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Cake day: July 14th, 2023

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  • 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.



  • From the blog post referenced:

    We do not provide evidence that:

    AI systems do not currently speed up many or most software developers

    Seems the article should be titled “16 AI coders think they’re 20% faster — but they’re actually 19% slower” - though I guess making us think it was intended to be a statistically relevant finding was the point.

    That all said, this was genuinely interesting and is in-line with my understanding of the human psychology that’s at play. It would be nice to see this at a wider scale, broken down across different methodologies / toolsets and models.








  • 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

  • The products currently on the marketplace have architectures that are far more sophisticated than just an LLM. Even something as simple as “Deep Research,” which both Anthropic and Claude have available, is using multiple interconnected systems to provide a single response.

    Consider Agentic AI, like Claude Code, where they’re using tools, analyzing the results of those tools, iterating, possibly calling out to MCP servers to do other things, etc… The tools allow them to do things like read or modify files in the working directory, execute programs (i.e., your linter, installing dependencies, running your app), querying against your app itself, and so on.

    And of course note that the single “Claude” box in that diagram has an architecture that’s more sophisticated than just being an LLM. At minimum, consumer facing LLMs generally have a supervisor that censors problematic inputs and outputs; this doesn’t make the system more competent but the same concept can be applied to any other sort of transparent wrapper.

    It seems to me that we already have consumer systems that are doing what you described, and we’re already working on enhancing their architectures further.




  • I think the best way to handle this would be to just encode everything and upload all files. If I wanted some amount of history, I’d use some file system with automatic snapshots, like ZFS.

    If I wanted to do what you’ve outlined, I would probably use rclone with filtering for the extension types or something along those lines.

    If I wanted to do this with Git specifically, though, this is what I would try first:

    First, add lossless extensions (*.flac, *.wav) to my repo’s .gitignore

    Second, schedule a job on my local machine that:

    1. Watches for changes to the local file system (e.g., with inotifywait or fswatch)
    2. For any new lossless files, if there isn’t already an accompanying lossy files (i.e., identified by being collocated, having the exact same filename, sans extension, with an accepted extension, e.g., .mp3, .ogg - possibly also with a confirmation that the codec is up to my standards with a call to ffprobe, avprobe, mediainfo, exiftool, or something similar), it encodes the file to your preferred lossy format.
    3. Use git status --porcelain to if there have been any changes.
    4. If so, run git add --all && git commit --message "Automatic commit" && git push
    5. Optionally, automatically craft a better commit message by checking which files have been changed, generating text like Added album: "Satin Panthers - EP" by Hudson Mohawke or Removed album: "Brat" by Charli XCX; Added album "Brat and it's the same but there's three more songs so it's not" by Charli XCX

    Third, schedule a job on my remote machine server that runs git pull at regular intervals.

    One issue with this approach is that if you delete a file (as opposed to moving it), the space is not recovered on your local or your server. If space on your server is a concern, you could work around that by running something like the answer here (adjusting the depth to an appropriate amount for your use case):

    git fetch --depth=1
    git reflog expire --expire-unreachable=now --all
    git gc --aggressive --prune=all
    

    Another potential issue is that what I described above involves having an intermediary git to push to and pull from, e.g., running on a hosted Git forge, like GitHub, Codeberg, etc… This could result in getting copyright complaints or something along those lines, though.

    Alternatively, you could use your server as the git server (or check out forgejo if you want a Git forge as well), but then you can’t use the above trick to prune file history and save space from deleted files (on the server, at least - you could on your local, I think). If you then check out your working copy in a way such that Git can use hard links, you should at least be able to avoid needing to store two copies on your server.

    The other thing to check out, if you take this approach, is git lfs. EDIT: Actually, I take that back - you probably don’t want to use Git LFS.



  • You can run a NAS with any Linux distro - your limiting factor is having enough drive storage. You might want to consider something that’s great at using virtual machines (e.g., Proxmox) if you don’t like Docker, but I have almost everything I want running in Docker and haven’t needed to spin up a single virtual machine.


  • You don’t have to finish the file to share it though, that’s a major part of bittorrent. Each peer shares parts of the files that they’ve partially downloaded already. So Meta didn’t need to finish and share the whole file to have technically shared some parts of copyrighted works. Unless they just had uploading completely disabled,

    The argument was not that it didn’t matter if a user didn’t download the entirety of a work from Meta, but that it didn’t matter whether a user downloaded anything from Meta, regardless of whether Meta was a peer or seed at the time.

    Theoretically, Meta could have disabled uploading but not blocked their client from signaling that they could upload. This would, according to that argument, still counts as reproducing the works, under the logic that signaling that it was available is the same as “making it available.”

    but they still “reproduced” those works by vectorizing them into an LLM. If Gemini can reproduce a copyrighted work “from memory” then that still counts.

    That’s irrelevant to the plaintiff’s argument. And beyond that, it would need to be proven on its own merits. This argument about torrenting wouldn’t be relevant if LLAMA were obviously a derivative creation that wasn’t subject to fair use protections.

    It’s also irrelevant if Gemini can reproduce a work, as Meta did not create Gemini.

    Does any Llama model reproduce the entirety of The Bedwetter by Sarah Silverman if you provide the first paragraph? Does it even get the first chapter? I highly doubt it.

    By the same logic, almost any computer on the internet is guilty of copyright infringement. Proxy servers, VPNs, basically any compute that routed those packets temporarily had (or still has for caches, logs, etc) copies of that protected data.

    There have been lawsuits against both ISPs and VPNs in recent years for being complicit in copyright infringement, but that’s a bit different. Generally speaking, there are laws, like the DMCA, that specifically limit the liability of network providers and network services, so long as they respect things like takedown notices.


  • Why should we know this?

    Not watching that video for a number of reasons, namely that ten seconds in they hadn’t said anything of substance, their first claim was incorrect (Amazon does not prohibit use of gen ai in books, nor do they require its use be disclosed to the public, no matter how much you might wish it did), and there was nothing in the description of substance, which in instances like this generally means the video will largely be devoid of substance.

    What books is the Math Sorcerer selling? Are they the ones on Amazon linked from their page? Are they selling all of those or just promoting most of them?

    Why do we think they were generated with AI?

    When you say “generated with AI,” what do you mean?

    • Generated entirely with AI, without even editing? Then why do they have so many 5 star reviews?
    • Generated with AI and then heavily edited?
    • Written partly by hand with some pieces written by unedited GenAI?
    • Written partly by hand with some pieces written by edited GenAI?
    • AI was used for ideation?
    • AI was used during editing? E.g., Grammarly?
    • GenAI was used during editing?E.g., “ChatGPT, review this chapter and give me any feedback. If sections need rewritten go ahead and take a first pass.”
    • AI might have been used, but we don’t know for sure, and the issue is that some passages just “read like AI?”

    And what’s the result? Are the books misleading in some way? That’s the most legitimate actual concern I can think of (I’m sure the people screaming that AI isn’t fair use would disagree, but if that’s the concern, settle it in court).


  • Further, “Whether another user actually downloaded the content that Meta made available” through torrenting “is irrelevant,” the authors alleged. “Meta ‘reproduced’ the works as soon as it made them available to other peers.”

    Is there existing case law for what making something “available” means? If I say “Alright, I’ll send you this book if you want, just ask,” have I made it available? What if, when someone asks, I don’t actually send them anything?

    I’m thinking outside of contexts of piracy and torrenting, to be clear - like if a software license requires you to make any changed versions available to anyone who uses the software. Can you say it’s available if your distribution platform is configured to prevent downloads?

    If not, then why would it be any different when torrenting?

    Meta ‘reproduced’ the works as soon as it made them available to other peers.

    The argument that a copyrighted work has been reproduced when “made available,” when “made available” has such a low bar is also perplexing. If I post an ad on Craigslist for the sale of the Mona Lisa, have I reproduced it?

    What if it was for a car?

    I’m selling a brand new 2026 Alfa Romeo 4E, DM me your offers. I’ve now “reproduced” a car - come at me, MPAA.