this post was submitted on 23 Jun 2026
1186 points (98.6% liked)
196
6429 readers
1373 users here now
Community Rules
You must post before you leave
Be nice. Assume others have good intent (within reason).
Block or ignore posts, comments, and users that irritate you in some way rather than engaging. Report if they are actually breaking community rules.
Use content warnings and/or mark as NSFW when appropriate. Most posts with content warnings likely need to be marked NSFW.
Most 196 posts are memes, shitposts, cute images, or even just recent things that happened, etc. There is no real theme, but try to avoid posts that are very inflammatory, offensive, very low quality, or very "off topic".
Bigotry is not allowed, this includes (but is not limited to): Homophobia, Transphobia, Racism, Sexism, Abelism, Classism, or discrimination based on things like Ethnicity, Nationality, Language, or Religion.
Avoid shilling for corporations, posting advertisements, or promoting exploitation of workers.
Proselytization, support, or defense of authoritarianism is not welcome. This includes but is not limited to: imperialism, nationalism, genocide denial, ethnic or racial supremacy, fascism, Nazism, Marxism-Leninism, Maoism, etc.
Avoid AI generated content.
Avoid misinformation.
Avoid incomprehensible posts.
No threats or personal attacks.
No spam.
Moderator Guidelines
Moderator Guidelines
- Don’t be mean to users. Be gentle or neutral.
- Most moderator actions which have a modlog message should include your username.
- When in doubt about whether or not a user is problematic, send them a DM.
- Don’t waste time debating/arguing with problematic users.
- Assume the best, but don’t tolerate sealioning/just asking questions/concern trolling.
- Ask another mod to take over cases you struggle with, if you get tired, or when things get personal.
- Ask the other mods for advice when things get complicated.
- Share everything you do in the mod matrix, both so several mods aren't unknowingly handling the same issues, but also so you can receive feedback on what you intend to do.
- Don't rush mod actions. If a case doesn't need to be handled right away, consider taking a short break before getting to it. This is to say, cool down and make room for feedback.
- Don’t perform too much moderation in the comments, except if you want a verdict to be public or to ask people to dial a convo down/stop. Single comment warnings are okay.
- Send users concise DMs about verdicts about them, such as bans etc, except in cases where it is clear we don’t want them at all, such as obvious transphobes. No need to notify someone they haven’t been banned of course.
- Explain to a user why their behavior is problematic and how it is distressing others rather than engage with whatever they are saying. Ask them to avoid this in the future and send them packing if they do not comply.
- First warn users, then temp ban them, then finally perma ban them when they break the rules or act inappropriately. Skip steps if necessary.
- Use neutral statements like “this statement can be considered transphobic” rather than “you are being transphobic”.
- No large decisions or actions without community input (polls or meta posts f.ex.).
- Large internal decisions (such as ousting a mod) might require a vote, needing more than 50% of the votes to pass. Also consider asking the community for feedback.
- Remember you are a voluntary moderator. You don’t get paid. Take a break when you need one. Perhaps ask another moderator to step in if necessary.
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
That's cool, I like to do so as well. But it highly depends on the topic. If you have no interest in the topic and only need to deal with some stuff temporarily then reading multiple related encyclopedia entries thoroughly is much more time intensive than getting it 'spoon fed'. Especially when you look at the outcomes this might just not be worth the effort.
And that's one of the uses where I find LLMs neat: get the answer tailored to what you want and what you need, skipping a lot of intellectual bloat that you have no use or interest for anyway.
But I fundamentally don't trust it in a way that is different than any real source. It just isn't a good tool for accurate information and never will be. Even if it's using accurate sources, the summaries can easily mislead by virtue of not understanding which details are most important. It can't reliably figure out what is relevant though language analysis alone.
Yeah you're right about the accuracy issue. Even if an LLM is 90% accurate, after 1 million requests, you still get 100 thousand erroneous replies. This is significant. Which is why caution is important. I suppose there is a middle way between risks of errors and the outcome one would like to achieve.
The best use of machine learning is to do "human like" pattern recognition at large scale. Like with any statistical analysis, it can have predictable error rates that can be accounted for. The key is to give it narrowly defined tasks that are then followed up by manual review.
A big part of the problem with using it to summarize information is that different topics require different perspectives to analyze properly. A scientist might excel at analyzing and understanding research papers written in their field, but be unable to parse the technical jargon and format of articles from different fields. This is also true with understanding fandoms or cultural subgroups. A specific LLM might be fantastic at analyzing certain types of articles, but suck at others, and because you aren't able to tweak parameters, you can't really train it to specialize.
In a sense, the major LLM companies are not only selling a questionable product, but actively holding back more productive uses of AI. They buy up all the computer hardware and waste it trying to build an all purpose tool. Meanwhile, the most fruitful uses of AI cost more to use. Training and running specialized models can theoretically be done on local hardware, but even most prosumers have been priced out. More ambitious uses for scientific research spend more on renting out supercomputers, all because techbros want a monopoly without offering a better product.
And on top of all of that, how much time are you actually saving by asking it to summarize for you? If you need to double check it anyways because it keeps getting stuff wrong, wouldn't it have been better to do it yourself from the start? You aren't doing something that would be impossible otherwise, you're just trying to save time. Machine learning can help scientists run simulations that would take millions of years to run otherwise. Comparing that to the time you spend using and then double checking the LLM, what would your gains be? Would it save orders of magnitude, or would you only save a few minutes off a half hour task? Is the risk of being misinformed worth even that?