What an amazing way to visualize early human migration. Lovely map by @HarvardCGA. A great colour scheme and an appropriate map projection! Source: buff.ly/3lbxonJ
You can see how LLMs still lack a lot of implicit context. For example, when reading a document, they are bad at guessing if the document can be trustworthy. They read an arxiv paper with grandiose unsupported claims and they repeat them to you as if it were its own judgment. 👇
Le Chaton Fat Quantique!
It will probably be something like "software factors darwinism".
You got an idea, you coded in 2h and open sourced the tool?
Good news: It was a good idea!.
Bad news: 68 other people did exactly the same!
It's time to recombine and let the fittest factor win! Or stick to yours for personal use
Things I am using more lately:
jq for pipes and schema navigation in JSONs. You can do stuff like
head -n 1 tmp.jsonl | jq '.field[3].field2 | keys'
to see keys of deep nodes
Folding text in vim to compact TODO notes
zf zo zc
to create a fold (from visual selection), open and close it
This is a bit of a DeepSeek moment and I guess the distillation debate is about to get uglier
well, fuck www.anthropic.com/news/fable-m...
👆 A paranoid LLM is ofc worse. This is just tuning a prior belief up or down. I guess you could self distill additional context for the train data e.g. "you know arxiv.org is such and such" or "this is an unknown source" with the hope it generalises (and also injecting some basic context).
