What are your thoughts on #privacy and #itsecurity regarding the #LocalLLMs you use? They seem to be an alternative to ChatGPT, MS Copilot etc. which basically are creepy privacy black boxes. How can you be sure that local LLMs do not A) “phone home” or B) create a profile on you, C) that their analysis is restricted to the scope of your terminal? As far as I can see #ollama and #lmstudio do not provide privacy statements.
D) what is AMD support like or is the Python fan boys still focusing on Nvidia exclusively?
I’m running gpt4all on AMD. Had to figure out which packages to install, which took a while, but since then it runs fine just fine
Good to know. Is there a particular guide that you followed to get it running on AMD?
arch wiki and gpt4all github & issues
Just curious. What do you have against Python?
It is slow. Syntax & community idioms suck. The package ecosystem is a giant mess—constant dependency breakage, many supply-side attacks, quality is all over the place with many packages with failing tests or build that isn’t reproducible—& can largely be an effect of too many places saying this is the first language you should learn first. When it comes to running Python software on my machine, it always is the buggiest, breaks the most shipping new software, & uses more resources than other things.
When I used to program in it, I thought Python was so versatile that it was the 2nd best language at everything. I learned more languages & thought it was 3rd best… then 4th… then realized it isn’t good at anything. The only reason it has things going for it is all the effort put into the big C libraries powering the math, AI, etc. libraries.
From my privacy trials on ollama - any model downloaded does not know the date or time and cannot access the internet.
If you are still sceptical you could download something like alpaca on flathub and once youve acquired a model, remove internet access etc through flatseal.
Since you ask, here are my thoughts https://fabien.benetou.fr/Content/SelfHostingArtificialIntelligence with numerous examples. To clarify your points :
- rely on open-source repository where the code is auditable, hopefully audited, and try offline
- see previous point
- LLMs don’t “analyze” anything, they just spit out human looking text
To clarify on the first point, as the other 2 unfold from there, such project would instantly lose credibility if they were to sneak in telemetry. Some FLOSS projects tried that in the past and it always led to uproars, reverts and often forks of the exact same codebase but without telemetry.
“They just spit out human looking text” is so incredibly regressive and asinine.
Before doing that, I would very carefully describe the problem I want to solve and other possible solutions. There are (relatively uncommon) situations where LLMs make sense, but many people are buying the snake oil when they don’t need it. Wouldn’t want to be played a fool.
Welcome to the tech field. Emerging tech often is uncommon to need. We make it more useful by having IT folks play around with it, and test in it new applications.
have you looked at backyard ai?
