If there’s anyone I can trust to give an accurate and unbiased assessment of AI company finances, it’s surely an outfit selling a “finance CLI for your trading agent”
In the US, some of those apps were generating gross nonsense like showing an image of a pizza with a sweet pie filling (because the menu said “pizza pie”).
I don’t get how AI images like this don’t open them up to false advertising lawsuits
The behavior of a single model and version can and does change. There’s not only built-in stochasticity, but closed hosted models like Claude are tweaked and changed all the time.
wow, that’s crazy that OpenAI is saying that negative opinion against them is actually a nefarious foreign conspiracy, and does not actually indicate people hate them
More of a lot of things. Universal healthcare (as opposed to employer-provided plans) encourages people to start their own businesses. UBI would be similar, but moreso.
You’d really think they’d really check everything and cross their t’s after their previous issues in marjorana fermion QC. I generally have a very high opinion of MS research, but this is getting a bit embarrassing.
Bingo. The typical data scientist has a masters or PhD in a non-CS quantitative field, and has had exactly zero CS or software eng classes. It’s a shame, because once you get over some of the idiosyncrasies, R is a really powerful and flexible functional language.
I’m a big fan of Polars. It’s really fast and memory efficient. With the lazy streaming functionality, I’ve been able to easily process 1 Tb+ data on a single machine (you do have to be careful to not do any operation that would cause the whole DF to materialize in that case).
It’s certainly miles better than Pandas, which has a terrible API in addition to being comically inefficient. In my group, we generally use it for any new work, and have also swapped out pandas for polars in critical spots of our existing code - the latter giving a huge benefit relative to the amount of work it took.
I largely agree with you on SQL being the common denominator, but there are some things that are just awkward in SQL, and much easier to do in Python or other general purpose language.
I’m not saying it doesn’t have flaws, but the tidyverse is still the most coherent and functional ML/stat computing ecosystem I’ve ever used. R packages outside of the tidyverse can get pretty gnarly. Even the R stdlib is usually considered to be inconsistent and riddled with legacy cruft.