r/devops

I'm a fucking newbie, still, I tried to learn code, I know some, not a lot. I'm familiar with concepts, but not what "used to be". You senior devs grew up coding in notepad and apache and sql, I grew up with modern frameworks AND am growing up realizing even THAT isn't necessary. AI has grown so much. I have talked to senior devs, and while they say people checking ai won't be replaced I can't help but disagree.

What do senior devs do when they don't know what's wrong? Let me hear you say you search the web for a solution someone posted long ago. Let me hear how you still, in this ai day and era, try to fix a bug for 6 hours. You turn to ai too. You use it the same way as the vibecoders. It's just experience. And I see people so confident that experience makes you safe from ai but I believe it's only a matter of time when your experience becomes obsolete. After all it's just information you gathered thoughout years.

And some platforms are paying senior devs to correct code made by ai, to train it. Some of you are giving away that 10+ years experience. It's buyable. Anything can be bought. And just like any stacks from before, AI will be free/affordable for 5-10 years until us newbies depend on it for fast clean code. And then they'll raise the price.

Historically this happened with any major platform or software and it's going to be the same.

And because this isn't ragebait enough, whatever you learned 30 years ago, is obsolete. Whatever I learn now even on my own (old stuff, new stuff, doesn't matter) will be obsolete in maybe 5 years of more AI fast paced development.

Mind my words we'll have holograms for calls, VR for social media, flying cars (in maybe 20 30 years because it's a structural, societal, architectural change) and robots inside the house for tasks like we own coffee machines. In the 50's you brewed the coffee, now you own a machine. You washed clothes by hand then using the washing machine. You run errands until your robot can do it for you. You mop the floor and fold clothes until the robot can do it for you (Tesla robot already released just not comercially for the masses) We have self driving cars. We have AI agents that can do anything you can do on your computer.

I am building a ci/cd pipeline. When a PR is merged, it will push a docker image to ECR, but when I am just pushing a regular commit, I don't want to run the full deployment. What task goes into ci and what goes into cd? I've seen different takes on where to run the docker-related stuff.

- CI (always runs on push/merge):

• lint
• scan (security, etc)
• build
• test (unit/integration)
• test docker image build ???

- CD (only runs on merge):

• build docker image
• automated e2e (if any)
• upload binaries to Nexus
• upload image to ECR

hey yall,

im a lead SRE at a global fortune 500 that you have heard of haha - i'm a bit of a lurker here

but just wanted to talk about runbooks and documentation for SOPs.

my feeling is that confluence docs kinda suck and runbooks are kind of a mess, our operators are jumping between the docs and their shell, the docs are often times missing a bunch of detail, they are brittle, poorly maintained, half the time the details are hidden in tribal knowledge and when dealing with the pressure of an active inc we notice the pain even more. extracting critical details & commands out of an inc can also be painful and doesnt always translate to improved operational readiness next time.

i admit we're not very mature.. but was curious if its only me feeling like this?

what are you guys doing with runbooks to solve these issues?

I’ve been checking how to build a disaster recovery plan for cloud dependencies and how to automate as much remediation as possible. The plan is to have the system auto-restart services or rollback deployments when things crash. 

But when I read up on AIOps, I saw recommendations to make sure a human is involved for anything high-risk. It makes sense because you do not want an automated script taking down a production database by mistake. But now I am trying to figure out where to draw the line exactly between guided recommendations and full automation. 

If anyone here relies on machine learning for root cause analysis, which actions do you let the system execute on its own, and what requires manual approval?

I have around 5 years of experience across DevOps/cloud/backend work. I’ve worked with Kubernetes, EKS/AKS, Helm, CI/CD, Docker, Terraform, ArgoCD, AWS/Azure, and some Java/Spring Boot.

My issue is that my knowledge is broad but uneven. I’m comfortable deploying applications, writing manifests, using kubectl, building pipelines, working with cloud networking/IAM, etc., but I’m much weaker on Kubernetes internals, low-level networking, storage/CSI, control plane, scheduling, CRDs/operators, upgrades, observability, and deep troubleshooting.

I’m considering first completing one comprehensive Kubernetes course to build a complete mental map, then spending the next several months going deeper through hands-on labs, troubleshooting, Linux/networking fundamentals, and production-style projects.

For people who became genuinely strong at Kubernetes/platform engineering: does this sequence make sense? What would you change?

I’m not looking for a giant list of tools, mainly feedback on the learning sequence and what gave you the biggest jump in depth.

Note: "Q improved with AI"

Hello, I ask for help since I am getting a lot of interviews but not passing any, I'm getting to start pretty frustrated and since we are entering the holiday season I want to get any opportunity possible.

The thing is that I am preparing myself based on my experience: 5+ yrs experience, jenkins, python, troubleshooting, AWS, IaC, etc. and when I prepare for the different kind of interviews none of my preparation seems to work:

• If it is situational, my explanation/experience is not enough for the role
• If it is pure technical, it doesn't demonstrate my experiencie or they ask for a very specific tool (which mostly has transfereable experiencie) and it's not enough for the position
• If it is trivia based (which I am pretty bad at memorizing) I failed because I didn't remember the flag for a command that I can find in a 15-sec google search or AI prompt

What makes this frustrating is that I genuinely feel capable of doing the jobs I'm interviewing for. I've worked in production environments, troubleshot real incidents, built and maintained pipelines and infrastructure, and worked with engineering teams. But apparently I'm still not presenting that knowledge in the way interviews expect.

So I'd especially like to hear from people who have successfully interviewed for DevOps/SRE/Platform roles recently:

How did you prepare?

Did you memorize common commands and syntax?

Did you grind interview questions?

Did you build labs?

Did you practice storytelling around your projects?

How did you deal with interviews covering an extremely broad toolset?

At this point I'm trying to understand how much interviewing is about being good at the actual job versus becoming good at the interview format itself.

Any practical advice would be appreciated.

So I work in a very small company as a dev
And we have external devops company which manages local http servers and MySQL servers for our saas product.

So the thing is I would like to shift some of their work to our in-house dev team.

I thought about starting small and manage a separate server on the cloud that will just run internal cron based daily tasks. But those tasks require read and write permission to the existing db which managed by the local company. Could I ask them to give me access to to the db from another server? I read that it’s a bad practice to expose prod db to the internet even if it has white list so I’m not sure here.

Hey r/devops, welcome to our weekly self-promotion thread!

Feel free to use this thread to promote any projects, ideas, or any repos you're wanting to share. Please keep in mind that we ask you to stay friendly, civil, and adhere to the subreddit rules!

Been working a lot with startups & everything is such a shit show. Supabase RLS
is never configured correctly, the idea of dev/staging/prod and ephemeral
environments just doesn't exist (or if they do, staging is front-end only and
connects directly to the prod database.), and they use so many different vendors
(Clerk/Cloudflare/Supabase/Railway/Vercel) that there's always functionality I
can't test anywhere other than prod.

Been trying to find a single unified platform I could recommend/implement, but
none have given the perfect mix of batteries included security, reliability, and
local testability.

Thinking of building something myself & would love some feedback on what you
hate, love, and wish existed. Will probably open-source it once I've got
something.

Feels like I hear about a new AI agent tool for ops every week. Where do you all keep up with this stuff? Any newsletters, communities, or people you follow that help you figure out what's worth trying?

And if something catches your eye, how do you vet it before letting it near production?

Here's some context :
We were using Sevalla for our object storage for over a year. We had more than half a terabyte of data. Their fucked up payment platform did not allow us to do the payment everytime but continued to work. But all of a sudden, our account was suspended and all our data were permanently deleted without any intimation. Wtf am I supposed to do now?

NEVER USE SEVALLA. They have fucked up support team.

Most of job post for DevOps on reddit are fake, If anyone asks you to interview under someone else's name, run.

If you ask for details, the real scheme comes out:

• The ask: You jump on Zoom calls, fake an accent, and clear technical rounds pretending to be a US-based guy.
• The deal: Split the paycheck 60/40 while they use their US citizenship to pass the background check, and you do all the actual work offshore.
• The catch: It’s straight-up identity and wire fraud. There's zero contract, so when they ghost you on payday, you get nothing. Plus, hiring platforms are heavily flagging biometric and IP mismatches now.

Anyone else seeing an influx of these messages recently? How is your team filtering them out?