hedgehog

I believe you set env vars on Windows through System Properties -> Advanced -> Environment Variables.

I believe you just need to set the env var OLLAMA_HOST to 0.0.0.0:11434 and then restart Ollama.

What OS is your server running? Do you have an Android phone or an iPhone?

In either case all you likely need to do is expose the port and then access your server by IP on that port with an appropriate client.

In Ollama you can expose the port to your local network by changing the bind address from 127.0.0.1 to 0.0.0.0

Regarding clients: on iOS you can use Enchanted or Apollo to connect to Ollama.

On Android there are likely comparable apps.

Please, enlighten me - how do you propose we use the term “AI” in a way that’s more useful than a definition that includes machine learning, large language models, and computer vision?

I doubt I’ll agree with your definition, but I’m curious to see how you would exclude machine learning, computer vision, LLMs, etc., from your definition. My assumption is that your definition is going to be either a derivative of “AI is anything computers can’t do yet” or based on pop culture / sci fi, but maybe you’ll surprise me.

To be clear, I’m a software engineer; I’m not speaking in sales speak. I’ve derived my understanding of the term from a combination of its historical context and how it’s used in both professional and academic contexts, not from marketing propaganda or from sci fi and pop culture. I’m certainly aware of the hype machine that’s ongoing, but there are also tons of fascinating advancements happening on a regular basis, and the term “AI” is at minimum a useful term to refer to technologies that leverage similar techniques.

it's not 'ai', it's just a poorly trained voice recognition system that's trying to decipher any random person's voice.

I’m baffled that you can say “It’s not ‘AI,’ it’s a machine learning powered speech to text system” with a straight face.

Even if we were to agree that ML-powered speech to text isn’t AI (and I don’t agree to that premise, for the record), there’s still the matter of processing the transcription to transform it into something that can be understood by the point of sale system - aka natural language processing. And while that NLP could be implemented without use of an LLM, given LLM’s current level of hype and the ease with which they can be shoved into any given product, I wouldn’t bet on Taco Bell execs approving such an approach, much less asking for it.

For reference, Anthropic is currently valued at $183 Billion and their annual recurring revenue is currently $5 Billion (up from $1 Billion in 2024). So this will cost them roughly 30% of their current annual revenue.

I’m a professional software engineer and I’ve been in the industry since before Kubernetes was first released, and I still found it overwhelming when I had to use it professionally.

I also can’t think of an instance when someone self-hosting would need it. Why did you end up looking into it?

I use Docker Compose for dozens of applications that range in complexity from “just run this service, expose it via my reverse proxy, and add my authentication middleware” to “in this stack, run this service with my custom configuration, a custom service I wrote myself or forked, and another service that I wrote a Dockerfile for; make this service accessible to this other service, but not to the reverse proxy; expose these endpoints to the auth middleware and for these endpoints, allow bypassing of the auth middleware if an API key is supplied.” And I could do much more complicated things with Docker if I needed to, so even for self-hosters with more complex use cases than mine, I question whether Kubernetes is the right fit.

Summary of my comment: the study showed that the AI tool in question was an effective tool for the task, nothing more.

I didn’t read this particular article, but I recently read a different one about the same study. I also clicked into the study itself and read the abstract and everything else that was freely available. The study was paywalled, but as far as I could tell:

• Performance immediately displayed a sustained increase of 24% relative to baseline while using the AI tool in question
• Immediately after the tool was taken away (after using it for three months), performance was 20% lower than the baseline
• The study did not check to see what level performance returned to after three months without it, nor when it returned to baseline levels
• The study also did not compare performance drops after returning from a three month vacation
• The study did not compare performance drops when losing access to other tools

This outcome is expected if given a tool that simplifies a process and then losing access to it. If I were writing code in Notepad and using _v2, _v3, etc for versioning, was then given an IDE and git for three months, then had to go back to my old ways with Notepad, I’d expect to be less effective than I had been. I’ve been relying on syntax highlighting, so I’m going to be paying less attention to the specific monochrome text than I used to. I’ll have fallen out of practice from using the version naming techniques that I used to use. All of the stuff that I did to make up for having worse tooling, I’m out of practice with.

But that doesn’t mean that I should use worse tools.

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.

I thought Hue bulbs used Zigbee?