bionsystem
Joined 8 years ago
Joined 8 years ago
I guess it all depends on context, where I work ops stuff is clearly the bottleneck for a variety of reasons (technical debt that we are constantly working around, secret management for compliance reasons, etc). We self-host everything from bare metal. Some people would need to rethink the infra from the bottom up before it is "LLM ready".
That doesn't mean my job is not threatened mid/long term, in fact, thanks to LLM it is possible to rebuild that in a reasonnable amount of time I think. It's actually one of my side projects to offer this as a service. But if that doesn't work maybe I should have a plan C.
Also eventually you'll also have to trust it to write the deployment code or even run the deployment itself, otherwise SRE is going to be the bottleneck. And only then should I feel anxiety about the rest of my career (that, or my employer decide LLM are good enough to get rid of me, even if they are imperfect).
Unfortunately I think it is as much a curse as it is a gift. One of my parents' friend is a math researcher. He is not at the absolute end of the spectrum, but he is the kind of people who will write on the bathroom walls because he has an idea NOW and it cannot wait a minute to explore. He forces himself to do competitive cycling and can't enjoy more casual hobbies because if he does not spend 100% of his energy and focus to something, his brain will just do math.
I remember listening to him and a teacher, the teacher would give some anecdotes on what he's seen at the ENS. People reciting world chess championship games at night to fall asleep. Reading massive math books (1000+ pages) in an afternoon. Another story I heard in a different context from another teacher, math students were assigned a python project, after an introductory course. They never did any programming before. One student wrote 300 LoC in a single function and showed it to the teacher. It didn't have a main function, so the teacher asked him how he tested it. The student didn't know what he was talking about. They added what's needed to run the function and it worked.
I found a way using (can't remember the tool name) which if you loop through the imports and gives it to the tool you get the package name, then I would build wheels to have all binaries and build a container or a VM with all that's needed, thus working completely around python package managers. This was a good enough workflow for the kind of deployment we needed.
Jupyter added a layer of complexity, I deployed it alongside RStudio as browser IDEs in docker swarm. Everybody wants a different set of deps and versions, so you have to keep track of everything, and also people may use things just for development that must not be shipped to prod, so you have to keep track of that too. Also some would develop notebooks on windows and expect them to work in linux VMs/containers and even in prod.
Nowadays devs ship container images anyway through a CI so it is less of an issue. In this era docker was far from being the de-facto everywhere, some people were still afraid of this, security didn't like it, etc.