Open-weights models are catching up and are now viable for many tasks.

Keep in mind that closed, proprietary models:

1) Use your data internally for training, analytics, and more - because "the data is the moat"

2) Are out of your control - one day something might work, another day it might fail because of a model update, a new "internal" system prompt, or a new guardrail that just simply blocks your task

4) Are built on the "biggest intellectual property theft" of this century, so they should be open and free ;-)

Why is everyone so careless about letting Claude Desktop upload their source code and/or private data to its servers? Or maybe I'm too paranoid for caring about where data go.

The codebase I am paid to work on is a dog's breakfast, largely agglomerated over many months by offshore contractors. If Anthropic includes it in their training set, it's their own funeral.

Because my employer tells me it’s fine so long as we’re using it with AWS Bedrock. Do I believe that Amazon then wouldn’t be siphoning all this as model training data? It’s a fun conspiracy…

"These run exclusively on servers Proton controls so your data is never stored on a third-party platform." But it's stored on somebody else's computer anyway.

"servers Proton controls" can mean anything. I can rent any random cloud server and have "control" over it.

How are "LLM hallucinations" different from a low-quality training dataset or randomly picked tokens due to overly random sampling settings?

What I see even in good models is that when you ask something hard or impossible (but looking routine) instead of replying “I cannot” they hallucinate. A better dataset would help only to solve problems which can be solved (based on this dataset).