Thank you. It seems largely ignored that LLMs still sample from a set of tokens based on estimated probability and the given temperature - but not on factuality or the described "confidence estimate" in the article. RAG etc. only move the estimated probabilities into a more factually based direction, but do not change the sampling itself
While this made me laugh and there is some truth to it, the nice thing when running the process described in the blog post is that you don't need to know what or how you want to count - the LLM has the knowledge to classify it correctly enough to get good estimations. Go and Rust are both good examples of words that have multiple meanings and are pre-/suffix to many other words.
In total numbers I got 539 jobs saying that they want Rust experience and 695 want Go experience. I think I should have added another line-chart showing the programming language distribution over time, thanks for the idea.
Thanks for looking this up. It's especially interesting bc if I search "golang" on LinkedIn jobs, I see 5,185 results (in the US), but I only get 148 results for "rust".
Hardly scientific, but shows the risk of using Hacker News to draw overly strong conclusions of language popularity.
Another thing to improve this, is to ask posters to add GLOBAL_REMOTE, COUNTRY_REMOTE or something that indicates is not local remote only (within the same country).
Yes, later this week I will follow up with something to tell a little bit about the animation and the sphere positioning, that graph was kind of the most fun in writing this blog post. Thank you for your feedback!
