u/didyousayboop

For whatever reason, this subreddit seems to get a ton of AI-generated posts.

Also, apparently a lot of people have not yet learned the pattern recognition to spot AI-generated content.

So, there is an AI detector called Pangram that you can use for free. It’s apparently pretty accurate: https://en.wikipedia.org/wiki/Pangram_(AI_detector)#Efficacy

Anecdotally, my subjective pattern recognition of whether something is AI or not has agreed with Pangram so far whenever I’ve used it.

Pangram: https://www.pangram.com/

See, for example, what happens when you copy and paste the text above into Pangram: https://www.pangram.com/history/b3f01db7-7f75-4a8d-a9da-30fc248b1c9b?ucc=K1xs21CjScU

It gives a verdict of 100% human-written.

For whatever reason, this subreddit seems to get a ton of AI-generated posts.

Also, apparently a lot of people have not yet learned the pattern recognition to spot AI-generated content.

So, there is an AI detector called Pangram that you can use for free. It’s apparently pretty accurate: https://en.wikipedia.org/wiki/Pangram_(AI_detector)#Efficacy

Anecdotally, my subjective pattern recognition of whether something is AI or not has agreed with Pangram so far whenever I’ve used it.

Pangram: https://www.pangram.com/

>Nine PBS in St. Louis filed a lawsuit against information management corporation Iron Mountain Data Centers July 28, seeking to recover over 50 terabytes of archival materials stored in one of the company’s Denver-based data centers. 

>The lawsuit filed in Denver District Court alleges that the station’s cloud-storage vendor, Open Source Storage, abruptly cut off access to Nine PBS’ data earlier this year without warning. It states OSS, which had a separate relationship with Iron Mountain to provide data storage, went “defunct,” leaving Nine PBS’ archives in a data center operated by Iron Mountain. 

>Iron Mountain has refused to return the materials to the station because its client, OSS, technically owned “the physical services housing the data” within Iron Mountain, according to the complaint.

>The station requested temporary and preliminary relief that would prevent Iron Mountain from deleting, modifying or overwriting its materials in the suit. A district judge granted the motion and set a hearing for Wednesday. 

>In a statement to Current, Nine PBS VP and CCO Leah Freeman confirmed the station’s lawsuit against Iron Mountain and its dedication to retrieving the archival materials and programming, which she says span over “70 years of our organization’s history.”

>“We are committed to ensuring we can recover and restore full access to this valuable content, which Nine PBS rightfully owns, as it holds significant historical importance for St. Louis.”

---

Update:

>District Court Judge Eric Elliff ordered Iron Mountain, which had a separate business relationship with OSS, to cooperate with Nine PBS in any way possible to retrieve the data. He found that the station is the rightful owner of the materials and entitled to recover them from OSS’ storage systems.

>Under his order, Nine PBS is to identify a third-party vendor, such as a former OSS employee, who can assist in accessing and retrieving the data from the infrastructure that’s housed in Iron Mountain’s center within 30 days.

https://current.org/2026/08/judge-sets-framework-for-nine-pbs-to-retrieve-archival-data/

I want people to understand how easy and cheap it is to start an alternative to Pointless Bitchery. Pointless Bitchery uses XenForo, but XenForo is needlessly expensive software. I believe the creator of Pointless Bitchery is spending $1,000/year, which is totally insane.

Lemmy and Discourse are two free softwares that would be good alternatives, arguably better than XenForo. Lemmy is Reddit-like (link aggregator style) and Discourse is a modern take on traditional Internet forums. 

Both are free and open-source. Both have third-party managed hosting that is relatively cheap. I did my own hosting of a Discourse forum on a cloud VPS (VPS = virtual private server). and it was not only cheap but easy to run. However, managed hosting is somewhat more expensive to abstract away some of the technical complexity.

Managed hosting is on the order of $10 to $20 a month.

Example of what Lemmy looks like: https://lemmy.world/

Example of what Discourse looks like: https://meta.discourse.org/

You can Google “Lemmy managed hosting” or “Discourse managed hosting” to do price comparisons.

For example, Lemmy hosting from Elestio is $11/month or $132/year.

Managed Discourse hosting from Elestio is $16/month or $192/year.

Previously, I wrote about how to host a Discourse forum on a cloud VPS easily and cheaply: https://www.reddit.com/r/datalounge/comments/1uetns3/how_to_host_a_large_internet_forum_for_542month/

The cost is $65/year before tax, maybe $75/year with tax, depending on what taxes you pay. It’s not particularly complicated to host Discourse on your own cloud VPS as opposed to paying for managed hosting.

I haven’t tried to host Lemmy myself, so I can’t tell you how hard or easy it is. The cost would likely be the same as Discourse.

Whatever you do, don’t pay $1,000/year for hosting…

Edit: See my comment here for info on how to host a XenForo forum for $255 a year.

https://preview.redd.it/b2xz2ezjpqgh1.png?width=1416&format=png&auto=webp&s=94cc5a0a2c853c1f28fda375f2273d99fcfaa14f

https://preview.redd.it/91gegfbnpqgh1.png?width=1456&format=png&auto=webp&s=63f9da02717f8678417bd65586a3422c2c399091

https://preview.redd.it/e34sxeh92rgh1.png?width=2550&format=png&auto=webp&s=e87d08665ce01ea8449bf8085e759a6bc248222e

Who is Hannah Ritchie? Per Wikipedia:

>Hannah Ritchie (born 1993) is a Scottish data scientist who is a senior researcher at the University of Oxford in the Oxford Martin School, and deputy editor at Our World in Data. Her work focuses on sustainability, in relation to climate change, energy, food and agriculture, biodiversity, air pollution, deforestation, and public health.

What does Hannah Ritchie say about the electricity consumption of ChatGPT? You can read it on her Substack. Here's a quote:

>I’ve written several articles on the footprint of individual LLM queries.

>A key takeaway from the numbers was that asking a chatbot a question — which is what most people were using AI for in their day-to-day lives — consumes very little energy.

>Tech companies have not been very transparent about the energy use of their AI models (and I think they should be), but the numbers seemed to converge around 0.3 watt-hours (Wh) per typical text query. To put this into context, asking ChatGPT or Gemini 10 simple questions is equivalent to about 10 seconds of microwaving or mere seconds of showering.

After getting into the weeds on where this data comes from and how the analysis is done, she goes on:

>What do these numbers mean for individual footprints?

>Many people are using AI for medium- to long-form text queries, such as asking a quick question or requesting a short fact-check or correction. Their energy use is very small, even if they’re asking tens or hundreds of questions a day. A hundred questions have a footprint of around 30 Wh. That’s roughly the amount of electricity the average American consumes in just over a minute (or for the average European, every two and a half minutes).[4]

And then she gives an important caveat about how the very heaviest power users of AI -- probably mostly people using it for coding (this is me editorializing, not what Ritchie herself says) -- are consuming significantly more:

>The footprint of someone who uses agents heavily is not so negligible.

>Let’s say they do 4 agentic queries per hour (how many you can do in an hour is limited by the fact that complex tasks can take 15 minutes or more to complete). And they do this for 6 hours a day. That’s 24 per day. We’ll assume that the total electricity use per query is actually 100 Wh (50 Wh multiplied by two).

>They’ll consume 2,400 Wh (or 2.4 kWh). That’s like running a tumble dryer for one cycle, or driving an electric car eight miles. It’s around 7% of the average American’s electricity use (but a much smaller share of total energy use).

>It’s not blowing up their footprint, but it’s not nothing either.

You can read the full section of her Substack post entitled "What’s the energy footprint of individual queries?" to get all the caveats, sources, and assumptions.

Hannah Ritchie has also published an article on Our World in Data on the same topic. That might be an equally good or better source.

Ritchie has also created an interactive calculator for comparing how much electricity different things use, including AI chatbots.

This is my own math, not using the calculator. Let's say you did 100 average ChatGPT queries per day. 0.34 watt-hours * 100 = 34 watt-hours. What is this equivalent to?

• A typical LED lightbulb uses 10 watts. Over 1 hour, that's 10 watt-hours. So, over about 3 ½ hours, a typical LED lightbulb will use 34 watt-hours.
• Or compare to a dishwasher. A typical dishwasher uses 1.2 kilowatt-hours (kWh) for a load of dishes. 1.2 kWh is 1,200 watt-hours. So, that's equivalent to 3,530 average ChatGPT queries. If you did 100 of those queries a day, running the dishwasher would be equivalent to about 35 days of ChatGPT usage.
• Another helpful comparison is a ceiling fan. A typical ceiling fan uses 75 watts. So, leave a ceiling fan on for 30 minutes, it will use about 38 watt-hours of electricity. About the same as 100 average ChatGPT queries.
• TVs use about 100 watts. So, in about 20 minutes your TV uses about as much electricity as 100 ChatGPT queries. One episode of Bob's Burgers!

I can't find any hard data on how many queries the typical user is doing per day. 100 seems like a lot. But then of course all the math can change depending on the type of query as well. 4 or 5 "reasoning" queries, according to Ritchie, would use as much energy as 100 average queries.

One point you might raise is that it's also the electricity consumed by training we have to consider, not just inference. But here's a quote from Hannah Ritchie's Our World in Data article on this topic:

>Before digging into the data, it’s worth clarifying what is included in AI energy consumption. It’s the electricity consumed for both training and running the models (called “inference”). Tech companies rarely publish data on how much energy is consumed when training their models, but based on the estimates we do have, it’s likely that energy demand is dominated by inference, not training.^(1)

That footnote at the end of the paragraph says:

>Epoch AI estimates that training Grok 4 consumed around 0.31 terawatt-hours (TWh) of electricity. As we’ll see later, total demand for AI in 2025 was around 155 TWh. So, training Grok 4 — a fairly large model — was around 0.2% of the total.

So, maybe we can say that training uses much less electricity than inference?

Another point you could raise is that we need to account for all the energy used to manufacture the GPUs that AI uses and all the other less direct energy costs. In other words, we need to do a life cycle analysis.

I can find almost no information about any life cycle analysis of AI chatbots, which would encompass inference, training, and everything else, like the manufacturing of the chips. I found a brief mention of a life cycle analysis in another Substack post by Hannah Ritchie:

>Mistral AI, another AI company, conducted an environmental analysis of its LLMs. It used a life-cycle assessment, conducted by external consultancy agencies. While the methodology was not that transparent or detailed, it did provide breakdowns of where in the process, impacts came from (I just wish they’d split out inference from training). Overall, the impacts were low: just 1 gram of CO2 per page of text generated (which is a fairly long text response). That’s very low.

https://preview.redd.it/b2xz2ezjpqgh1.png?width=1416&format=png&auto=webp&s=94cc5a0a2c853c1f28fda375f2273d99fcfaa14f

https://preview.redd.it/91gegfbnpqgh1.png?width=1456&format=png&auto=webp&s=63f9da02717f8678417bd65586a3422c2c399091

https://preview.redd.it/e34sxeh92rgh1.png?width=2550&format=png&auto=webp&s=e87d08665ce01ea8449bf8085e759a6bc248222e

Who is Hannah Ritchie? Per Wikipedia:

>Hannah Ritchie (born 1993) is a Scottish data scientist who is a senior researcher at the University of Oxford in the Oxford Martin School, and deputy editor at Our World in Data. Her work focuses on sustainability, in relation to climate change, energy, food and agriculture, biodiversity, air pollution, deforestation, and public health.

What does Hannah Ritchie say about the electricity consumption of ChatGPT? You can read it on her Substack. Here's a quote:

>I’ve written several articles on the footprint of individual LLM queries.

>A key takeaway from the numbers was that asking a chatbot a question — which is what most people were using AI for in their day-to-day lives — consumes very little energy.

>Tech companies have not been very transparent about the energy use of their AI models (and I think they should be), but the numbers seemed to converge around 0.3 watt-hours (Wh) per typical text query. To put this into context, asking ChatGPT or Gemini 10 simple questions is equivalent to about 10 seconds of microwaving or mere seconds of showering.

After getting into the weeds on where this data comes from and how the analysis is done, she goes on:

>What do these numbers mean for individual footprints?

>Many people are using AI for medium- to long-form text queries, such as asking a quick question or requesting a short fact-check or correction. Their energy use is very small, even if they’re asking tens or hundreds of questions a day. A hundred questions have a footprint of around 30 Wh. That’s roughly the amount of electricity the average American consumes in just over a minute (or for the average European, every two and a half minutes).[4]

And then she gives an important caveat about how the very heaviest power users of AI -- probably mostly people using it for coding (this is me editorializing, not what Ritchie herself says) -- are consuming significantly more:

>The footprint of someone who uses agents heavily is not so negligible.

>Let’s say they do 4 agentic queries per hour (how many you can do in an hour is limited by the fact that complex tasks can take 15 minutes or more to complete). And they do this for 6 hours a day. That’s 24 per day. We’ll assume that the total electricity use per query is actually 100 Wh (50 Wh multiplied by two).

>They’ll consume 2,400 Wh (or 2.4 kWh). That’s like running a tumble dryer for one cycle, or driving an electric car eight miles. It’s around 7% of the average American’s electricity use (but a much smaller share of total energy use).

>It’s not blowing up their footprint, but it’s not nothing either.

You can read the full section of her Substack post entitled "What’s the energy footprint of individual queries?" to get all the caveats, sources, and assumptions.

Hannah Ritchie has also published an article on Our World in Data on the same topic. That might be an equally good or better source.

Ritchie has also created an interactive calculator for comparing how much electricity different things use, including AI chatbots.

This is my own math, not using the calculator. Let's say you did 100 average ChatGPT queries per day. 0.34 watt-hours * 100 = 34 watt-hours. What is this equivalent to?

• A typical LED lightbulb uses 10 watts. Over 1 hour, that's 10 watt-hours. So, over about 3 ½ hours, a typical LED lightbulb will use 34 watt-hours.
• Or compare to a dishwasher. A typical dishwasher uses 1.2 kilowatt-hours (kWh) for a load of dishes. 1.2 kWh is 1,200 watt-hours. So, that's equivalent to 3,530 average ChatGPT queries. If you did 100 of those queries a day, running the dishwasher would be equivalent to about 35 days of ChatGPT usage.
• Another helpful comparison is a ceiling fan. A typical ceiling fan uses 75 watts. So, leave a ceiling fan on for 30 minutes, it will use about 38 watt-hours of electricity. About the same as 100 average ChatGPT queries.
• TVs use about 100 watts. So, in about 20 minutes your TV uses about as much electricity as 100 ChatGPT queries. One episode of Bob's Burgers!

I can't find any hard data on how many queries the typical user is doing per day. 100 seems like a lot. But then of course all the math can change depending on the type of query as well. 4 or 5 "reasoning" queries, according to Ritchie, would use as much energy as 100 average queries.

One point you might raise is that it's also the electricity consumed by training we have to consider, not just inference. But here's a quote from Hannah Ritchie's Our World in Data article on this topic:

>Before digging into the data, it’s worth clarifying what is included in AI energy consumption. It’s the electricity consumed for both training and running the models (called “inference”). Tech companies rarely publish data on how much energy is consumed when training their models, but based on the estimates we do have, it’s likely that energy demand is dominated by inference, not training.^(1)

That footnote at the end of the paragraph says:

>Epoch AI estimates that training Grok 4 consumed around 0.31 terawatt-hours (TWh) of electricity. As we’ll see later, total demand for AI in 2025 was around 155 TWh. So, training Grok 4 — a fairly large model — was around 0.2% of the total.

So, maybe we can say that training uses much less electricity than inference?

Another point you could raise is that we need to account for all the energy used to manufacture the GPUs that AI uses and all the other less direct energy costs. In other words, we need to do a life cycle analysis.

I can find almost no information about any life cycle analysis of AI chatbots, which would encompass inference, training, and everything else, like the manufacturing of the chips. I found a brief mention of a life cycle analysis in another Substack post by Hannah Ritchie:

>Mistral AI, another AI company, conducted an environmental analysis of its LLMs. It used a life-cycle assessment, conducted by external consultancy agencies. While the methodology was not that transparent or detailed, it did provide breakdowns of where in the process, impacts came from (I just wish they’d split out inference from training). Overall, the impacts were low: just 1 gram of CO2 per page of text generated (which is a fairly long text response). That’s very low.

https://preview.redd.it/b2xz2ezjpqgh1.png?width=1416&format=png&auto=webp&s=94cc5a0a2c853c1f28fda375f2273d99fcfaa14f

https://preview.redd.it/91gegfbnpqgh1.png?width=1456&format=png&auto=webp&s=63f9da02717f8678417bd65586a3422c2c399091

https://preview.redd.it/e34sxeh92rgh1.png?width=2550&format=png&auto=webp&s=e87d08665ce01ea8449bf8085e759a6bc248222e

Who is Hannah Ritchie? Per Wikipedia:

>Hannah Ritchie (born 1993) is a Scottish data scientist who is a senior researcher at the University of Oxford in the Oxford Martin School, and deputy editor at Our World in Data. Her work focuses on sustainability, in relation to climate change, energy, food and agriculture, biodiversity, air pollution, deforestation, and public health.

What does Hannah Ritchie say about the electricity consumption of ChatGPT? You can read it on her Substack. Here's a quote:

>I’ve written several articles on the footprint of individual LLM queries.

>A key takeaway from the numbers was that asking a chatbot a question — which is what most people were using AI for in their day-to-day lives — consumes very little energy.

>Tech companies have not been very transparent about the energy use of their AI models (and I think they should be), but the numbers seemed to converge around 0.3 watt-hours (Wh) per typical text query. To put this into context, asking ChatGPT or Gemini 10 simple questions is equivalent to about 10 seconds of microwaving or mere seconds of showering.

After getting into the weeds on where this data comes from and how the analysis is done, she goes on:

>What do these numbers mean for individual footprints?

>Many people are using AI for medium- to long-form text queries, such as asking a quick question or requesting a short fact-check or correction. Their energy use is very small, even if they’re asking tens or hundreds of questions a day. A hundred questions have a footprint of around 30 Wh. That’s roughly the amount of electricity the average American consumes in just over a minute (or for the average European, every two and a half minutes).[4]

And then she gives an important caveat about how the very heaviest power users of AI -- probably mostly people using it for coding (this is me editorializing, not what Ritchie herself says) -- are consuming significantly more:

>The footprint of someone who uses agents heavily is not so negligible.

>Let’s say they do 4 agentic queries per hour (how many you can do in an hour is limited by the fact that complex tasks can take 15 minutes or more to complete). And they do this for 6 hours a day. That’s 24 per day. We’ll assume that the total electricity use per query is actually 100 Wh (50 Wh multiplied by two).

>They’ll consume 2,400 Wh (or 2.4 kWh). That’s like running a tumble dryer for one cycle, or driving an electric car eight miles. It’s around 7% of the average American’s electricity use (but a much smaller share of total energy use).

>It’s not blowing up their footprint, but it’s not nothing either.

You can read the full section of her Substack post entitled "What’s the energy footprint of individual queries?" to get all the caveats, sources, and assumptions.

Hannah Ritchie has also published an article on Our World in Data on the same topic. That might be an equally good or better source.

Ritchie has also created an interactive calculator for comparing how much electricity different things use, including AI chatbots.

This is my own math, not using the calculator. Let's say you did 100 average ChatGPT queries per day. 0.34 watt-hours * 100 = 34 watt-hours. What is this equivalent to?

• A typical LED lightbulb uses 10 watts. Over 1 hour, that's 10 watt-hours. So, over about 3 ½ hours, a typical LED lightbulb will use 34 watt-hours.
• Or compare to a dishwasher. A typical dishwasher uses 1.2 kilowatt-hours (kWh) for a load of dishes. 1.2 kWh is 1,200 watt-hours. So, that's equivalent to 3,530 average ChatGPT queries. If you did 100 of those queries a day, running the dishwasher would be equivalent to about 35 days of ChatGPT usage.
• Another helpful comparison is a ceiling fan. A typical ceiling fan uses 75 watts. So, leave a ceiling fan on for 30 minutes, it will use about 38 watt-hours of electricity. About the same as 100 average ChatGPT queries.
• TVs use about 100 watts. So, in about 20 minutes your TV uses about as much electricity as 100 ChatGPT queries. One episode of Bob's Burgers!

I can't find any hard data on how many queries the typical user is doing per day. 100 seems like a lot. But then of course all the math can change depending on the type of query as well. 4 or 5 "reasoning" queries, according to Ritchie, would use as much energy as 100 average queries.

One point you might raise is that it's also the electricity consumed by training we have to consider, not just inference. But here's a quote from Hannah Ritchie's Our World in Data article on this topic:

>Before digging into the data, it’s worth clarifying what is included in AI energy consumption. It’s the electricity consumed for both training and running the models (called “inference”). Tech companies rarely publish data on how much energy is consumed when training their models, but based on the estimates we do have, it’s likely that energy demand is dominated by inference, not training.^(1)

That footnote at the end of the paragraph says:

>Epoch AI estimates that training Grok 4 consumed around 0.31 terawatt-hours (TWh) of electricity. As we’ll see later, total demand for AI in 2025 was around 155 TWh. So, training Grok 4 — a fairly large model — was around 0.2% of the total.

So, maybe we can say that training uses much less electricity than inference?

Another point you could raise is that we need to account for all the energy used to manufacture the GPUs that AI uses and all the other less direct energy costs. In other words, we need to do a life cycle analysis.

I can find almost no information about any life cycle analysis of AI chatbots, which would encompass inference, training, and everything else, like the manufacturing of the chips. I found a brief mention of a life cycle analysis in another Substack post by Hannah Ritchie:

>Mistral AI, another AI company, conducted an environmental analysis of its LLMs. It used a life-cycle assessment, conducted by external consultancy agencies. While the methodology was not that transparent or detailed, it did provide breakdowns of where in the process, impacts came from (I just wish they’d split out inference from training). Overall, the impacts were low: just 1 gram of CO2 per page of text generated (which is a fairly long text response). That’s very low.

https://preview.redd.it/b2xz2ezjpqgh1.png?width=1416&format=png&auto=webp&s=94cc5a0a2c853c1f28fda375f2273d99fcfaa14f

https://preview.redd.it/91gegfbnpqgh1.png?width=1456&format=png&auto=webp&s=63f9da02717f8678417bd65586a3422c2c399091

Who is Hannah Ritchie? Per Wikipedia:

>Hannah Ritchie (born 1993) is a Scottish data scientist who is a senior researcher at the University of Oxford in the Oxford Martin School, and deputy editor at Our World in Data. Her work focuses on sustainability, in relation to climate change, energy, food and agriculture, biodiversity, air pollution, deforestation, and public health.

What does Hannah Ritchie say about the electricity consumption of ChatGPT? You can read it on her Substack. Here's a quote:

>I’ve written several articles on the footprint of individual LLM queries.

>A key takeaway from the numbers was that asking a chatbot a question — which is what most people were using AI for in their day-to-day lives — consumes very little energy.

>Tech companies have not been very transparent about the energy use of their AI models (and I think they should be), but the numbers seemed to converge around 0.3 watt-hours (Wh) per typical text query. To put this into context, asking ChatGPT or Gemini 10 simple questions is equivalent to about 10 seconds of microwaving or mere seconds of showering.

After getting into the weeds on where this data comes from and how the analysis is done, she goes on:

>What do these numbers mean for individual footprints?

>Many people are using AI for medium- to long-form text queries, such as asking a quick question or requesting a short fact-check or correction. Their energy use is very small, even if they’re asking tens or hundreds of questions a day. A hundred questions have a footprint of around 30 Wh. That’s roughly the amount of electricity the average American consumes in just over a minute (or for the average European, every two and a half minutes).[4]

And then she gives an important caveat about how the very heaviest power users of AI -- probably mostly people using it for coding (this is me editorializing, not what Ritchie herself says) -- are consuming significantly more:

>The footprint of someone who uses agents heavily is not so negligible.

>Let’s say they do 4 agentic queries per hour (how many you can do in an hour is limited by the fact that complex tasks can take 15 minutes or more to complete). And they do this for 6 hours a day. That’s 24 per day. We’ll assume that the total electricity use per query is actually 100 Wh (50 Wh multiplied by two).

>They’ll consume 2,400 Wh (or 2.4 kWh). That’s like running a tumble dryer for one cycle, or driving an electric car eight miles. It’s around 7% of the average American’s electricity use (but a much smaller share of total energy use).

>It’s not blowing up their footprint, but it’s not nothing either.

You can read the full section of her Substack post entitled "What’s the energy footprint of individual queries?" to get all the caveats, sources, and assumptions.

Are you aware of any estimates of AI chatbot energy use from reliable sources that are significantly higher or lower than Ritchie's estimates? If so, please cite them in the comments!

From Paul’s old web show Speakeasy, which I only just discovered. I loved how silly it is.

I moderate subreddits, but I'm not asking about any of the subreddits I moderate.

I'm a member of a subreddit where the sole moderator has announced their intention to "repurpose" the subreddit a week from now.

There is an active community in the subreddit and they don't want this to happen. The moderator doesn't seem interested in what the community's wishes are.

Is a moderator allowed to do that? It seems wrong, but I couldn't find specific wording in the Moderator Code of Conduct that seems like it would apply to this specific situation.

Is the best recourse filing a report of a Moderator Code of Conduct violation? Not sure if it's against the rules. If it is against the rules, not sure which rule or what the best recourse is.

Thanks for any help you can provide.

---

Edited to add some important details:

Strangely, the moderator has not clearly said what the planned change is or isn't. They have intimated they might shut down the subreddit completely. Reading between the lines, it seems like they just want to try to close the subreddit. Otherwise, they have just said the subreddit will be "repurposed", and they themselves put "repurposed" in quotes, as if it's a euphemism.

They have not outright said they want to shut down the subreddit, but they've said the subreddit will soon cease to serve any purpose. They specified a contingency in which the subreddit would unexpectedly remain open, which seems to imply that, barring that contingency, the subreddit will be closed.

---

Edit #2:

The question has been mostly resolved, see comments here.

I moderate subreddits, but I'm not asking about any of the subreddits I moderate.

I'm a member of a subreddit where the sole moderator has announced their intention to "repurpose" the subreddit a week from now.

There is an active community in the subreddit and they don't want this to happen. The moderator doesn't seem interested in what the community's wishes are.

Is a moderator allowed to do that? It seems wrong, but I couldn't find specific wording in the Moderator Code of Conduct that seems like it would apply to this specific situation.

Is the best recourse filing a report of a Moderator Code of Conduct violation? Not sure if it's against the rules. If it is against the rules, not sure which rule or what the best recourse is.

Thanks for any help you can provide.

---

Edited to add some important details:

Strangely, the moderator has not clearly said what the planned change is or isn't. They have intimated they might shut down the subreddit completely. Reading between the lines, it seems like they just want to try to close the subreddit. Otherwise, they have just said the subreddit will be "repurposed", and they themselves put "repurposed" in quotes, as if it's a euphemism.

They have not outright said they want to shut down the subreddit, but they've said the subreddit will soon cease to serve any purpose. They specified a contingency in which the subreddit would unexpectedly remain open, which seems to imply that, barring that contingency, the subreddit will be closed.

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Edit #2:

The question has been mostly resolved, see comments here.

Created by u/Terrible-Computer-12 a.k.a. Randy Whitlock, who also created the Threedom animated videos

Archive Team scrapes websites and uploads them to the Internet Archive. If a website is shutting down, you can ask them to scrape it.

Archive Team communicates and coordinates via IRC only, not Reddit (or anywhere else): https://wiki.archiveteam.org/index.php/Archiveteam:IRC

Instructions for requesting help from Archive Team:

Step 1. Go here and set up The Lounge for IRC: https://www.pikapods.com/apps#chat Don’t worry, you don’t actually need to pay or put in a credit card. You will get over 2 months’ worth of free credits. You'll need to set a username and password for The Lounge when you set up your PikaPod. 

(You can use a free desktop IRC client like KVIrc, but this will make your IP address public and available forever in public logs. So, use a free VPN like ProtonVPN or RiseupVPN if you go with that option.)

Step 2. Go to the Archive Team IRC channels. Archive Team operates exclusively on IRC. Here’s the info: https://wiki.archiveteam.org/index.php/Archiveteam:IRC The network is Hackint and the channel you want is #archiveteam-bs (if you need step-by-step instructions for using IRC, look up a guide online or ask an AI chatbot)

Step 3. In a brief message, explain what's happening and ask the Archive Team volunteers for help. Mention ArchiveBot for most websites or Wikibot for wikis.

Step 4. Wait. You might not get a response right away. If no one replies after two hours, try again.

Archive Team scrapes websites and uploads them to the Internet Archive. If a website is shutting down, you can ask them to scrape it.

Archive Team communicates and coordinates via IRC only, not Reddit (or anywhere else): https://wiki.archiveteam.org/index.php/Archiveteam:IRC

Instructions for requesting help from Archive Team:

Step 1. Go here and set up The Lounge for IRC: https://www.pikapods.com/apps#chat Don’t worry, you don’t actually need to pay or put in a credit card. You will get over 2 months’ worth of free credits. You'll need to set a username and password for The Lounge when you set up your PikaPod. 

(You can use a free desktop IRC client like KVIrc, but this will make your IP address public and available forever in public logs. So, use a free VPN like ProtonVPN or RiseupVPN if you go with that option.)

Step 2. Go to the Archive Team IRC channels. Archive Team operates exclusively on IRC. Here’s the info: https://wiki.archiveteam.org/index.php/Archiveteam:IRC The network is Hackint and the channel you want is #archiveteam-bs (if you need step-by-step instructions for using IRC, look up a guide online or ask an AI chatbot)

Step 3. In a brief message, explain what's happening and ask the Archive Team volunteers for help. Mention ArchiveBot for most websites or Wikibot for wikis.

Step 4. Wait. You might not get a response right away. If no one replies after two hours, try again.

We are living in a world that is nearly archival utopia. 200 years ago, we didn’t have photography, recorded video, or recorded speech or music. Now we have an almost unlimited amount. Anyone can publish text effortlessly. Look at this Reddit post for example. 200 years ago, how would I have published a text like this and distributed it to people? 

If we lose more information now than ever, it’s only because we produce vastly more information than ever. The fraction that we lose perhaps dwarfs the total information production of eras past, but the amount preserved is far larger still. Our expectations have risen to the point where we think everything should be preserved because technology has advanced to the point where that is finally possible. This is not doom or darkness. This is an astonishing victory for human civilization. 

What’s also remarkable is how far we’ve pushed audiovisual media. CD-quality FLAC audio seems to be at about the limit of what the human ear can hear. (In fact, the human ear might not even be able to hear the difference between FLAC audio and a 320 kbps mp3.) Apparently, companies that test displays with higher than 4K resolution are finding that people don’t notice much, if any, difference between 4K and 8K or higher. Might we be getting close to the limit of how good things can look and sound?

One of the few missing pieces is cheap, high-capacity, ultra-long-term archival storage media. M-Discs are one of the few attempts to offer this in a consumer technology. piqlFilm is a promising technology, but at roughly $30 per gigabyte it’s far pricier than even M-Discs. Microsoft’s Project Silica could be the Holy Grail we’re all looking for, but it’s still in R&D and may never become a commercial technology. 

Even so, we should put in perspective what we’re chasing after. We’re looking for mass data storage on a scale and at a cost that has never existed before in human history. It’s always been possible to carve writing in stone or clay. It’s never before been realistic to imagine putting petabytes of knowledge and culture in a storage medium that lasts for centuries or millennia. 

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There is no secret conspiracy to destroy information. When there is an attempt to destroy information, it is typically brazen and obvious. In 2025, when the Trump administration was purging government websites for misguided ideological reasons (and perhaps partly also out of plain incompetence), everyone knew exactly what was happening and why. It was not like a cat burglar stealing jewels in the night. It was like a bank robbery in broad daylight with the silent alarm going and panicked tellers behind the counter. 

Economic trends and trends in consumer technology virtually always happen for obvious, logical reasons. Companies seek to maximize profit. Investors seek to maximize ROI. Consumers seek to maximize convenience and minimize cost. There is no spooky, shadowy “they” behind the curtain, directing the show. It’s just business. 

Corporations do things that are bad for consumers, but when they do, it’s for perfectly obvious and logical reasons. It’s not a secret. It’s not more complicated than the incentives of profit, ROI, competitiveness, long-term business strategy, and so on. When companies seek to combat piracy, for example, they aren’t trying to destroy culture or history to advance some grand, mysterious agenda. They are trying to stop people from ripping off their product so they can sell it and make money. 

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Professional archivists and librarians do most of the important work. Archival and librarianship is like anything else. Professionals do it better. When you go to school to study something for years and then get years of work experience, you do it better than when you have no knowledge and no experience. Obvious, right? Professional pilots fly better, professional chefs cook better, professional musicians sing better, professional coders code better, professional nurses stitch wounds better. It’s that simple.

Actually, it’s not just about individuals but institutions. Institutions are more than the sum of their parts. Institutions institute accountability, teamwork, and best practices that emerge from decades of experience. It’s easier to follow annoying protocols that exist for good reasons if you’ll get in trouble if you don’t. A colleague’s second pair of eyes can catch mistakes or make improvements. We have institutions because they’re so much better than individuals working alone. 

Professionals also have funding. That means people can work full-time on this stuff. 

Amateurs and hobbyists fill some gaps. Retro game preservation benefits a lot from a large population of enthusiastic hobbyists. Sometimes collectors have the only copy left of a film. Archive Team does incredible work saving the web. 

Archive Team is so impressive, in fact, I feel “amateur” isn’t the right word for them. Not professional, but also a notch above the typical hobbyist. Also, Archive Team is not exactly an institution, but it is, well, a team. It has rules, procedures, cooperation, and teamwork. 

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When it comes to information, perhaps our scarcest resource is attention. Deletion may be as important as downloading. 

Google describes its mission “to organize the world's information and make it universally accessible and useful.” I always thought that was a good mission statement. The “organize” and “make it useful” parts are important but easy to forget. 

One of the threats to information is other information. If you have 10,000 photos named IMG_0001 through IMG_9999 and you only really care about finding one specific photo, the other 9,999 photos might make you just give up. Maybe it’s not quite as bad as the photo getting deleted, but it may lead to the same practical outcome. 

We can compensate a lot for disorganization with good tools. Search, thumbnails, AI classification, metadata. But the best tools we currently have can’t completely compensate for the levels of disorganization that many (most?) computer users live with. Downloading a lot more files can make the problem worse.

If you’re looking for one photo in a sea of other photos, in a way you’re lucky. Sometimes you don’t even know what you want. Let’s say you have 1,000 music albums collected over years. Which ones do you actually like? Can you remember? If you acquire them indiscriminately and keep everything, now you have to keep track of that in your head. Or you forget. What data we keep and delete is a cue to what we think is important. That meta-information is lost when we just accumulate whatever.  

Information overload is probably a bigger problem today than information scarcity. Knowing what information to pay attention to is a bigger problem than whether you have enough of the sort of information you want or need. Adding more information to the mix makes this worse, not better. 

Maybe what we need, then, is not data hoarding, but data curation, data librarianship, data archiving with aggressive pruning. Maybe half the work is figuring out what’s important and what’s not. 

Torrents make it easy to share massive amounts of data (even up to petabytes) and unlimited numbers of files. It just requires people to persistently seed the torrents.

How to make a torrent:

1. Use qBittorrent: https://www.qbittorrent.org/download

2. Follow this guide to create a torrent using qBittorrent: https://www.wikihow.com/Create-a-Torrent

3. When creating the torrent, add this tracker: https://opentrackr.org/ (go to the site and copy the tracker URL)

4. For good measure, add this tracker too: https://stealth.si/ (go to the site and copy the tracker URL)

5. Start seeding the torrent you created.

6. In qBittorrent, right-click the torrent --> Copy --> Magnet link

7. Share the magnet link on r/DHexchange.

8. If you need a VPN to hide your IP address while seeding the torrent, RiseupVPN is free and allows torrenting: https://riseup.net/en/vpn ProtonVPN is a good option if you don’t mind paying for a VPN: https://protonvpn.com/

Aren’t the Addams family and the Manson family both really bad? Is this a standup joke Paul and Scott heard one time and think is funny because it makes no sense? Is that the joke?

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Edit: I don't think this joke is original to Paul or Scott. I assume it's a joke they heard somewhere one time and like to repeat because it doesn't make sense. Either that or the original joke did make sense and said something like "the Partridge family" instead of "the Manson family" and they've just mistakenly slipped into saying "the Manson family". But I really think it's more likely the first one.

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Edit 2: Two different commenters explained the joke’s origins here and here.