#rants — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #rants, aggregated by home.social.
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🤦♂️ Oh no, another tech bro's poetic rant about hating #soldering. 🌪️ Apparently, soldering is the end of civilization and the grey smoke is its harbinger. 🙄 Maybe next time, just hire someone who doesn't have an existential crisis over a soldering iron. 🔧✨
https://user8.bearblog.dev/rant/ #techbro #rants #existentialcrisis #innovation #humor #HackerNews #ngated -
Ah, the good ol' days of paper #maps and shirtless staring contests with Kentucky hill folk 😳. Let's write a 14-minute rant about how #cyberlibertarians are hypocritical, because nothing screams relevance like reminiscing about getting lost without GPS 🌐🧭. Ranting about the Internet while using it to share your rants? Priceless irony! 😂
https://matduggan.com/the-intolerable-hypocrisy-of-cyberlibertarianism/ #nostalgia #irony #rants #technology #HackerNews #ngated -
Interesting:
“A Case Against Currying”, Emilia H (https://emi-h.com/articles/a-case-against-currying.html).
Via HN: https://news.ycombinator.com/item?id=47477090
On Lobsters: https://lobste.rs/s/w2x9dq/case_against_currying
#Programming #FunctionalProgramming #FP #Currying #Rants #PLDI #Haskell
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“Why The Heck Are We Still Using Markdown?”, Burak Güngör (https://bgslabs.org/blog/why-are-we-using-markdown/).
Via HN: https://news.ycombinator.com/item?id=47629903
On Lobsters: https://lobste.rs/s/nn403y/why_heck_are_we_still_using_markdown
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Ask Hackaday: Using CoPilot? Are You Entertained?
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Age-Verification and the World Before Social Media
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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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@cloudskater wrote:
Some instances are run by bad people. Hell, a few projects like Lemmy and Matrix are DEVELOPED by assholes, but the FLOSS and federated nature of these platforms allows us to bypass/fork them and create healthy spaces outside their reach.
Nope, that is actually what is killing the fediverse. I just explained here:
The issue is the divergence in semantic interpretation that emerges at the interpretation layer. ActivityPub standardizes message delivery and defines common activity types. However, it leaves extension semantics and application-layer policy decisions to individual implementations. Servers may introduce custom JSON-LD namespaces and enforce local behaviors, such as reply restrictions, while remaining protocol-compliant. But, the noise created by divergences are problematic, because it creates unexpected, unintended, and unpredictable behavior.
Divergence appears when implementations rely on non-normative metadata and assume reciprocal handling to preserve a consistent user experience. Behavioral alignment then varies. Syntactic exchange succeeds, but behavioral consistency is not guaranteed. Though instances continue to federate at the transport level, policy semantics and processing logic differ across deployments. Those differences produce inconsistent experiences and results between implementations.
That leads to fragmentation, specifically semantic or behavioral fragmentation and an inconsistent user experiences. ActivityPub ensures syntactic interoperability, but semantic interoperability (everyone interprets and enforces rules the same way) varies. This creates a system that is federated at the transport level yet fragmented in behavior and expectations across implementations. It is funny how the thing that the fediverse touted has made the entire thing very brittle. ActivityPub technically federates correctly, but semantically falls apart once servers start adding their own behavioral rules.
https://neon-blue-demon-wyrm.x10.network/archives/16932
FYI, I’m not doing culture wars or political debates. I’m just saying this idea of “forking away” from them is literally breaking the fediverse’s distributed network and creating all kinds of issues with semantic interoperability. Yes, federation is still happening at the delivery level, but the semantic issues are out of fucking control. You are a federation by the very sheer skin of your teeth.
The reason why developers are leaving the fediverse is because you folks don’t take criticism. You respond to criticism with — I’m being so serious right now — political manifestos and harassing developers. ActivityPub developers and authors oversold you folks on the capabilities of ActivityStreams. They flat-out lied to y’all.
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ActivityPub Server’s Custom Reply‑Control Extensions Undermine Federation
It seems like Activitbypub developers are extending ActivityPub with optional metadata to fix a lot of its issues, but that is still problematic. Trying to add moderation tools and user control to threads seems to be the ongoing battle. I am fascinated by dumpster fires, so I’ve started looking at the ActivityPub protocol in detail. I tend to become fascinated with things that are going down in flames.
As a brief recap of the problem:
So, one of the very popular features on Bluesky—also popular on Twitter—is the ability to select who can reply to a post. A major issue in the Fediverse is the inability to decide who can reply, and once you block someone, their harassing reply is still there. I honestly thought it was simply a case of them choosing not to add or address it for cultural reasons. What is clear from that thread is that they were always aware that the ActivityPub protocol and most Fediverse implementations don’t provide a universal way to control reply visibility or enforce blocks across instances.
An ActivityPub server that has reply control is GoToSocial. ActivityPub, as defined by the W3C specification, standardizes how servers federate activities. It defines actors, inboxes, outboxes, and activity types (Create, Follow, Like, Announce, etc.) expressed using ActivityStreams 2.0. It also specifies delivery mechanics (including how a Create activity reaches another server’s inbox) and how collections behave.
The specification does not include interaction policy semantics such as “only followers may reply” or “replies require manual approval.” There is no field in the normative vocabulary requiring conforming servers to enforce reply permissions. That category of rule is outside the protocol’s defined contract.
GoToSocial implements reply controls through what it calls interaction policies. These appear as additional properties on ActivityStreams objects using a custom JSON-LD namespace controlled by the GoToSocial project.
JSON-LD permits additional namespaced terms. This means the document remains structurally valid ActivityStreams and federates normally. The meaning of those custom fields, however, comes from GoToSocial’s own documentation and implementation. Other servers can ignore them without violating ActivityPub because they are not part of the interoperable core vocabulary.
Enforcement occurs locally. When a remote server sends a reply—a Create activity whose object references another via inReplyTo—ActivityPub governs delivery, not acceptance criteria. Whether the receiving server checks a reply policy, rejects the activity, queues it, or displays it is determined in the server’s inbox-processing code. The decision to accept, display, or require approval happens after successful protocol-level delivery. This behavior belongs to the application layer.
These are server-side features layered on top of ActivityPub’s transport and data model that are not actually part of ActivityPub. The protocol ensures standardized delivery of activities; however, the server implementation defines additional constraints and user-facing behavior. Two GoToSocial instances may both recognize and act on the same extension fields. However, a different implementation, such as Mastodon, has no obligation under the specification to interpret or enforce GoToSocial’s interactionPolicy properties. These fields function as extension metadata rather than protocol requirements.
The semantics of GoToSocial are not part of the specification’s defined vocabulary and processing rules for ActivityPub. They no longer operate purely at the protocol layer; it has become an application-layer contract implemented by specific servers.
Let’s use the AT Protocol as an example. Bluesky’s direct messages (DMs) are not currently part of the AT Protocol (ATProto). The AT Protocol has nothing that specifies anything for DMs, so DMs are not part of the AT Protocol. The AT Protocol was designed to handle public social interactions, but it does not define private or encrypted messaging. Bluesky implemented DMs at the application level, outside of the core protocol. DMs are centralized and stored on Bluesky’s servers. What is happening with servers like GoToSocial is sort of like that. The difference is that the AT Protocol was designed for different app views; ActivityPub was not.
The issue is the divergence in semantic interpretation that emerges at the interpretation layer. ActivityPub standardizes message delivery and defines common activity types. However, it leaves extension semantics and application-layer policy decisions to individual implementations. Servers may introduce custom JSON-LD namespaces and enforce local behaviors, such as reply restrictions, while remaining protocol-compliant. But, the noise created by divergences are problematic, because it creates unexpected, unintended, and unpredictable behavior.
Divergence appears when implementations rely on non-normative metadata and assume reciprocal handling to preserve a consistent user experience. Behavioral alignment then varies. Syntactic exchange succeeds, but behavioral consistency is not guaranteed. Though instances continue to federate at the transport level, policy semantics and processing logic differ across deployments. Those differences produce inconsistent experiences and results between implementations.
That leads to fragmentation, specifically semantic or behavioral fragmentation and an inconsistent user experiences. ActivityPub ensures syntactic interoperability, but semantic interoperability (everyone interprets and enforces rules the same way) varies. This creates a system that is federated at the transport level yet fragmented in behavior and expectations across implementations. It is funny how the thing that the fediverse touted has made the entire thing very brittle. ActivityPub technically federates correctly, but semantically falls apart once servers start adding their own behavioral rules.
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The Death of Baseload and Similar Grid Tropes
https://web.brid.gy/r/https://hackaday.com/2026/02/12/the-death-of-baseload-and-similar-grid-tropes/
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So, I am a Computational Biologist. Keep that in mind. I’m an actual scientist who works with ecological concepts, specifically the microbiome. One of the most insufferable reactions to the cyberpunk era we inhabit is the emergence of anti-science ideas from the left in response to techno-fascism. The strange part is that many people on the left do not even recognize them as anti-science, because they assume the left is aligned with science and the right opposed to it; ergo, if the left says it, it must be scientific. It is insane: washing your hands is technology. Medicine is technology.
I think, because the Internet has hijacked people’s brains, many conflate technology with electronics or machines. Anthropologically, technology consists of material objects, techniques, and organized practices through which humans intentionally intervene in their environments. Technology is culture, and human culture is technology. When someone learns a skill or a discipline from someone else, that is an extension of technology.
Technology encompasses craft traditions (blacksmithing), agriculture, and institutionalized processes of teaching and learning. Agriculture is one of the oldest forms of technology. Yes, farmers are tech workers. I write code, but I also spent a large amount of time on a farm, and I can tell you that many tech workers who pride themselves on writing code would not know what to do with farm equipment.
So, from that broad perspective, we can sum technology up in one word: education. A basic heuristic for determining if something is cultural or not is: can it be taught and learned? These words? I was taught English, and I am using an invented language to transmit knowledge to you; ergo, I am using technology to transmit cultural knowledge to you. Reading a book is thus using a piece of human technology. So, being anti-tech connotes being anti-education.
What got me thinking about this is a toot I read on Mastodon:
The truth is that society needs to develop ethically and ecologically more than it does technologically. That’s not to say that we should shun technology, but our development along other lines lags far behind our technological capacity.
Sounds valid, right? That is the distinct smell of bull shit.This is a clear example of what is called a platitude. Platitudes are memetically hijacking people’s brains. Memetics actually hijack your brain—they change it. It’s similar to how a retrovirus can alter the genome of its host. So, trying to have conversations with these people is pointless, which is why I avoid the chronically online Internet scene and arguing with them.
It made me want to scream. As I mentioned earlier, technology is basically a set of things you learn from other humans—typically within a culture—that helps you do or make something. You know what else is learned within human society? A normative set of cultural values about how we ought to behave. So, both technology and culture emerge from the same thing simultaneously and mutually. You cannot have humans intervening in things to achieve ecological development, because that is technology, and you cannot educate humans on ethics without an invented language. It is literally an anti-education argument.
Ethics and technology arise together from the same human conditions and social processes. It makes little sense to claim that technology is “outpacing” ethics. The two do not develop independently. We form ethical norms in response to new capacities and circumstances. There would be no cultural norms about how to use the Internet if the Internet did not exist. And, there would be no ethical debates about AI if AI did not exist. Ethical reflection emerges alongside technological change because both are products of human culture.
As new problems create new technologies that create new problems, societies respond by negotiating norms, rules, and expectations appropriate to those contexts. The same pattern appears in politics. Politics concerns who gets what, when, and how—it is the negotiation of power, rights, and resources. Without resources or competing claims, there would be nothing to negotiate. Ethics and politics are not trailing behind technology because they are co-emergent responses to the same underlying realities.
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Stepping Back From Social Media To Read a Book
I’m taking a break. After spending like two years in the worst parts of the Internet modeling the memetic spread of conspiracy-driven behavioral patterns and developing social media software as a side hustle, I think I’m going to take a step back and… I don’t know… maybe read a book? lol.
I’m a Computational Biologist who pretty much studies the memetics of conspiracy theories and how they act as another vector/epidemiological layer. I’ve also been working on various contracts for social media development stuff. Working on the shit I’ve been working on for years forces you to see the worst parts of people that they split off. It makes you hate everyone — and I mean everyone.
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BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity
I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”
Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.
Bluesky mentions that here:
https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update
As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.
I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.
Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.
Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.
Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.
Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.
Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.
Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.
I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.
In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.
I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.
This can be modeled by representing each item as a node in a weighted graph, where edges are weighted by dissimilarity rather than similarity. Highly similar items are connected by low-weight edges, while less similar items are connected by higher-weight edges. Distances in the graph, computed using standard shortest-path algorithms, then correspond to degrees of similarity. Closely related items are separated by short path lengths, while increasingly dissimilar items require longer paths through the graph. It turns out that attempts to generalize metric spaces for noetic domains—to model noetic/psychic spaces—are actually pretty useful for social media algorithms, lol.
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BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity
I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”
Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.
Bluesky mentions that here:
https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update
As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.
I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.
Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.
Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.
Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.
Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.
Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.
Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.
I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.
In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.
I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.
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BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity
I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”
Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.
Bluesky mentions that here:
https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update
As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.
I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.
Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.
Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.
Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.
Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.
Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.
Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.
I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.
In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.
I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.
This can be modeled by representing each item as a node in a weighted graph, where edges are weighted by dissimilarity rather than similarity. Highly similar items are connected by low-weight edges, while less similar items are connected by higher-weight edges. Distances in the graph, computed using standard shortest-path algorithms, then correspond to degrees of similarity. Closely related items are separated by short path lengths, while increasingly dissimilar items require longer paths through the graph. It turns out that attempts to generalize metric spaces for noetic domains—to model noetic/psychic spaces—are actually pretty useful for social media algorithms, lol.
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BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity
I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”
Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.
Bluesky mentions that here:
https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update
As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.
I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.
Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.
Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.
Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.
Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.
Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.
Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.
I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.
In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.
I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.
This can be modeled by representing each item as a node in a weighted graph, where edges are weighted by dissimilarity rather than similarity. Highly similar items are connected by low-weight edges, while less similar items are connected by higher-weight edges. Distances in the graph, computed using standard shortest-path algorithms, then correspond to degrees of similarity. Closely related items are separated by short path lengths, while increasingly dissimilar items require longer paths through the graph. It turns out that attempts to generalize metric spaces for noetic domains—to model noetic/psychic spaces—are actually pretty useful for social media algorithms, lol.
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Easy For The Masses - Last week, we were talking about how glad we are to be the type who by-and-large u... - https://hackaday.com/2025/10/11/easy-for-the-masses/ #lowestcommondenominator #hackadaycolumns #androidhacks #sideloading #newsletter #opensource #android #rants
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“Package Managers Are Evil”, Bill “GingerBill” Hall (https://www.gingerbill.org/article/2025/09/08/package-managers-are-evil/).
On HN: https://news.ycombinator.com/item?id=45167394
On Lobsters: https://lobste.rs/s/zvdtdn/package_managers_are_evil
#Programming #Packages #Dependencies #DependencyHell #PackageManagers #Rants #DependencyManagement
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“Package Managers Are Evil”, Bill “GingerBill” Hall (https://www.gingerbill.org/article/2025/09/08/package-managers-are-evil/).
On HN: https://news.ycombinator.com/item?id=45167394
On Lobsters: https://lobste.rs/s/zvdtdn/package_managers_are_evil
#Programming #Packages #Dependencies #DependencyHell #PackageManagers #Rants #DependencyManagement
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“Package Managers Are Evil”, Bill “GingerBill” Hall (https://www.gingerbill.org/article/2025/09/08/package-managers-are-evil/).
On HN: https://news.ycombinator.com/item?id=45167394
On Lobsters: https://lobste.rs/s/zvdtdn/package_managers_are_evil
#Programming #Packages #Dependencies #DependencyHell #PackageManagers #Rants #DependencyManagement
-
“Package Managers Are Evil”, Bill “GingerBill” Hall (https://www.gingerbill.org/article/2025/09/08/package-managers-are-evil/).
On HN: https://news.ycombinator.com/item?id=45167394
On Lobsters: https://lobste.rs/s/zvdtdn/package_managers_are_evil
#Programming #Packages #Dependencies #DependencyHell #PackageManagers #Rants #DependencyManagement
-
“Package Managers Are Evil”, Bill “GingerBill” Hall (https://www.gingerbill.org/article/2025/09/08/package-managers-are-evil/).
On HN: https://news.ycombinator.com/item?id=45167394
On Lobsters: https://lobste.rs/s/zvdtdn/package_managers_are_evil
#Programming #Packages #Dependencies #DependencyHell #PackageManagers #Rants #DependencyManagement
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Ask Hackaday: How Do You Distro Hop? - If you read “Jenny’s Daily Drivers” or “Linux Fu” here on Hackaday, you know we li... - https://hackaday.com/2025/09/29/ask-hackaday-how-do-you-distro-hop/ #hackadaycolumns #distrohopping #askhackaday #linuxhacks #opensuse #rants #linux #kde
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Whither the Chip Shortage? - Do you remember the global chip shortage? Somehow it seems so long ago, but it’s n... - https://hackaday.com/2025/09/27/whither-the-chip-shortage/ #howquicklyweforget #hackadaycolumns #chipshortage #newsletter #parts #rants
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Knowing That It Is Possible - We like to think that we can do almost anything. Give me a broken piece of consume... - https://hackaday.com/2025/09/06/knowing-that-it-is-possible/ #cellphonehacks #newsletter #smartphone #cellphone #rants
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Measurement is Science - I was watching Ben Krasnow making iron nitride permanent magnets and was struck by... - https://hackaday.com/2025/06/21/measurement-is-science/ #hackadaycolumns #quantification #measurement #science #magnets #rants
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Open Source Hardware, How Open Do You Want It To Be? - In our wider community we are all familiar with the idea of open source software. ... - https://hackaday.com/2025/03/07/open-source-hardware-how-open-do-you-want-it-to-be/ #opensourceharware #opensource #featured #hardware #interest #rants #ohl
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Open Source Hardware, How Open Do You Want It To Be? - In our wider community we are all familiar with the idea of open source software. ... - https://hackaday.com/2025/03/07/open-source-hardware-how-open-do-you-want-it-to-be/ #opensourceharware #opensource #featured #hardware #interest #rants #ohl
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Open Source Hardware, How Open Do You Want It To Be? https://hackaday.com/2025/03/07/open-source-hardware-how-open-do-you-want-it-to-be/ #opensourceharware #opensource #Featured #hardware #Interest #Rants #OHL
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Living right next to #downtown #losangeles in the #koreatown area has some interesting characteristics.
Like a natural habitat of sirens and shouting.
I'll be shouting soon too everyone here is absolutely insane, and quite frankly so am I.
So eh.
At least there are a lot of pretty palm trees, and it's always sunny to see the trash, and about like 65 out in January.
How the hell did I get here....
#rants #politics #clapper #videos #uspol #USPolitics #homelessness
https://clapperapp.com/video/x9jVzMyZPJYzqeQD?is_invite=1&r=DJq9G2GpJa&c=sh&m=co
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#ConvictedFelon #DonaldTrump Erupts in #Incoherent #Rants After New #Court #Brief #Details How He ‘Resorted to #Crimes’ to #Defy 2020 #Election.
The new court filing comes the day after #runningmate #ShadyVance #refused to #admit #Trump #lost the 2020 #election when asked by TimWalz during the #VPdebate
https://www.thewrap.com/trump-incoherent-rants-jack-smith-filing-jan-6/
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Thanks for the Great Comments! - Every once in a while, there’s a Hackaday article where the comments are hands-dow... - https://hackaday.com/2024/06/22/thanks-for-the-great-comments/ #hackadaycolumns #enclosures #newsletter #comments #rants
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#NFL #player #rants about #PrideMonth #celebrating “deadly #sins” in #graduation #speech as #audience #groans.
The #KansasCityChiefs #kicker and #SuperBowl #champ used the #speech to advocate for #ChristianNationalist #beliefs.
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#CNN #KFile #Reveals #Shocking #Tapes of New #GOP #Speaker #MikeJohnson’s #AntiLGBTQ #Gay #ConversionTherapy #Rants.
On Wed. night’s edition of #CNN’s #OutFront, #Kaczynski joined #anchor #ErinBurnett to reveal #audiotapes he dug up from Johnson’s time as an adviser to #ExodusInternational.
#Women #Transgender #LGBTQ #LGBTQIA #Conservatives #Extremism #Fascism #RepublicanParty #Hate #Bigotry #Violence #Genocide #Discrimination #Homophobia #Transphobia #ThePartyOfHate
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When you #Scroll the #Feed and #Threads remember what you #Read and #See is a form of #Consuming as in #Eating - Make sure you are eating more than just the #Reporting of the #Garbage of #World #News and #Rants
#Awareness of your #Diet #Matters
Take #Time to #Breathe and take in #Goodness amongst all the #Clamor
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Will the Fax Machine Ever Stop Singing? - Throughout the 80s and 90s, you couldn’t swing a stapler around any size office wi... - https://hackaday.com/2022/11/16/will-the-fax-machine-ever-stop-singing/ #universalservice #hackadaycolumns #iptelephony #phonehacks #faxmachine #facsimile #rants #ofcom #fax
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Will the Fax Machine Ever Stop Singing? - Throughout the 80s and 90s, you couldn’t swing a stapler around any size office wi... - https://hackaday.com/2022/11/16/will-the-fax-machine-ever-stop-singing/ #universalservice #hackadaycolumns #iptelephony #phonehacks #faxmachine #facsimile #rants #ofcom #fax
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Will the Fax Machine Ever Stop Singing? - Throughout the 80s and 90s, you couldn’t swing a stapler around any size office wi... - https://hackaday.com/2022/11/16/will-the-fax-machine-ever-stop-singing/ #universalservice #hackadaycolumns #iptelephony #phonehacks #faxmachine #facsimile #rants #ofcom #fax
