Alternatively, esoteric languages and frameworks will become even more lucrative ,simply because only the person who invented them and their hardcore following will understand half of it.

Obviously, not a given, but not unreasonable given what we have seen historically.

Well Gemini completely hallucinated command line switches on a recent question I asked it about the program “john the ripper”.

We absolutely need public sources of truth at the very least until we can build systems that actually reason based on a combination of first principles and experience, and even then we need sources of truth for experience.

You simply cannot create solutions to new problems if your data gets too old to encompass the new subject matter. We have so systems which can adequately determine fact from fiction, and new human experiences will always need to be documented for machines to understand them.

Experienced the same thing with a library that has no documentation and takes advantage of c++23(latest) features.

Naively asking it for code for anything remotely complex is foolish, but if you do know what you're doing and understand how to manage context, it's a ridiculously potent force multiplier. I rarely ask it for anything without specifying which libraries I want to use, and if I'm not sure which library I want, I'll ask it about options and review before proceeding.

With GPT 4o I had some success pointing it to the current documentation of projects I needed and had it giving me current and actual answers.

Like "Help me to do this and that and use this list of internet resources to answer my questions"

That is only true for trivial questions.

I've answered dozens of questions on stackoverflow.com with tags like SIMD, SSE, AVX, NEON. Only a minority of these asked for a single SIMD instruction which does something specific. Usually people ask how to use the complete instruction set to accomplish something higher level.

Documentation alone doesn't answer questions like that, you need an expert who actually used that stuff.

Published documentation has been and can be wrong. In the late 1990's and early 2000's when I still did a mix of Microsoft technologies and Java, I found several bad non-obvious errors in MSDN documentation. AI today would likely regurgitate it in a soft but seemingly mild but arguably authoritative sounding way. At least when discussing with real people after the arrows fly and the dust settles, we can figure out the truth.

Yeah, this is wildly optimistic.

From personal experience, I'm skeptical of the quantity and especially quality of published documentation available, the completeness of that documentation, the degree to which it both recognizes and covers all the relevant edge cases, etc. Even Apple, which used to be quite good at that kind of thing, has increasingly effectively referred developers to their WWDC videos. I'm also skeptical of the ability of the LLMs to ingest and properly synthesize that documentation - I'm willing to bet the answers from SO and Reddit are doing more heavy lifting on shaping the LLM's "answers" than you're hoping here.

There is nothing in my couple decades of programming or experience with LLMs that suggests to me that published documentation is going to be sufficient to let an LLM produce sufficient quality output without human synthesis somehwere in the loop.

Knowledge gained from experience that is not included in documentation is also significant part of SO. For example "This library will not work with service Y because of X, they do not support feature Y, as I discovered when I tried to use it myself" or other empirical evidence about the behavior of software that isn't documented.

Following the article’s conclusion farther, humans would stop producing new documentation with new concepts.

Curious about this statement, do you mind expanding?

> In theory, just providing the grammar for a new programming language should be enough for a sufficiently smart LLM to answer problems in that language.

I doubt it. Take a language like Rust or Haskell or even modern Java or Python. Without prolonged experience with the language, you have no idea how the various features interact in practice, what the best practices and typical pitfalls are, what common patterns and habits have been established by its practitioners, and so on. At best, the system would have to simulate building a number of nontrivial systems using the language in order to discover that knowledge, and in the end it would still be like someone locked in a room without knowledge of how the language is actually applied in the real world.

> sufficiently smart LLM

Cousin of the sufficiently smart compiler? :-p

Synthetic data can never contain more information than the statistical model from which it is derived: it is simply the evaluation of a non-deterministic function on the model parameters. And the model parameters are simply a function of the training data.

I don't see how you can "bootstrap a smarter model" based on synthetic data from a previous-gen model this way. You may as well well just train your new model on the original training data.

Synthetic data without some kind of external validation is garbage.

E.g. you can't just synthetically generate code, something or someone needs to run it and see if it performs the functions you actually asked of it.

You need to feed the LLM output into some kind of formal verification system, and only then add it back to the synthetic training dataset.

Here, for example - dumb recursive training causes model collapse:

https://www.nature.com/articles/s41586-024-07566-y

Yeah, There was a reference in a paywalled article a year ago (https://www.theinformation.com/articles/openai-made-an-ai-br...): "Sutskever's breakthrough allowed OpenAI to overcome limitations on obtaining high-quality data to train new models, according to the person with knowledge, a major obstacle for developing next-generation models. The research involved using computer-generated, rather than real-world, data like text or images pulled from the internet to train new models."

I suspect most foundational models are now knowingly trained on at least some synthetic data.

This argument always conflates simple processes with complex ones. Humans can work with abstract concepts at a level LLMs currently can’t and don’t seem likely capable of. “True” and “False” are the best examples.

> AI companies are already paying humans to produce new data to train on and will continue to do that.

The sheer difference in scale between the domain of “here are all the people in the world that have shared data publicly until now” and “here is the relatively tiny population of people being paid to add new information to an LLM” dooms the LLM to become outdated in an information hoarding society. So, the question in my mind is, “Why will people keep producing public information just for it to be devalued into LLMs?”

How would a custom language differ from what we have now?

If you mean obfuscation, then yeah, maybe that makes sense to fit more into the window. But it’s easy to unobfuscate, usually.

Otherwise, I‘m not sure what the goal of an LLM specific language could be. Because I don’t feel most languages have been made purely to accommodate humans anyway, but they balance a lot of factors, like being true to the metal (like C) or functional purity (Haskell) or fault tolerance (Erlang). I‘m not sure what „being for LLMs“ could look like.

The OP has qualified "code" with bad availability of samples online. My experience with LLMs on a proprietary language with little online presence confirms their statement. It is not even worth trying, in many cases.

Yes, it's sad. On the other hand, I think it's a good thing that people share knowledge less, publicly and free of charge on the web, because there is so much exploitation going on. Big corporations obviously capitalize on the good will of people with their LLMs, but there are also others who take advantage of the ones who want to help. A lot of users seemingly expect others to solve their problems for free and don't even put any effort into asking their questions. It's a massive drain for energy and enthusiasm, some even suffer from burnout (I assume more in open-source projects than on SO but still). I rather want it to be harder to connect with people sharing the same passion "in private" than having outsider who don't contribute anything profit off of activities happening in the open. This frustratingly appears to become the main reason for corporate open source these days.

Both of those platforms are making answers harder to find. For me, a person used to getting the correct answer in Stackoverflow right away, scrolling through endless GitHub discussions is a nightmare. Aren't we just moving backwards?

No doubt that discord has struck a good balance. Much better than GitHub imo. Both for maintainers to get a soft understanding of their users, and equally beneficial for users who can interact casually without being shamed for filing an issue the wrong way.

There’s some weird blind spot with techies who are unable to see the appeal. UX matters in a “the medium is the message”-kind of way. Also, GitHub is only marginally more open than discord. It’s indexable at the moment, yes, but would not surprise me at all if MS is gonna make an offensive move to protect “their” (read our) data from AI competitors.

It's more that Discord is replacing Slack as the place where community happens. Less about about indexing, which still sucks even in Discord search. Slack/Salesforce threw a lot of small projects under the bus, post-acquisition, with the reductions to history from message count to 90 days

Discord stores trillions of messages. If they haven’t figured out how to make a slop generator out of it yet, I’m sure it’s coming soon.

> You come to SO for answers, why would you care that someone is teaching some LLM on them later?

This doesn't make the slightest bit of sense. The people who would be concerned are the ones who are providing answers. They are not coming to SO solely to get answers.

I mean part of the reason to not ask about stuff on SO, there are several types of questions that one might like to ask - such as:

I don't know the first thing about this thing, help me get to where I know the first thing. This is not allowed any more.

I want to know the pros and cons of various things compared. this is not allowed.

I have quality questions regarding an approach that I know how to do, but I want to know better ways. This is generally not allowed but you might slip through if you ask it just right.

I pretty much know really well what I'm doing but having some difficulty finding the right documentation on some little thing,help me - this is allowe

Something does not work as per the documentation, help me, this is allowed

I think I have done everything right but it is not working, this is allowed and is generally a typo or something that you have put in the wrong order because you're tired.

At any rate, the ones that are not allowed are the only questions that are worth asking.

The last two that is allowed I generally find gets answered in the asking - I'm pretty good in the field I'm asking in, the rigor of making something match SO question requirements leads me to the answer.

If I ask one of the interesting disallowed questions and get shit on then I will probably go through a period of screw it, I will just look extra hard for the documentation before I bother with that site again.

What you're describing is "open slop", and yes, there will be a lot of it.

Open source as we know it today, not so much.

LLM users are feeding their entropy into the model, and paying for the privilege.

These LLM users produce the new training data. They are being assimilated into the tool.

This is the future of "open source": Anonymous common property continuously harvested from, and distributed to, LLM users.

<< Why would those things go away?

I think that is because, overall, the human nature does not change that much.

<< Open source is no different, here. Or are you suggesting that non-open-source copyrighted material (code or otherwise) is protected by keeping the "source" (or equivalent) secret? Good luck making money on that blockbuster movie if you don't dare show it to anyone, or that novel if you don't dare let people read it.

You may be conflating several different media types and we don't even know what the lawsuit tea leaves will tell us about that kind of visual/audio IP. As far as code goes, I think most companies have already shown how they protect themselves from 'open' source code.

does this mean you trust complete randoms just as much?

if i need advice on repairing a weird unique metal piece on a 1959 corvette, im going to trust the advice of an expert in classic corvettes way before i trust the advice of my barber who knows nothing about cars but confidently tells me to check the tire pressure.

this “oh no, experts have be wrong before” we see so much is wild to me. in nuanced fields i’ll take the advice of experts any day of the week waaaaaay before i take the advice from someone who’s entire knowledge of topic comes from a couple twitter post and a couple of youtube’s but their rhetoric sounds confident. confidently wrong dipshits and sophists are one of the plagues of the modern internet.

in complex nuanced subjects are experts wrong sometimes? absofuckinlutely. in complex nuanced subjects are they correct more often than random “did-my-own-research-for-20-minutes-but-got-distracted-because-i-can’t-focus-for-more-than-3-paragraphs-but-i-sound-confident guy?” absofuckinlutely.

These systems behave more like individual experts than like the internet – except they're more likely to be wrong than an expert is.

This is a real problem with permissive licensing. Large corporations effectively brainwashed large swaths of developers into working for free. Not working for the commons for free, as in AGPL, but working for corporations for free.

To a degree, yes. I only open source work where I expect reciprocal value from other contributions.

See also: BSD vs. GPL

The management of these walled gardens will keep saying that to your face as they sell your contributions. Meanwhile your family gets nothing.

"It's a nice thing to do" never seems to sway online platforms to treat their users better. This kind of asymmetry seems to only ever go one way.

One could answer that question to people whose salary does not depend upon not understanding the answer.

It's the technology message board on 4chan, each board has a name like that. /a/ for anime, /v/ for video games, etc.

As a user, why would I care whether an answer is "incorrect" or "hallucinated"? Neither one is going to solve the problem I have at hand. It sounds like a distinction without a difference.

I don't think human mistakes are distinguishable from hallucinations.

Why a vote? Voting != Verification.

LLMs are much better experience on the "Q side". Sure, there's the occasional hallucination here and there, but QnA sites are not all StackOverflow. Most of them are just content farms for SEO and advertising purposes - meaning, the veracity of the content doesn't matter, as long as it's driving clicks. At this moment, this makes LLMs much more trustworthy.

It's a good idea but probably not easy to implement. SO answers are usually quite neat, like an email. Solving a problem with ChatGPT is more like ... chat. It's hard to turn it into something googlable and Google is how SO gets most of its traffic and utility.

I think the actual reason people can't do it is that we avoid situations with high risk and no apparent reward. And we aren't sufficiently supportive of other people doing surprising things (so there's no reward for trying). I.e. it's a modern culture problem, not a human brain problem.

> These LLMs are polymaths that can spit out content at a super human rate.

Do you mean in theory or currently? Because currently, LLMs make simple errors (eg [1]) and are more capable of spitting out, well, nonsense. I think it's safe to say we're a long way from LLMs producing anything creatively good.

I'll put it this way: you won't be getting The Godfather from LLMs anytime soon but you can probably get an industrial film with generic music that tells you how to safely handle solvents, maybe.

Computers are generally good at doing math but LLMs generally aren't [2] and that really demonstrates the weaknesses in this statistical approach. ChatGPT (as one example) doesn't understand what numbers are or how to multiply them. It relies seeing similar answers to derive a likely answer so it often gets the first and large digits of the answer correct but not the middle. You can't keep scaling the input data to have it see every possible math question. That's just not practical.

Now multiplying two large numbers is a solvable problem. Counting Rs in strawberry is a solvable problem. But statistical LLMs are going to have a massive long tail of these problems. It's really going to take the next generational change to make progress.

[1]: https://www.inc.com/kit-eaton/how-many-rs-in-strawberry-this...

[2]: https://www.reachcapital.com/2024/07/16/why-llms-are-bad-at-...

"LLMs can train on the official documentation of tools l/libraries but they can't experiment and figure out solutions to weird problems"

LLMs train on way more than just the official documentation: they train on the code itself, the unit tests for that code (which, for well written projects, cover all sorts of undocumented edge-based) and - for popular projects - thousands of examples of that library being used (and unit tested) "in the wild".

This is why LLMs are so effective at helping figure out edge-cases for widely used libraries.

The best coding LLMs are also trained on additional custom examples written by humans who were paid to build proprietary training data for those LLMs.

I suspect they are increasingly trained on artificially created examples which have been validated (to a certain extent) through executing that code before adding it to the training data. That's a unique advantage for code - it's a lot harder to "verify" non-code generated prose since you can't execute that and see if you get an error.

> Sometimes online forums are the only place where you can find solutions for niche situations and edge cases.

That's the most valuable aspect of it. When you find yourself in these niches situations, it's nice when you see someone has encountered it and has done the legwork to solve it, saving you hours and days. And that's why Wikis like the Arch Wiki are important. You need people to document the system, not just individual components.

The first time that I asked a question on #cpp @Freenode was a unique experience for my younger self.

My message contained greetings and the question in the same message. I was banned immediately and the response from the mods was:

- do not greet; we don't have time for that bullshit

- do not use natural language questions; submit a test case and we will understand what you mean through your code

- do not abbreviate words (you have abbreviated "you" as "u"); if you do not have time to type the words, we do not have time to read them

The ban lasted for a week! :D

Yes, immature people are everywhere, but SO took it to a new level before they had to implement a code of conduct. I remember asking questions and getting "this is is common misconception, maybe you're looking for X instead" type of actually helpful and kind answers.

After some point it came to a point that if you're not asking a complete problem which can't be modeled as a logic statement, you're labeled as stupid for not knowing better. The thing is, if I knew better or already found the answer, I'd not be asking to SO in the first place.

After a couple of incidents, I left the place for the better. I can do my own research, and share my knowledge elsewhere.

Now they're training their and others models with that corpus, I'll never add a single dot to their dataset.

The major misunderstanding is that SO exists to help the question author first. It is not an IRC. The most value comes from googling a topic and getting existing answers on SO.

In other words, perhaps in your very specific case, your question is not XY problem but for the vast majority of visitors from google it won't be so. https://en.wikipedia.org/wiki/XY_problem

Personally, I always answered SO from at least two perspectives: how the question looks for someone coming from google and how the author might interpret it.

> Q: how do I do thing X in C?

SO does suck, but i've found that if you clarify in the question what you want, and pre-empt the Y instead of X type answers, you will get some results.

I find that this is mainly a problem in languages that attract "practical"/"best tool for the job" Philistines. Not going to name names right now but I had never really experienced this until I started using languages from a certain Washington based software company.

On the other hand, I find it to be a fatal flaw that LLMs can’t say, “Hey you probably don’t actually want to do it that way.”

It’s also quite fun when you ask niche questions that haven’t been asked or answered yet ("How do I do X with Y?"), and just get downvoted for some reason.

That’s when I stopped investing any effort into that community.

Turned out it, counter-intuitively, was impossible. And not documented anywhere.

My questions always get closed and marked as a duplicate with a comment linking to a question that's unrelated

I mean, those all sound like good questions. You might be a super genius, but most people who ask how to do X actually want to do Y. And if they DO want X, then those other questions about compiler and OS version really matter. The fact that you didn’t include them in your question shows you aren’t really respecting the time of the experts on the platform. If you know you are doing something unusual, then you need to provide a lot more context.

I find that LLMs are precisely that: marvelous engines to explore "what you don't know that you don't know", about anything.

Medium is even worse about this. It's more self-promotion than it is common help.

>The main issue with Stack Overflow (and similar public Q&A platforms) is that many contributors do not know what they do not know, leading to inaccurate answers.

The best Q&A platform would be the one where experts and scientists answer questions but sites like Wikipedia and Reddit showed that broad range of audience can also be pretty good at providing useful information and moderating it.

What you mention has been serious from day one indeed.

But to me the worst issue is it's now "Dead Overflow": most answers are completely, totally and utterly outdated. And seen that they made the mistake of having the concept of an "accepted answer" (which should never have existed), it only makes the issue worse.

If it's a question about things that don't change often, like algorithms, then it's OK. But for anything "tech", technical rot is a very real thing.

To me SO has both outdated and inaccurate answers.

I've gotten answers from OpenAI that were technically correct but quite horrible in the longer term. I've gotten the same kinds of answers on Stack Overflow, but there other people are eager to add the necessary feedback. I got the same feedback from an LLM but only because in that case I knew enough to ask for it.

Maybe we can get this multi-headed advantage back from LLMs by applying a team of divergent AIs to the same problem. I've had other occasions when OpenAI gave me crap that Claude corrected, and visa versa.

At the same time, if I coworker comes asking me for something _strange_, my first response is to gently inquire as to the direction of their efforts instead of helping them find an answer. Often enough, this ends up going back up their "call stack" to some goofy logic branch, which we then together undo, and everyone is pleased.

I think that knowledge hoarding may come back with a vengeance with the threat people feel from LLMs and offshoring.

The models come out fast enough

Doesn’t seem to be a great strategy to always need these things retrained, but OpenAI’s o1 has things from early 2024

Don’t ask about knowledge cutoffs anymore, that’s not how these things are trained these days. They don’t know their names or the date.

Hm fair point. Rudeness is actually a sign of humanity. Like that one black mirror episode

I’m sorry that you ran afoul of a CoC or whatever, but this sounds like a real ‘airing dirty laundry’ tangent.

It won't. If you look at advanced engineering/mathematics material online it is abysmal in quality of actually "explaining" the content. Most of the learning and understanding of intricacies happens via dialogue with professors/mentors/colleagues/etc.

That said, when that is not available, LLMs do an excellent job or rubber ducky-ing complicated topics.

I've found chatgpt quite helpful in understanding some things that I couldn't figure out when approaching pytorch for an internship

The reason those rules are created is because at some point something happened that necessitated that rule. (Not always of course, there are dictatorial mods.)

The fundamental problem is that communities/forums (in the general sense, e.g., market squares) don't scale, period. Because moderation and (transmission and error correction of) social mores don't scale.

> Some people appear to thrive in this environment where there are rules for everything. People who memorize and enforce all of the rules on others get to operate a tiny little power trip while opening an opportunity to lecture internet strangers all day.

Toddlers go through this sometimes around ages 2 or 3. They discover the "rules" for the first time and delight in brandishing them.

Well, I believe the underlying problem of platforms like StackOverflow, ticketing systems (in-house and public) and even CRMs is not really solvable. The problem is, the quality of an answer is actually not easy to determine. All the mechanisms we have are hacks, and better solutions would need more resources... which leads to skewed incentives, and ultimately to a "knwoledge" db thats actually not very good. People are incentiviszed to collect karma points, or whatever it is. But these metrics are not really resembling the quality of their work... Crowdsourcing this mechanisms via upvotes or whatever does also not really work, because quantity is not quality... As said, I believe this is a problem we can not solve.

Everyone has a pet theory about what’s wrong with SO; but here’s the truth:

Whatever they’re doing, it isn’t working.

Blame mods. Blame AI. Blame askers… whatever man.

That is a sinking ship.

If you don’t see people complain about SO, it’s because they aren’t using it, not because they’re using the search.

Pretty hard to argue at this point that the problem is with the users being too shit to use the platform.

That’s some high level BS.

Because consumers in every market develop models of reality (and make purchasing decisions) on the basis of their best attempts to derive accuracy from their own inevitably flawed perceptions, instead of having perfect information about every aspect of the world?

It was useful and not the wild west as long as a very small group of intelligent and highly motivated individuals moderated it. First and foremost Jeff Atwood used to do a lot of moderation himself - not unlike dang on HN.

When that stopped, the site (and to some degree its growing number of sister sites) continued on its ballistic curve, slowly but continuously descending into the abyss.

My primary take away is that we have not found a way to scale moderation. SO was doomed anyway, LLMs have just sped up that process.

I disagree. It was useful because the UI was (and is!) great. Easy to use markdown input, lists of answers sorted by votes, very limited ads, etc. The gamification was also well done.

Compared to anything before it (endless phpBB forums, expertsexchange, etc.) it was just light years ahead.

Even today compared the SO UI with Quora. It's still 10x better.

I have definitely noticed a large drop in responses on SO. I am old enough to have seen the death of these platforms. First to go was usenet when AOL and its ilk became a thing and every channel turned into spam.

I think the problem isn't specific to SO. Text-based communication with strangers lacks two crucial emotional filters. Before speaking, a person anticipates the listener's reaction and adjusts what they say accordingly. After speaking, they pay attention to the listener's reaction to update their understanding for the future.

Without seeing faces, people just don't do this very well.

The model of QA that Stack Overflow and its various forks follow the same approach struggle with the 90/9/1 problem ( https://en.wikipedia.org/wiki/1%25_rule ).

Q&A was designed to handle the social explosion problem and the eternal September problems by having a larger percent of the username take an interest in the community over time and continue to maintain that ideal. Things like comments and discussions being difficult is part of the design to make it so that you don't get protracted discussions that in turn needs more moderation resources.

The fraction of the people doing the curation and moderation of the site overall has dropped. The reasons for that drop are manyfold. I believe that much of it falls squarely upon Stack Overflow corporate without considering second order effects of engaging and managing the community of people who are interested in the success of the site as they envision.

Ultimately, Stack Overflow has become too successful and the people looking to it now have a different vision for what it should be that comes into conflict with both the design of the site and the vision of the core group.

While Stack Overflow can thrive with a smaller number of people asking "good" (yes, very subjective) questions it has difficulty when it strays into questions that need discussion (which its design comes into conflict with) or too many questions for the committed core group to maintain. Smaller sites can (and do) have a larger fraction of the user base committed to the goals of the site and in turn are able to provide more individual guidance - while Stack Overflow has long gone past that point.

---

Stack Overflow and its Q&A format that has been often copied works for certain sized user bases. It needs enough people to keep it interesting, but it fails to scale when too many people participate who have a different idea of what questions should be there.

There is a lack of moderation tools for the core user base to be able to manage it at scale (you will note the history of Stack Overflow has been removing and restricting moderation tools until it gets "too" bad - see also removal of 20k users helping with flag handling and the continued rescoping of close reasons).

Until someone comes up with a fundamentally different approach that is able to handle moderation at scale or sufficient barriers for new accounts (to handle the Eternal September problem), we are going to continue to see Stack Overflow clones spout and die on the vine along with a continued balkanization of knowledge in smaller areas that are able handle vision and moderation at a smaller scale.

---

Every attempt at a site I've seen since (and I include things like Lemmy in this which did a "copy reddit" and then worry (or not) about moderation) have started from a "get popular, then work on the moderation problem" which is ultimately too late to really solve the problem. The tools for moderation need to be baked into the design from the start.

Plagiarizing means violating copyright, loosely speaking. When you, as a human, use SO, you assign your rights to the content to SO. That company is licensing the content to 3rd parties, including those who want to train their LLMs.

What I find is that the LLMs are not spitting out SO text word for word, as one would when plagiarizing. Rather, the LLM uses the context and words of my question when answering, making the response specific and cohesive (by piecing together answers from across questions).

A lot of people comment on online forums for free and are arseholes there too. Not in this thread so far that I've read, to be clear, but it certainly happens. How would you qualify the difference?

The people I am referring to are not helping. At this point they are making SO worse.

The problems are two-fold:

1. Any community with volunteer moderators attracts the kind of people you don't want to be moderators. They enjoy rigidly enforcing the rules even if it makes no sense.

2. There are two ways to find questions and answer them: random new questions from the review queue, and from Google when you're searching for a problem you have. SO encourages the former, and unfortunately the vast majority of questions are awful. If you go and review questions like this you will go "downvote close, downvote close, downvote close". You're going to correctly close a load of trash questions that nobody cares about and a load of good questions you just don't understand.

I've started recording a list of questions I've asked that get idiotic downvotes or closed, so I can write a proper rant about it with concrete examples. Otherwise you get people dismissing the problem as imaginary.

These mods now hold SO hostage. SO is definitely aware of the problem but they can't instigate proper changes to fix it because the mods like this situation and they revolt if SO tries to remedy it.