You’re doing it right by avoiding as much of Gitlab’s CI features. I’ve seen versions where scripts are inlined in the YAML with expressions in random rule fields and pipeline variables thrown all over the place. And don’t get me started on their “includes” keyword, it’s awful in practice, gives me nightmares.
Then I write a Kubernetes manifest in YAML with JSON schema validation and the heart rate goes down again.
I agree. You can’t just dismiss the problem saying it’s “just data represented in vector space” and on the other hand not be able properly censor the models and require AI safety research. If you don’t know exactly what’s going on inside, you also can’t claim that copyright is not being violated.
Someone should produce two identically named films in the same year and watch IMdB burn while they try to disambiguate them.
Bay leaves contain several different fatty acids which, when cooked, are transferred into your food. Fatty acids have a large effect on the flavour and nutrition of food. Next time to cook plain rice, add a few bay leaves to the pot and you will notice the change in flavour.
Failing to understand why does not make you correct by ignoring it.
Learning how to use AI tools is another meta-skill just like learning how to use a search engine such as Google. The latter is widely accepted as a must-know for software developers.
Only if you have archived data and use fitting lifecycle policies. 2TB of regular S3 would cost ~$40 which is about 4x the price of Google Drive. That’s not even accounting for the data retrieval costs.
I used to have Netflix until I moved country and they blocked me out of my own account. Fuck them, cancelled. To the seas with their content.
Abstractions aren’t concrete and all of these standards you’re referring to are concrete data serialisations. You may be interested in CUE which captures this concept in its design.