311–320 of 604 posts
Unless you have constant load that justify 24/7 deployments most devs will just use an API. Or find solutions that doesn’t need you to pay > $1 / hour.
I have a timeline that I am still trying to work through but it goes like this : 2012 - moores law basically ends - nand gates do t get smaller just more cleverly wrapped. Single threaded execution more or less stops at 2 GHz and has remained there. 2012-2022 - no one notices single threaded is stalled because everything moves to VMs in the cloud - the excess parallel compute from each generation is just shared out i…
Edit: it’s worth expanding on the data centre costs issues - if it’s fair to say we have stalled on “free”speed ups for standard software (ie C/Linux) - that is clock speeds more or less stopped and we get more 64 bit cores but each core is more or less no faster (hand wavy), then the amount of clients that can be packed into a data centre stays the same - you are renting out CPUs in a Docker VM - that’s basically on…
Ancient Java servlets back in the early 2000s were more suitable for performance on current gen hardware than modern NodeJS, Python etc...
> The biggest problem: developers don’t want GPUs. They don’t even want AI/ML models. They want LLMs. System engineers may have smart, fussy opinions on how to get their models loaded with CUDA, and what the best GPU is. But software developers don’t care about any of that. When a software developer shipping an app comes looking for a way for their app to deliver prompts to an LLM, you can’t just give them a GPU. I'm…
I don't think this split exists, at least in the way you framed it.
What does exist is workload, and problems that engineers are tasked with fixing. If you are tasked with fixing a problem or implementing a feature, you are not tasked with learning all the minute details or specifics of a technology. You are tasked with getting shit done, which might even turn out to not involve said technology. You are paid to be a problem-solver, not an academic expert on a specific module.
What you tried to describe as "magic" is actually the balance between broad knowledge vs specialization, or being a generalist vs specialist. The bulk of the problems that your average engineer faces requires generalists, not specialists. Moreover, the tasks that actually require a specialist are rare, and when those surface the question is always whether it's worth to invest in a specialist. There are diminished returns on investment, and throwing a generalist at the problem will already get some results. You give a generalist access to a LLM and he'll cut down on the research time to deliver something close to what a specialist would deliver. So why bother?
With this in mind, I would go as far as to frame a scenario backhandedly described as "want to understand where their code is running and what it's doing" (as if no engineer needs to have insight on how things work?) as opposed to the dismissive "just wants to `git push` and be done with it" scenario, can actually be classified as a form of incompetence. You,as an engineer, only have so many hours per day. Your day-to-day activities involve pushing new features and fixing new problems. To be effective, your main skillet is learn the system in a JIT way, dive in, fix it, and move on. You care about system traits, not low-level implementation details that may change tomorrow on a technology you may not even use tomorrow. If, instead, you feel the need to waste time on topics that are irrelevant to address the immediate needs of your role, you are failing to deliver value. I mean, if you frame yourself as a Kubernetes expert who even know commit hashes by heart, does that matter if someone asks you, say, why is a popup box showing off-center?
I have a timeline that I am still trying to work through but it goes like this : 2012 - moores law basically ends - nand gates do t get smaller just more cleverly wrapped. Single threaded execution more or less stops at 2 GHz and has remained there. 2012-2022 - no one notices single threaded is stalled because everything moves to VMs in the cloud - the excess parallel compute from each generation is just shared out i…
The unsung hero of early computing was Dennard scaling. Taking CPUS from 10MHz to 2GHz, all alongside massive per-clock efficiency improvements must have been a crazy time. From a 50MHz 486 in 1990 to a 1.4GHz P3 in 2000 is a factor of 28 improvement in speed solely due to clock speed! Add on all the other multiplicative improvements from IPC...
Since then, in more than 20 years, the clock frequency has increased only 2 times, while in the previous decade (1983-1993) it had increased only about 5 times, where a doubling of the clock frequency (33 to 66 MHz) had occurred between 1989 and 1993 (for cheap CPUs with MOS logic, because expensive CPUs using ECL had reached 80 MHz already during the seventies).
Also, Pentium III has reached 1.4 GHz only in early 2002, not in 2000, while 80486 has reached 50 GHz only in 1991, not in 1990.
Earlier quoted context omitted.
> There's an (increasingly small) group of software developers who don't like "magic" and want to understand where their code is running and what it's doing. These developers gravitate toward open source solutions like Kubernetes Kubernetes is not the first thing that comes to mind when I think of "understanding where their code is running and what it's doing"...
Indeed, I have to wonder how many people actually understand Kubernetes. Not just as a “user” but exactly all what it is doing behind the scenes… Just an “idle” Kubernetes system is a behemoth to comprehend…
I would ask a different question. How many people actually need to understand implementation details of Kubernetes?
Look at any company. They pay engineers to maintain a web app/backend/mobile app. They want features to be rolled out, and they want their services to be up. At which point does anyone say "we need an expert who actually understands Kubernetes"?
I really liked playing around with fly gpus, but it's just too expensive for hobby-use. Same goes for the rest of fly.io honestly. The DX is great and I wish I could move all of my homelab stuff and public websites to it, but it'd be way too expensive :(
Who do you use instead for hobby projects?
I used to use cheap vms/vps from lowendtalk deals, but usually they're on over-subscribed hosts and can't do anything heavy.
Actual host recommendations: I like Racknerd and Hivelocity currently. OVH too, but I've read a lot of horror stories so I guess ymmv.
Earlier quoted context omitted.
I keep seeing this opinion and I don't understand it. For various reasons, I recently transitioned from a dev role to running a 60+ node, 14+ PB bare metal cluster. 3 years in, and the only thing ever giving me trouble is Ceph. Kubernetes is etcd, apiserver, and controllers. That's exactly as many components as your average MVC app. The control-loop thing is interesting, and there are a few "kinds" of resources to ge…
“etcd, apiserver, and controllers.” …and containerd and csi plugins and kubelet and cni plugins and kubectl and kube-proxy and ingresses and load balancers…
Do you understand you're referring to optional components and add-ons?
> and kubectl
You mean the command line interface that you optionally use if you choose to do so?
> and kube-proxy and ingresses and load balancers…
Do you understand you're referring to whole classes of applications you run on top of Kubernetes?
I get it that you're trying to make a mountain out of a mole hill. Just understand that you can't argue that something is complex by giving as your best examples a bunch of things that aren't really tied to it.
It's like trying to claim Windows is hard, and then your best example is showing a screenshot of AutoCAD.
> The biggest problem: developers don’t want GPUs. They don’t even want AI/ML models. They want LLMs. System engineers may have smart, fussy opinions on how to get their models loaded with CUDA, and what the best GPU is. But software developers don’t care about any of that. When a software developer shipping an app comes looking for a way for their app to deliver prompts to an LLM, you can’t just give them a GPU. I'm…
Earlier quoted context omitted.
> There's an (increasingly small) group of software developers who don't like "magic" and want to understand where their code is running and what it's doing. These developers gravitate toward open source solutions like Kubernetes Kubernetes is not the first thing that comes to mind when I think of "understanding where their code is running and what it's doing"...
Great opportunity for someone ballsy to write a book about kubernetes internals for the general engineering population. Bonus points for writing a basic implementation from first principles capturing the essence of the problem kubernetes really was meant to solve. The 100 pages kubernetes book, Andriy Burkov style.
What would be the interest of it? Think about it:
- kubernetes is an interface and not a specific implementation,
- the bulk of the industry standardized on managed services, which means you actually have no idea what are the actual internals driving your services,
- so you read up on the exact function call that handles a specific aspect of pod auto scaling. That was a nice read. How does that make you a better engineer than those who didn't?
Earlier quoted context omitted.
It's worth noting that "container" and "process" are pretty similar abstractions. A lot of people don't realize this, but a container is sort of just a process with a different filesystem root (to oversimplify). That arguably is what a process should be on a server.
No, they are not. I'm not sure who started this whole container is just a process thing, but it's not a good analogy. Quite a lot of things you spin up containers for have multiple processes (databases, web servers, etc). Containers are inherently difficult to sum up in a sentence. Perhaps the most reasonable comparison is to liken them to a "lightweight" vm, but the reasons people use them are so drastically differe…
It came from how Docker works, when you start a new container it runs a single process in the container, as defined in the Dockerfile.
It's a simplification of what containers are capable of and how they do what they do, but that simplification is how it got popular.