[Quarrelsome]

reminds me of software to an extent. The issue with most software projects is humans, they ask for the wrong things, stress urgency arbitrarily, fail to see the big picture, are disorganised, give conflicting commands, etc, etc.

When I use reasonably recent models they can give me some fantastic output and do pretty much _exactly_ what I want. I assume when they don't, then that it's my fuck up tbh.

[pixl97]

People tend to be unaware of how much information is encoded in human society, hence why overseas developers can be a problem quite often because they live in a society with different rules.

We also tend to ignore how much on boarding with new developers and sometimes it takes months to get them fully up to speed.

This leads to two problems with LLMs, one human and one architectural.

First humans treat the LLM like a magic machine "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" Style.

The other is an AI context is terribly small so you can only work on issues that fit in the context without getting compressed out. Something highly original will take a lot more context than expected.