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Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

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61–70 of 78 posts

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#61

Azure Kubernetes Wrangler (SRE) here, before I turn some LLM loose on my cluster, I need to know what it supports, how it supports it and how I can integrate into my workflow. Videos show CrashLoopBackOff pod and analyzing logs. This works if Pod is writing to stdout but I've got some stuff doing straight to ElasticSearch. Does LLM speak Elastic Search? How about Log Files in the Pod? (Don't get me started on that ni…

> CertManager is on strike and certificate has expired Had a good chuckle here, hah.

Same. Typically call it “hung” but maybe stating certmanager is on strike will get the point across better.

But sigh does it really get to the state of the kubernetes ecosystem. All these projects need to be operated, can’t just set it and forget it.

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#62
post #58

Earlier quoted context omitted.

What would you propose as an alternative to k8s?

go back to building monoliths

As a software engineer, DevOps engineer, platform engineer and SRE in a mixed bag, I would say not building monoliths -- instead build a microservice but slightly larger that can still be easily cloneable, scalable and fault tolerant. A mix of monolith and microservice, you may say, and I would like to call that "siloservice".

Silo: A silo is a cylindrical tower used for bulk storage, like grain silos that stand tall near farms. Another kind of silo is harder to see — military silos are underground.

Obviously, you don't need 10 fragmented microservices interdepending on each other, that's one of the biggest overengineering for microservices in real world practices, but you can build multiple "siloservices" that does the same stuff more effectively while getting easy maintenance. I got this inspiration from working with monorepos in the past.

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#63
post #58

Earlier quoted context omitted.

What would you propose as an alternative to k8s?

go back to building monoliths

I don't get how that solves the scalability and deployment/sdlc problems k8s is used for

Like, monoliths and k8s seem orthogonal to me. It's like saying 'docker was a failure we should go back to writing Java' to me.

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#64
post #25
post #21

Earlier quoted context omitted.

Getting tired of seeing this concept of practically guaranteed hallucinations from any LLM used in production. I've used LLMs for various tasks, and if you tune your system correctly, it can be very reliable. It's just not always plug-and-play reliability. You need to set up your fine-tuning and prompts and then test well for consistent results.

> You need to set up your fine-tuning and prompts and then test well for consistent results. Tell that to Google... Seriously, it is well established that these systems hallucinate. Trying to say otherwise shows you are trying to push something that just is not true. They can be right, yes. But when they are wrong they can be catastrophically wrong. You could be wasting time looking into the wrong problem with someth…

The most common hallucinations I've seen are phantom GitHub repos and issues, and this usually appears when I ask for a source.

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#65

For god sakes, SREs need to give up on K8. It was a bad idea, just move on. The answer is not, “let an ai figure it out.” That is legitimately scary.

What’s more “legitimately scary” is that people wanna run it back to monoliths like it’s the 90s and think building a modern scalable system is too hard

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#66

For god sakes, SREs need to give up on K8. It was a bad idea, just move on. The answer is not, “let an ai figure it out.” That is legitimately scary.

What would you propose as an alternative to k8s?

I think the big problem is it tries to do too much. We used to have many tools as SRE but now teams are really limited. We handed the keys to the engineers which I think is overall a good intention. But we didn’t set them up with sensible defaults, which left them open to making really bad decisions. We made it easy to increase the diversity in the fleet and we removed observability. I think things are more opaque, more complicated, and I have fewer tools to deal with it.

I miss having lots of tools to reach for. Lots of different solutions, depending on where my company was and what they were trying to do.

I don’t think one T-shirt size fits all. But here are some specific things that annoy me.

Puppet had a richer change management language than docker. When I lost puppet, we had to revert back to shitty bash scripts, and nondeterminism from the cicd builds. The worst software in your org is always the build scripts. But now that is the whole host state! So SREs are held captive by nonsense in the cicd box. If you were using Jenkins 1.x, the job config wasn’t even checked in! With puppet I could use git to tell me what config changed, for tracked state anyway. Docker is nice in that the images are consistent, which is a huge pain point with bad puppet code. So it’s a mixed bag.

The clouds and network infrastructure have a lot of old assumptions about hosts/ips/ports. This comes up a lot in network security, and service discovery, and cache infrastructure. Dealing with this in the k8 world is so much harder, and the cost and performance so much worse. It’s really shocking to me how much people pay because they are using these software based networks.

The Hypervisors and native cloud solutions were much better at noisy neighbor protection, and a better abstraction for carving up workloads. When I worked at AWS I got to see the huge lengths the ebs and ec2 teams put into providing consistent performance. VMWare has also done a ton of work on QoS. The os kernels are just a lot less mature on this. Running in the cloud inside a single vm removed most of the value of this work.

In the early 2010s, lots of teams were provisioning ec2 instances and their bills were easy to see in the bill as dollars and cents. At my last company, we were describing workloads as replicas/gbs/cpus/clusters on a huge shared cluster. Thousands of hosts, a dozen data centers.

This added layer of obfuscation hides true cost of a workload. I watched a presentation from a large well known software service company say that their k8 migration increased their cloud spend because teams were no longer accountable to spend. At my company, I saw the same thing. Engineers were given the keys on provisioning but were not in the loop for cost cutting. That fell to the SREs, who were blamed for exploding costs. The engineers are really just not prepared to handle this kind of work. They have no understanding about the implications in terms of cost and performance. We didn’t train them on these things. But we took the keys away from the SRE’s and handed it to the engineers.

The debugging story is particularly weak. Once we shipped on docker and K8 we lost ssh access to production. 10 years into the docker experiment, we now have a generation of senior engineers who don’t know how to debug. I’ve spent dozens of hours on conference calls while the engineers fumbled around. Most of these issues could have been diagnosed with netstat/lsof/perl -pe/ping/traceroute. If the issue didn’t appear in New Relic, then they were totally helpless. The loss of the bash one-liner is really detrimental to engineers progress.

There is too much diversity in the docker base images and too many of them stuck. The tool encourages every engineer to pick a different one. To solve this my org promised to converge on alpine. But if you use a docker distribution, now you are shipping all of user mode to every process. I was on the hook for fixing a libc exploit for our fleet. I had everyone on a common base image, so fixing all 80 of my host classes took me about a few days. But my coworkers in other orgs who had hundreds of different docker images were working on it a year later. Answering the question, which LibC am I on became very difficult.

Terraform has a better provisioning/migration story. Use that to design your network, perform migrations. Use the cloud native networking constructs. Use them for security boundaries. Having workloads move seamlessly between these “anything can be on me hosts” make security, a real nightmare.

I left being an SRE behind when I saw management get convinced docker/k8 was a cancer treatment, a desert topping and a floor wax. it’s been five years and I think I made the right call.

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#67
post #25
post #21

Earlier quoted context omitted.

Getting tired of seeing this concept of practically guaranteed hallucinations from any LLM used in production. I've used LLMs for various tasks, and if you tune your system correctly, it can be very reliable. It's just not always plug-and-play reliability. You need to set up your fine-tuning and prompts and then test well for consistent results.

> You need to set up your fine-tuning and prompts and then test well for consistent results. Tell that to Google... Seriously, it is well established that these systems hallucinate. Trying to say otherwise shows you are trying to push something that just is not true. They can be right, yes. But when they are wrong they can be catastrophically wrong. You could be wasting time looking into the wrong problem with someth…

> Tell that to Google...

Yeah, because Google's LLMs have an completely open question/answer space.

For e.g. a Kubernetes AI, you can nowadays just feed in the whole Kubernetes docs + a few reference Helm charts, tell it to stick close to the material, and you'll have next to no hallucinations. Same thing for simple data extraction tasks, where in the past you couldn't use LLMs because they would just hallucinate data into the output that wasn't there in the input (e.g. completely mangling an ID), which nowadays is essentially a non-issue.

As soon as you have a restrictable space in which the LLM acts, you have a lot of options to tune them that hallucinations are not a major issue nowadays.

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#68
post #25
post #21

Earlier quoted context omitted.

Getting tired of seeing this concept of practically guaranteed hallucinations from any LLM used in production. I've used LLMs for various tasks, and if you tune your system correctly, it can be very reliable. It's just not always plug-and-play reliability. You need to set up your fine-tuning and prompts and then test well for consistent results.

> You need to set up your fine-tuning and prompts and then test well for consistent results. Tell that to Google... Seriously, it is well established that these systems hallucinate. Trying to say otherwise shows you are trying to push something that just is not true. They can be right, yes. But when they are wrong they can be catastrophically wrong. You could be wasting time looking into the wrong problem with someth…

It's definitely an overblown problem. In practice it's not a big issue.

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#69
post #21

Earlier quoted context omitted.

Getting tired of seeing this concept of practically guaranteed hallucinations from any LLM used in production. I've used LLMs for various tasks, and if you tune your system correctly, it can be very reliable. It's just not always plug-and-play reliability. You need to set up your fine-tuning and prompts and then test well for consistent results.

> it can be very reliable You need to quantify this. With actual numbers. I am getting very tired of seeing everyone pushing LLMs and being disingenuous about exactly how often it is getting things wrong. And what the impact of that is. There is a reason that AI is not taking off in the enterprise and that is because people who take their job seriously are getting tired too.

Actual number: 0. I haven't seen a single hallucination in my workloads since GPT-4 but the task is very specific, as it should be.

Re: Launch HN: Parity (YC S24) – AI for on-call engineers working with Kubernetes

#70
post #68
post #25

Earlier quoted context omitted.

> You need to set up your fine-tuning and prompts and then test well for consistent results. Tell that to Google... Seriously, it is well established that these systems hallucinate. Trying to say otherwise shows you are trying to push something that just is not true. They can be right, yes. But when they are wrong they can be catastrophically wrong. You could be wasting time looking into the wrong problem with someth…

It's definitely an overblown problem. In practice it's not a big issue.

Yeah.... no it's really not overblown.

It is a serious problem when these tools are being pushed as trustworthy when they are anything but.

On an almost daily occurrence I deal with some sort of hallucination in code, in summarizing something, we see it constantly on social media when people try to use Google's AI summary as a source of truth.

Let's not try to lie to push an agenda about what the capabilities of what these models can do. They are very powerful, but they make mistakes. There is zero question about that, and quite often.

The problem isn't that they hallucinate, the problem is that we have comments like yours trying to downplay it. Then we have people that, it is right just enough times that they start trusting it without double checking.

That is the problem, it is right enough times that you just start accepting the answers. That leads to, making scripts that grab data and put it into a database without checking. It's fine if it is not business critical data, but it's not really fine when we are talking about health care data or.. oh idk, police records like a recent post was talking about.

If you are going to use it for your silly little project, or you're going to bring down your own companies infrastructure go for it. But let's not pretend the problem doesn't exist and shove this technology into far more sensitive areas.

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