I hate how much I agree with you in principle and how ugly it looks in practice. With doubled periods, at least - different marks don’t trigger that same reaction. For example, a question mark inside, followed by a period or comma outside feels right.

It’s not grammatically incorrect to end a sentence with a preposition. It’s a common misconception that it is a rule, basically because one guy argued in favor of it back in the 1600s and had some support for formal writing in the 1700s. But it’s never been a broad rule, and even in formal contexts it’s not a rule in any current, reputable style or usage guides (so far as I know, at least).

Some more info on the topic: https://www.merriam-webster.com/grammar/prepositions-ending-a-sentence-with

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:sallypass@ds099999.mongolab.com: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.

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

PSTN is wiretapped.

It’s a good thing that the website itself supports sending and receiving alerts, then.

When did Democrats have a supermajority in both the House and the Senate?

I thought Hue bulbs used Zigbee?

The up arrow moves through the letters, e.g., A->B->C. The down arrow moves to the next character in the sequence, e.g., C->CA->CAA. If you click past the correct letter, you’ll have to click all the way through again. And if you submit the wrong letter, you have to start all over (after it takes twenty seconds attempting to connect with the wrong password and then alerts you that it didn’t work, of course).

Fair point, I should have asked about commercial games in general

That said I didn’t mean that the game studio itself would do the AI training and own their models in-house; if they did, I’d expect it to go just as poorly as you would. Rather, I’d expect the model to be created by an organization specialized in that sort of thing.

For example, “Marey” is one example I found of a GenAI model that its creators are saying was trained ethically.

Another is Adobe Firefly, where Adobe says they trained only on licensed and public domain content. It also sounds like Adobe is paying the artists whose content was used for AI training. I believe that Canva is doing something similar.

StabilityAI is also doing something similar with Stable Audio 2.0, where they partnered with a music licensing company, AudioSparx, to ensure that artists are compensated, AI opt outs are respected, etc…

I haven’t dug into any of those too deep, but they seem to be heading in the right direction at the surface level, at least.

One of the GenAI scenarios that’s the most terrifying to me is the idea of a company like Disney using all the material they have copyright for to train their own, proprietary GenAI image, audio, and video tools… not because I think the outputs would be bad, but because of the impact that would have on creators in that industry.

Fortunately, as long as copyright doesn’t apply to purely AI generated outputs, even if trained entirely on your own content, then I don’t think Disney specifically will do this.

I mention that as an example because that usage of AI, regardless of how ethically the model was trained, would still be unethical, in my opinion. Likewise in game creation, an ethically trained and operated model could still be used unethically to eliminate many people’s jobs in the interest solely of better profits.

I’d be on board with AI use (in game creation or otherwise) if a company were to say, “We’re not changing the budget we have for our human workforce, including for contractors, licensed art, and so on, other than increasing it as inflation and wages increase. We will be using ethical AI models to create more content than we otherwise would have been able to.” But I feel like in a corporate setting, its use is almost always going to result in them cutting jobs.

Are you okay with AAA studios using GenAI that was trained only on licensed works?

Depends on your e-reader! If you have a Kindle, Kobo, or Nook, yes, that’s true. However:

Boox has e-readers that run Android and you can install Hoopla. The Palma 2 is phone sized which is great. The Page, Leaf2, and Go 7 are all in the 7” form factor, plus they have 6” versions. And they have tablet sizes, too. They have both traditional black&white and color e-ink displays.

I have the Boox Air 3C and the original Palma and both are great. I’ll likely get a Boox as my next standard sized e-reader, too (whenever I replace my Kindle Oasis). Though unless the technology drastically improves before then, it’ll be one with a black and white screen. (The color is nice in the tablet sizes, though, especially for comics from Hoopla.)

Some other options that I’m less familiar with include:

• Bigme has Android 7” color e-readers, as well as tablets and e-ink smartphones.
• Meebook has e-readers that run Android (and Android e-ink tablets)
• The MuSnap Aura C is a 10” Android e-ink tablet
• XPPen has an 11” Android e-ink tablet