic and structure. In principle, the same testing techniques can be applied to each. If it were really nondisprovable, you could not test to see what is generated by a human and what is generated by AI. But you can test it. Studies have found that AI-generated code tends to exhibit a higher frequency of certain types of defects. So, reviewers and testers know what logic flaws and security weaknesses to look for. This would not be the case if it were nondisprovable.
You can study this from datasets where the source of the code is known. You can use open-source pull requests identified as AI-assisted versus those written without such tools. You then evaluate both groups using the same industry-standard analysis tools: static analyzers, complexity metrics, security scanners, and defect classification systems. These tools flag bugs, vulnerabilities, performance issues, and maintainability concerns. They do so in a consistent way across samples.
A widely cited analysis of 470 real pull requests reported that AI-generated contributions contained roughly 1.7 times as many issues on average as human-written ones. The difference included a higher number of critical and major defects. It also included more logic and security-related problems. Because these findings rely on standard measurement tools — counting defects, grading severity, and comparing issue rates — the results are grounded in observable data. Again, I am making a point here. It’s testable and therefore disproveable.
This is a good paper that goes into it:
In this paper, we present a large-scale comparison of code authored by human developers and three state-of-the-art LLMs, i.e., ChatGPT, DeepSeek-Coder, and Qwen-Coder, on multiple dimensions of software quality: code defects, security vulnerabilities, and structural complexity. Our evaluation spans over 500k code samples in two widely used languages, Python and Java, classifying defects via Orthogonal Defect Classification and security vulnerabilities using the Common Weakness Enumeration. We find that AI-generated code is generally simpler and more repetitive, yet more prone to unused constructs and hardcoded debugging, while human-written code exhibits greater structural complexity and a higher concentration of maintainability issues. Notably, AI-generated code also contains more high-risk security vulnerabilities. These findings highlight the distinct defect profiles of AI- and human-authored code and underscore the need for specialized quality assurance practices in AI-assisted programming.
https://arxiv.org/abs/2508.21634
The big problem in discussions about AI in programming is the either-or thinking, when it’s not about using it everywhere or banning it entirely. Tools like AI have specific strengths and weaknesses. Saying ‘never’ or ‘always’ oversimplifies the issue and turns the narrative into propaganda that creates moral panic or shills AI. It’s a bit like saying you shouldn’t use a hammer just because it’s not good for brushing your teeth.
AI tends to produce code that’s simple, often a bit repetitive, and very verbose. It’s usually pretty easy to read and tweak. This helps with long-term maintenance. But AI doesn’t reason about code the way an experienced developer does. It makes mistakes that a human wouldn’t, potentially introducing security flaws. That doesn’t mean we shouldn’t use for where it works well, which is not everywhere.
AI works well for certain tasks, especially when the scope is narrow and the risk is low. Examples include generating boilerplate code, internal utilities, or prototypes. In these cases, the tradeoff is manageable. However, it’s not suitable for critical code like kernels, operating systems, compilers, or cryptographic libraries. A small mistake memory safety or privilege separation can lead to major failures. Problems with synchronization, pointer management, or access control can cause major problems, too.
Other areas where AI should not be used include memory allocation handling, scheduling, process isolation, or device drivers. A lot of that depends on implicit assumptions in the system’s architecture. Generative models don’t grasp these nuances. Instead of carefully considering the design, AI tends to replicate code patterns that seem statistically likely, doing so without understanding the purpose behind them.
Yes, I’m aware that Microsoft is using AI to write code everywhere I said it should not be used. That is the problem. However, political pundits, lobbyists, and anti-tech talking heads are discussing something they have no understanding of and aren’t specifying what the problem actually is. This means they can’t possibly lead grassroots initiatives into actual laws that specify where AI should not be used, which is why we have this weird astroturfing bullshit.
They’re taking advantage of the reaction to Microsoft using AI-generated code where it shouldn’t be used to argue that AI shouldn’t be used anywhere at all in any generative context. AI is useful for tasks like writing documentation, generating tests, suggesting code improvements, or brainstorming alternative approaches. These ideas should then be thoroughly vetted by human developers.
Something I’ve started to notice about a lot of the content on social media platforms is that most of the posts people are liking, sharing, and memetically mutating—and then spreading virally—usually don’t include any citations, sources, or receipts. It’s often just some out-of-context screenshot with no reference link or actual sources.
A lot of the anti-AI content is not genuine critique. It’s often misinformation, but people who hate AI don’t question it or ask for sources because it aligns with their biases. The propaganda on social media has gotten so bad that anything other than heavily curated and vetted feeds is pretty much useless, and it’s filled with all sorts of memetic contagions with nasty hooks that are optimized for you algorithmically. I am at the point where I will disregard anything that is not followed up with a source. Period. It is all optimized to persuade, coerce, or piss you off. I am only writing about this because this I’m actually able to contribute genuine information about the topic.
That they said symbolic propositions written by AI agents (i.e., code) are non-disprovable because they were written by AI boggles my mind. It’s like saying that an article written in English by AI is not English because AI generated it. It might be a bad piece of text, but it’s syntactically, semantically, and grammatically English.
Basically, any string of data can be represented in a base-2 system, where it can be interpreted as bits (0s and 1s). Those bits can be used as the basis for symbolic reasoning. In formal propositional logic, a proposition is a sequence of symbols constructed according to strict syntax rules (atomic variables plus logical connectives). Under a given semantics, it is assigned exactly one truth value (true or false) in a two-valued logic system.
They are essentially saying that code written by AI is not binary, isn’t symbolically logical at all, and cannot be evaluated as true or false by implying it is nondisproveable. At the lowest level, compiled code consists of binary machine instructions that a processor executes. At higher levels, source code is written in symbolic syntax that humans and tools use to express logic and structure. You can also translate parts of code into formal logic expressions. For example, conditions and assertions in a program can be modeled as Boolean formulas. Tools like SAT/SMT solvers or symbolic execution engines check those formulas for satisfiability or correctness. It blows my mind how confidently people talk about things they do not understand.
Furthermore that they don’t realize the projection is wild to me.
@davidgerard wrote:
“But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.”
They are presenting a story—i.e., saying that the studies are not disprovable—and accusing computer scientists of using anecdotal evidence without actually providing evidence to support this, while expecting people to take it prima facie. You’re doing what you are accusing others of doing.
It comes down to this: they feel that people ought not to use AI, so they are tacitly committed to a future in which people do not use AI. For example, a major argument against AI is the damage it is doing to resources, which is driving up the prices of computer components, as well as the ecological harm it causes. They feel justified in lying and misinforming others if it achieves the outcome they want—people not using AI because it is bad for the environment. That is a very strong point, but most people don’t care about that, which is why they lie about things people would care about.
It’s corrupt. And what’s really scary is that people don’t recognize when they are part of corruption or a corrupt conspiracy to misinform. Well, they recognize it when they see the other side doing it, that is. No one is more dangerous than people who feel righteous in what they are doing.
It’s wild to me that the idea that if you cannot persuade someone, it is okay to bully, coerce, harass them, or spread misinformation to get what you want—because your side is right—has become so normalized on the Internet that people can’t see why it is problematic.
That people think it is okay to hurt others to get them to agree is the most disturbing part of all of this. People have become so hateful. That is a large reason why I don’t interact with people on social media, really consume things from social media, or respond on social media and am writing a blog post about it instead of engaging with who prompted it.
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The Grey Scale of Politics
Too much either/or thinking in public discourse. No wonder we can't find effective solutions.
https://tiereddemocraticgovernance.org/blog_details.php?blog_cat_id=21&id=400
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The #slipperyslope is not a #fallacy.
Proof: [gestures vaguely at everything] #QED.
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Extensibility: The "100% Lisp" Fallacy
https://kyo.iroiro.party/en/posts/100-percent-lisp/
#HackerNews #Extensibility #Lisp #Fallacy #Programming #HackerNews
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Extensibility: The "100% Lisp" Fallacy
https://kyo.iroiro.party/en/posts/100-percent-lisp/
#HackerNews #Extensibility #Lisp #Fallacy #Programming #HackerNews
-
Extensibility: The "100% Lisp" Fallacy
https://kyo.iroiro.party/en/posts/100-percent-lisp/
#HackerNews #Extensibility #Lisp #Fallacy #Programming #HackerNews
-
Extensibility: The "100% Lisp" Fallacy
https://kyo.iroiro.party/en/posts/100-percent-lisp/
#HackerNews #Extensibility #Lisp #Fallacy #Programming #HackerNews
-
Extensibility: The "100% Lisp" Fallacy
https://kyo.iroiro.party/en/posts/100-percent-lisp/
#HackerNews #Extensibility #Lisp #Fallacy #Programming #HackerNews
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I see a lot of people concerned about #inequality. The only "good" reason to be concerned with it is based on a #fallacy; the fixed pie fallacy, or the idea that the amount of #wealth in the world is fixed.
The entire planet is vastly wealthier than it was a hundred or two hundred years ago. The amount of wealth is not fixed. Therefore, it does not follow that one person being #rich means another must be #poor.
Put another way, #poverty for one is not caused by the wealth of another.
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The Grey Scale of Politics
Either/Or thinking also belongs to the political left:
https://tiereddemocraticgovernance.org/blog_details.php?blog_cat_id=21&id=400
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DEBATE PRO TIP: If you start your argument with something like "According to #ChatGPT, …", you have AUTOMATICALLY lost the debate.
This is a #fallacy known as "Argument from authority". Just because a supposed authority says something doesn't make it true.
And ChatGPT is the WORST authority about anything. LLMs are just computer programs that imitate human language, but they do not "think". There's no #magic going on here.
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Does faulty thinking tend to be faster?
People often fell for lures more quickly (than they realized the correct answer) on cognitive reflection and conjunction #fallacy tests, but this reversed in gambler's fallacy tasks: lured responses took longer.
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Did you know that we once believed that cultivating land changes the #climate?
During the 1870s, the #American Southwest & northern South #Australia experienced a small blip in climate. This led to short-lived favourable rainfall patterns.
This was used to justify human habitation that had started in those areas.
As we are prone to do, this phenomenon led us to correlate #human activities with more rainfall.
The #hypothesis was known as "Rain follows the plough".
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🦄 The Micro-Benchmark Fallacy
It's a trap.
by @sindresorhus https://sindresorhus.com/blog/micro-benchmark-fallacy
#benchmark #fallacy -
What about the view that pacifism is a luxury paid for by those doing #violence on your behalf?
I've heard it in that scene from Patton, and cop shows, and gun arguments. And the Upstairs Downstairs reboot.
It's used to justify genocide.
I don't agree, but I can't dismiss it.
Where's the #fallacy?
#politics #war #guns #anarchy -
Ah, the classic "less immigrants, more jobs" #fallacy 🤦♂️. Turns out blocking H-1B visas didn't magically create jobs for locals—shocking! Who knew that complex economies don't adhere to simplistic nativist fantasies? 😂
https://www.nber.org/digest/dec17/fewer-h-1b-visas-did-not-mean-more-employment-natives #lessImmigrantsMoreJobs #H1Bvisas #economicComplexity #nativistMyths #jobMarketReality #HackerNews #ngated -
Ah, the classic "less immigrants, more jobs" #fallacy 🤦♂️. Turns out blocking H-1B visas didn't magically create jobs for locals—shocking! Who knew that complex economies don't adhere to simplistic nativist fantasies? 😂
https://www.nber.org/digest/dec17/fewer-h-1b-visas-did-not-mean-more-employment-natives #lessImmigrantsMoreJobs #H1Bvisas #economicComplexity #nativistMyths #jobMarketReality #HackerNews #ngated -
Ah, the classic "less immigrants, more jobs" #fallacy 🤦♂️. Turns out blocking H-1B visas didn't magically create jobs for locals—shocking! Who knew that complex economies don't adhere to simplistic nativist fantasies? 😂
https://www.nber.org/digest/dec17/fewer-h-1b-visas-did-not-mean-more-employment-natives #lessImmigrantsMoreJobs #H1Bvisas #economicComplexity #nativistMyths #jobMarketReality #HackerNews #ngated -
Ah, the classic "less immigrants, more jobs" #fallacy 🤦♂️. Turns out blocking H-1B visas didn't magically create jobs for locals—shocking! Who knew that complex economies don't adhere to simplistic nativist fantasies? 😂
https://www.nber.org/digest/dec17/fewer-h-1b-visas-did-not-mean-more-employment-natives #lessImmigrantsMoreJobs #H1Bvisas #economicComplexity #nativistMyths #jobMarketReality #HackerNews #ngated
