Every major AI shop has a ton of in-house classifiers already, big, small, generalist, specialized. Some are used in inference pipelines (e.g. safeguards), some are used in data preparation, training, analysis and investigation, research, various one-off and intermediate tasks etc. Offering them on a public API doesn't always make business sense. I don't see much substance to this buzz, looks like people that are new to all this are discovering that classifiers exist, they are more efficient at classification, and many tasks commonly done with generative models are classification in disguise. Which is not bad at all, a fresh look at their use is great to have.
This. It’s machine learning vs. “AI” for the uninitiated. Soon there will be a new ground breaking model that does k-means clustering and will get a billon dollar funding (but only if you're young and live in SF)
The good news is it’s fun to see people discover and get excited about things that I like as well.
Its in a modern and easily to deploy package. The hype is a bit wierd. "0 cost output tolkens" is such a silly phrasing.
I would have never considered importing pytorch for filtering through log files before even knowing my way around it. But if i can type a filtering condition by text and hit enter; i may actually use that to save some time.
Id want something local though, but thats hardly a difficult demand for what it is.
orbital-decay · · focus · HN ↗
0x20cowboy · · focus · HN ↗
The good news is it’s fun to see people discover and get excited about things that I like as well.
aDyslecticCrow · · focus · HN ↗
I would have never considered importing pytorch for filtering through log files before even knowing my way around it. But if i can type a filtering condition by text and hit enter; i may actually use that to save some time.
Id want something local though, but thats hardly a difficult demand for what it is.