You don’t have a moral or ethical obligation to respect their terms, but I wouldn’t go too wild with it, as using a ton of data might get noticed and fixed, causing someone else who’s benefiting from this and who can’t afford a replacement setup to lose it.

"I think we should have, like, the same concept of privacy for your conversations with AI

Step 1: Don’t use ChatGPT or other cloud AI services

Step 2: Use AI locally within FOSS applications, or not at all

Certainly the latter.

I have pretty decent insurance through work, but if I’m picking up a prescription, it’s cheaper for me to say I don’t have insurance and use a free discount card (like GoodRx) than to use my insurance. We’re talking $150-$200 for one prescription (a one month supply) with insurance vs $30 without.

To be fair, I have an HDHP with an HSA so my insurance is only supposed to negotiate a discount until I hit the deductible, rather than paying for it. Full price is $200-$250, I think? (I get generics and each generic variant has a slightly different price.) So technically they’re providing a discount, just not a very good one.

Insurance also likes to require a “prior authorization,” which was always a fun surprise after making it through the pharmacy line. That normally takes a couple days to resolve, at minimum, and sometimes longer. If you’re not familiar with prior auths, it’s basically when the insurance company says “Hey doc, can you justify why you’re prescribing this and answer these eight questions?” and then they have someone without a medical degree review the answer and see if it’s good enough.

The only downside to paying out of pocket with a discount card is that the $30 doesn’t go toward my deductible. But since my deductible is multiple thousands of dollars, unless something else happens during the year, I won’t hit my deductible off the $150-$200 prescriptions + regular doctor visits alone. But that’s at most $360 out of pocket that wouldn’t have gone toward the deductible, assuming I had a health crisis in December, vs $1440-$2040 saved if I don’t.

X-rays are even worse, because you’re not told the price ahead of time.

I’m a millennial and I did it more than once on hardware older than I was, but because I wanted to, not because there were no other options.

Illegal vote suppression elected Trump, but even if it hadn’t, you should blame Democrats before blaming people who voted for third party candidates. Now, if you’re talking about people who “protest voted” by voting for Trump (in both the primaries and the election), then sure. Those people did, in fact, play an instrumental part in electing him.

Why blame Democrats? Well, beyond just kinda being Republican-lites:

• for opposing ranked choice voting (and alternatives)
• for not rallying around progressive candidates
• for not choosing Kamala via primary elections in 2024

Democrats are the bare minimum “harm reduction” party, and I don’t bare any ill will toward people who voted for them rather than a party that would actually try to effect change, but the opposite mindset - blaming third party voters for not voting for Democrats - is very shortsighted. And as third party voters have never had the power to enact RCV or STAR voting or otherwise improve the system, blaming them instead of the Democrats who have had that power is inane.

I’ve voted for a Democrat every single presidential election that I’ve been able to, but I honestly wish I hadn’t. I’d much rather there be more visibility for third parties, and for more people to feel empowered to vote for third party candidates.

I don’t use my Windows 10 desktop a ton, but I’ve definitely gotten the full page “Update to Windows 11” screen a few times, and it has Windows 10 Pro installed.

Sure, just be aware that unripe tomatoes (not just the stems) are also toxic to cats.

You can’t just consider the cheese! You gotta look up all the ingredients!

• https://www.cancateat.food/food/bread
• https://www.cancateat.food/food/lettuce
• https://www.cancateat.food/food/tomato
• https://www.cancateat.food/food/mayonnaise
• https://www.cancateat.food/food/beef
• https://www.cancateat.food/food/cheese

Consensus: hold the tomato! Otherwise, if there’s no seasoning, everything else is acceptable in small amounts.

Glaring doesn’t imply a negative meaning. In this case it’s used to mean “obvious”.

Unless you’re suggesting that “glaring” means “obviously staring” (it doesn’t - that would be “glaringly staring”) this doesn’t make any sense.

“[He’s] glaring at [direct object]” is an example of a sentence that uses the present participle form of the verb “glare,” which explicitly communicates anger or fierceness.

If you’re not convinced, read on.

—————

The verb form that takes an object is:

Glare (verb with object): to express with a glare. They glared their anger at each other

The noun form the above definition references is:

Glare (noun): a fiercely or angrily piercing stare.

“Glaring” can be an adjective and one of those definitions does mean “obvious” or “conspicuous,” but the use of that form of the word doesn’t make sense in her sentence. Think about a comparable sentence like “The undercover operative is conspicuous at the bar,” where the bar is the location. (Even then, most people wouldn’t use “glaring” in that sentence, as “conspicuous” or “obvious” are much less ambiguous; the operative could be staring piercingly or angrily at the bar rather than being glaring while being at the bar.) Another example that makes a bit more sense is “The effect of the invasive plants is glaring at the park.”

But for that interpretation to be valid here, you’d have to:

• believe that the dude is trying to hide/blend in, or otherwise explain how he - not what he’s doing, but the dude himself - is conspicuous
• believe that the woman’s referring to her own ass as a location
• assume that she isn’t commenting on how the guy is looking at her ass, even though the joke depends on giving him something different to look at

That’s a bit of a stretch.

This is what I would try first. It looks like 1337 is the exposed port, per https://github.com/nightscout/cgm-remote-monitor/blob/master/Dockerfile

x-logging:
  &default-logging
  options:
    max-size: '10m'
    max-file: '5'
  driver: json-file

services:
  mongo:
    image: mongo:4.4
    volumes:
      - ${NS_MONGO_DATA_DIR:-./mongo-data}:/data/db:cached
    logging: *default-logging

  nightscout:
    image: nightscout/cgm-remote-monitor:latest
    container_name: nightscout
    restart: always
    depends_on:
      - mongo
    logging: *default-logging
    ports:
      - 1337:1337
    environment:
      ### Variables for the container
      NODE_ENV: production
      TZ: [removed]

      ### Overridden variables for Docker Compose setup
      # The `nightscout` service can use HTTP, because we use `nginx` to serve the HTTPS
      # and manage TLS certificates
      INSECURE_USE_HTTP: 'true'

      # For all other settings, please refer to the Environment section of the README
      ### Required variables
      # MONGO_CONNECTION - The connection string for your Mongo database.
      # Something like mongodb://sally:[email protected]:99999/nightscout
      # The default connects to the `mongo` included in this docker-compose file.
      # If you change it, you probably also want to comment out the entire `mongo` service block
      # and `depends_on` block above.
      MONGO_CONNECTION: mongodb://mongo:27017/nightscout

      # API_SECRET - A secret passphrase that must be at least 12 characters long.
      API_SECRET: [removed]

      ### Features
      # ENABLE - Used to enable optional features, expects a space delimited list, such as: careportal rawbg iob
      # See https://github.com/nightscout/cgm-remote-monitor#plugins for details
      ENABLE: careportal rawbg iob

      # AUTH_DEFAULT_ROLES (readable) - possible values readable, denied, or any valid role name.
      # When readable, anyone can view Nightscout without a token. Setting it to denied will require
      # a token from every visit, using status-only will enable api-secret based login.
      AUTH_DEFAULT_ROLES: denied

      # For all other settings, please refer to the Environment section of the README
      # https://github.com/nightscout/cgm-remote-monitor#environment

To run it with Nginx instead of Traefik, you need to figure out what port Nightscout’s web server runs on, then expose that port, e.g.,

services:
  nightscout:
    ports:
      - 3000:3000

You can remove the labels as those are used by Traefik, as well as the Traefik service itself.

Then just point Nginx to that port (e.g., 3000) on your local machine.

—-

Traefik has to know the port, too, but it will auto detect the port that a local Docker service is running on. It looks like your config is relying on that feature as I don’t see the label that explicitly specifies the port.

JustWatch is still useful if you want to act like you watched it legitimately, e.g., if a coworker asks where they can watch it. Even if your coworker also pirates, they might not have an account on your private tracker, Usenet, etc…

I may be wrong, as I haven’t actually torrented anything substantial since Demonoid was still a thing, but it all feels less accessible than it used to be.

It’s not “dark green,” that’s for sure.

There’s a difference between an answer from ChatGPT and an answer from ChatGPT that’s been reviewed by a person, particularly if that person is knowledgeable of the topic. ChatGPT isn’t deterministic, so if I go and ask ChatGPT the same thing, there’s no guarantee I’ll get an at all similar answer.

The problem for me is that I have no way of knowing whether the person posting the ChatGPT response is or isn’t an expert and whether they actually reviewed the output. However that’s true of people in general, just replace “reviewing the output” with “not trolling,” so the effort to assess the utility of a comment is pretty similar.

https://knowyourmeme.com/memes/inb4--2

Ars points out that these findings contradict those of other experiments and then goes on to postulate as to why. I clicked on the link to the other experiment:

when data is combined across three experiments and 4,867 developers, our analysis reveals a 26.08% increase (SE: 10.3%) in completed tasks among developers using the AI tool

By comparison, this experiment considered 16 developers. That’s 0.3% as many as the experiments its findings contradict. Fortunately, the authors don’t claim their findings are broadly applicable. They even have a table that reads:

We do not provide evidence that | Clarification —- | —- AI systems do not currently speed up many or most software developers | We do not claim that our developers or repositories represent a majority or plurality of software development work AI systems do not speed up individuals or groups in domains other than software de- velopment | We only study software development AI systems in the near future will not speed up developers in our exact setting | Progress is difficult to predict, and there has been substantial AI progress over the past five years [2] There are not ways of using existing AI systems more effectively to achieve positive speedup in our exact setting | Cursor does not sample many tokens from LLMs, it may not use optimal prompting/scaffolding, and domain/repository-specific training/finetuning/few-shot learning could yield positive speedup

That said, the study has been an interesting read so far. I highly recommend reading it directly rather than just the news posts about it. Check out their own blog post: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/

I personally find the psychological effect - the devs thought they were 20% faster even afterward - to be pretty interesting, as it suggests that even if more time overall is spent, use of AI could reduce cognitive load and potentially side effects like burnout.

I’d like to see much larger scale studies set up like this, as well as studies of other real world situations. For example, how does this affect the amount of time this takes 10,000 different developers to onboard onto an unfamiliar repository?

There’s a whole history of people, both inside and outside the field, shifting the definition of AI to exclude any problem that had been the focus of AI research as soon as it’s solved.

Bertram Raphael said “AI is a collective name for problems which we do not yet know how to solve properly by computer.”

Pamela McCorduck wrote “it’s part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, but that’s not thinking” (Page 204 in Machines Who Think).

In Gödel, Escher, Bach: An Eternal Golden Braid, Douglas Hofstadter named “AI is whatever hasn’t been done yet” Tesler’s Theorem (crediting Larry Tesler).

https://praxtime.com/2016/06/09/agi-means-talking-computers/ reiterates the “AI is anything we don’t yet understand” point, but also touches on one reason why LLMs are still considered AI - because in fiction, talking computers were AI.

The author also quotes Jeff Hawkins’ book On Intelligence:

Now we can see the entire picture. Nature first created animals such as reptiles with sophisticated senses and sophisticated but relatively rigid behaviors. It then discovered that by adding a memory system and feeding the sensory stream into it, the animal could remember past experiences. When the animal found itself in the same or a similar situation, the memory would be recalled, leading to a prediction of what was likely to happen next. Thus, intelligence and understanding started as a memory system that fed predictions into the sensory stream. These predictions are the essence of understanding. To know something means that you can make predictions about it. …

The human cortex is particularly large and therefore has a massive memory capacity. It is constantly predicting what you will see, hear, and feel, mostly in ways you are unconscious of. These predictions are our thoughts, and, when combined with sensory input, they are our perceptions. I call this view of the brain the memory-prediction framework of intelligence.

If Searle’s Chinese Room contained a similar memory system that could make predictions about what Chinese characters would appear next and what would happen next in the story, we could say with confidence that the room understood Chinese and understood the story. We can now see where Alan Turing went wrong. Prediction, not behavior, is the proof of intelligence.

Another reason why LLMs are still considered AI, in my opinion, is that we still don’t understand how they work - and by that, I of course mean that LLMs have emergent capabilities that we don’t understand, not that we don’t understand how the technology itself works.

We are. Why do you think we stopped?

It may be aware of them, but not in that context. If you asked it how to solve the problem rather than to solve the problem for you, there’s a chance it would suggest you use a reverse image search.

LLM image processing doesn’t work the same way reverse image lookup does.

Tldr explanation: Multimodal LLMs turn pictures into a thousand 200-500 or so words tokens, but reverse image lookups create perceptual hashes of images and look the hash of your uploaded image up in a database.

Much longer explanation:

Multimodal LLMs (technically, LMMs - large multimodal models) use vision transformers to turn images into tokens. They use tokens for words, too, but these tokens don’t also correspond to words. There are multiple ways this could be implemented, but a common approach is to break the image down into a grid, then transform each “patch” of a specific size, e.g., 16x16, into a single token. The patches aren’t transformed individually - the whole image is processed together, in context - but it still comes out of it with basically 200 or so tokens that allow it to respond to the image, the same way it would respond to text.

Current vision transformers also struggle with spatial awareness. They embed basic positional data into the tokens but it’s fragile and unsophisticated when it comes to spatial awareness. Fortunately there’s a lot to explore in that area so I’m sure there will continue to be improvements.

One example improvement, beyond improved spatial embeddings, would be to use a dynamic vision transformers that’s dependent on the context, or that can re-evaluate an image based off new information. Outside the use of vision transformers, simply training LMMs to use other tools on images when appropriate can potentially help with many of LMM image processing’s current shortcomings.

Given all that, asking an LLM to find the album for you is like - assuming you’ve given it the ability and permission to search the web - like showing the image to someone with no context, then them to help you find what music video - that they’ve never seen, by an artist whose appearance they describe with 10-20 generic words, none of which are their name - it’s in, and to hope there were, and that they remembered, the specific details that would make it would come up in the top ten results if searched for on Google. That’s a convoluted way to say that it’s a hard task.

By contrast, reverse image lookup basically uses a perceptual hash generated for each image. It’s the tool that should be used for your particular problem, because it’s well suited for it. LLMs were the hammer and this problem was a torx screw.

Suggesting you use - or better, using a reverse image lookup tool itself - is what the LLM should do in this instance. But it would need to have been trained to think to suggest this, capable of using a tool that could do the lookup, and have both access and permission to do the lookup.

Here’s a paper that might help understand the gaps between LMMs and tasks built for that specific purpose: https://arxiv.org/html/2305.07895v7