CallMeAl (Not AI)

Everything you type in the chat box is sent to the LLM provider but they get Duck Duck Go’s IP instead of yours.

So if you type personal things its mostly just like typing them directly to ChatGPT. However, with duck.ai your IP, Browser info, Location (if shared), etc is seen by Duck Duck Go instead of OpenAI.

I don’t think DuckDuckGo is lying when they say that they don’t use your chats to train models. However, that leaves plenty for OpenAI and Duck Duck Go to do with your chats, like building shadow profiles.

I suggest that if you want to be anonymous to Duck Duck Go, then use duck.ai via vpn or tor. Always assume the content of your chat session is being logged by the LLM provider.

Yes, 100 projects from the Fat Head would make sense.

Do you have any way to establish that these 100 more often come from the Long Tail?

If an app includes 50 well-known big projects and 1000 small projects, the sum result can still be that small projects make up for a large fraction of the code.

I understand your point that this is possible. It is an assumption to assume it is most likely the case however.

I would expect the Fat Head of most used open source projects to make up the vast majority of the open source code included in apps. It is not a common practice to include 1000 small projects into a code base for an app, or even 100.

Is it not reasonable then to expect that the 77% of app code from open source is because the most popular app building blocks are open source? Aren’t the popular open source languages, frameworks, and databases are themselves big enough to exceed the number lines of internally written code for the app business logic most of the time?

For example, if I make a “small” electron app its going to be 90% or more open source because the electron base is so large already.

The insight that a majority of open source projects are small contributions by hobby developers, and that it is their summed joint effort what matters, is very interesting.

The vast majority of open source projects are by hobby developers but how much of those projects make up the 77% of the open source included in apps mentioned in the study?

The author assumes an even distribution but I challenge that.

The most popular (Top listed by Github, Gitlab, etc) open source languages (python, typescript, etc), frameworks (rails, flutter, react, etc), and databases (postgres, mongo, redis etc) are all either directly corporately funded (Google, Microsoft, Meta, etc) and/or have robust foundations and sustainability plans.

I would expect these to make up the vast majority of the open source code in modern apps.

What does “optimized for desktop use over Tor” mean? Please explain the optimizations.

we had it back in the day https://en.wikipedia.org/wiki/ZipSlack

If you ask me, you are better off focusing on monitoring, fast detection, and auto-healing in a homelab rather than High Availability. I use an ancient tool called monit and newer tools like uptime kuma for this. Detection and restart is easier than having 2 of everything.

I’ve been using Quad9 DoH for a few months now. Very happy with it so far.