If this works `git clone me@github.com:me/mine.git release_01 && ln -s release_01 /var/www/me/mine/current` then your Docker builds should also be extremely quick. Where I have seen extremely slow docker builds is with Python services using ML libraries. But those I reallly don't want to be building on the production servers.
"ECS would have worked for 99% of these apps, if they even needed that."
I used to agree with that but is EKS really that much more complicated? Yes you pay for the k8s control plane but you gain tooling that is imho much easier to work with than IaC.
I always feel like I am taking crazy pills when I read these threads. The k8s API and manifests config feels like a create standardardized way to deploy containers. I wouldn't want to run a k8s cluster from scratch but EKS has been pretty straightforward to work with. Being able to use kind locally for testing is amazing and k9s is my new favourite infra monitoring tool.
Even if you just run on 2 nodes with k3s it seems worth it to me for the standardized tooling. Yes, it is not a $5 a month setup but frankly if what you host can be served by a single $5 a month VM I don't particularly care about your insights, they are irrelevant in a work context.
Do those use cases need LLMs? Probably not. but if good results can be had with a day of prompting (in addition to the stuff mentioned in the article, which you have to do anyway) and a smaller model like Haiku gives good results why would you build a classifer before you have literally millions of customers?
The LLM solution will be much more flexible because prompts can change more easily than training data and input tokens are cheap.
I don't disagree that very numerical tasks like revenue forecasting are not a good fit for LLMs. But neither did a lot of data scientist concerns themselves with such things (compared to business analysts and the like). Software to achieve this has been commoditized.
As others have mentioned, one big issue is that every company does these things differently and just because someone texts you a link doesn't mean it's phishing, even though it feels shady. In Australia I have had calls by immigration officers on supressed numbers that wanted PII over the phone without being able to tell me what the purpose of the call is.
The average person self hosts literally nothing, why would it be different for inference? Which benefits severely from economies of scale and efficient 24/7 utlization
I think people are mainly confused because the AirPod Pros are quite competitively priced compared to other higher end offerings. The Max are so far off the market that it doesn't seem to make any sense and it seems unlikely that apple couldn't make up for lost margins with higher volume. Maybe they just literally can't/don't want to produce many of the Max and price them accordingly.
I'm delighted to see somebody else who refuses to use the pompous syntax Apple promote for these things. Entire thread full of people uncritically accepting that they should be referred to in actual conversation as AirPods Max as if it's a term that deserves more grammatical respect than most of them would give to attorneys general.
> This system was one of the oldest IT systems in NAV, and ran in production for 51 years, from when the National Insurance Scheme was introduced in 1967. In January 2018, Presys was put into production, which together with Pesys became the successor to DSF. At that point, DSF was also shut down.
The system is written in PL/I.
It's like the Apollo 11 code, but for social services.
"ECS would have worked for 99% of these apps, if they even needed that."
I used to agree with that but is EKS really that much more complicated? Yes you pay for the k8s control plane but you gain tooling that is imho much easier to work with than IaC.