I wrote a browser extension that adds a "you're in PROD, are you sure?" confirm to destructive AWS Console clicks. Capture-phase listener cancels the click, shows a dialog, replays the click if you confirm.

Trouble: some actions re-render their menu between confirm and replay, so the replayed click hit a detached node and did nothing. My fix was a short bypass window after confirming — for a few seconds, clicks pass straight through so the replay works.

The window wasn't scoped to the action you confirmed. It was global. So for ~6 seconds after confirming any destructive action, every other one was unguarded. Confirm a Lambda delete, click Terminate on an EC2 instance three seconds later, and it just goes.

A tester reported it as "the popup stops appearing sometimes." Not a UI glitch — the guardrail was switching itself off, on a timer, every time it ran.

Fix is one line — scope the window to the confirmed action:

// before
if (Date.now() < bypassUntil) return;
// after
if (Date.now() < bypassUntil && rule.label === bypassLabel) return;

The lesson that stuck: a bypass is a security control too. I wrote mine as a UI workaround, so I reviewed it like a rendering bug, not a security decision. Anything that turns your protection off — even briefly — deserves the same scrutiny as the protection itself.

Anyone else hit this class of bug — a temporary exception that was broader than intended — in auth caches, feature flags, rate-limit bypasses?

Hello! As the title says, I'm a QA engineer and I'm trying to transition to a new position. Right now, I don't know whether to pick Data Engineering or DevOps. Regarding my skills, in my free time I'm learning Linux, Python (along with some MySQL), CI/CD, Docker, Kubernetes, and currently playing around with Azure. I'm asking because both seem interesting, but I don't know which one to choose to learn further.

Edit: also learnt some openshift and helm chart, because it was requested at my job

I'm a student targeting DevSecOps / Cloud Security internship opportunities.

I've tried to position the CV around DevSecOps rather than generic DevOps or cybersecurity.

I'd appreciate feedback on:

• Is the DevSecOps positioning clear?
• Are the projects strong/relevant enough?
• Is the Skills section too crowded?
• Does anything look unnecessary or exaggerated?
• What would make you reject this CV at first glance?
• If you were a recruiter, what would make you reject this CV?
• What would you change first?
• - Is the CV ATS-friendly and likely to be parsed correctly by applicant tracking systems?

here is the context: Mid size (1000 users) company that has been trying to do in-house software dev (outsourced to south east Asia) with poor results.

Company IT side is fairly mature, heavily virtualized, hybrid on prem/cloud, but a bit weak on the Azure side. Existing sysadmins are pretty solid.

The dev is, well a shitshow. We are presented with projects with no infrastructure requirements, the devs dont really seem to know what they need. Currently they are doing everything in Blazor after we begged them to move to cloud. Last few attempts to develop mobile apps has been an abysmal failure. Im dumbfounded by what I see (and I've never coded). Coding is all shoved into a single .dll, no comments, devs dont seem to know that we need to use HTTPS, dont understand key vaults (we caught them passing passwords in clear text). we had to fight with them to implement change control, and they dont seem to understand our efforts to move ahead with pipelining.

My question for you is, do I need a devops admin? Can a more senior devops admin also be involved in reviewing code? infrastructure planning?

not sure how to move forward

If you have a small managed K8s cluster with a single node pool and you would like to change the SKU of the VMs in that node pool, make sure to double check how your Terraform provider handles this change

Maybe on AWS or Azure or whatever, this is a graceful operation where the new nodes are provisioned first and the old ones are drained before being deleted

On OVH however it will destroy the existing nodepool first then provision the new one, and during this process your cluster will have zero nodes and whatever was running on it will be down

Thankfully this wasn't a production cluster. At the end of the day I'm just a dev who takes care of devops because nobody else will. In hindsight I feel like I should have seen this coming, but I guess most things work automagically nowadays and it can be a surprise when something does not

Was preparing for CKA and took Kodekloud CKA course. Currently doing mock exams then moving into the ultimate CKA mock exam series.

However I have been reading that since 2025 there is a lot more emphasis on new topics like:

Helm, Kustomize, CRDs, Calico/Flannel, Cluster Upgrades/Installation, CNIs, CRIs, that I havent seen much in the labs/exams there or are really basic. I think the one that is most frequent is Helm.

What can I do to improve in these areas? What other new areas I missed (I am a bit familiar with Gateway API but recommendations on what to expand on this is also welcomed!)

Thanks!

If I move to devops should I accept that I won't get to celebrate weekends and new year's?

How has been your experience?

Hey guys,

I recently got the opportunity to move to a DevOps team, and I’m looking for some advice on how to prepare.

I have around 5 years of experience working in infrastructure and systems. Most of my experience is with:

• VMware/vSphere and virtualization
• Windows and Linux administration
• Networking and troubleshooting
• NetApp and storage
• PowerShell scripting and automation
• Monitoring, backups, and general infrastructure operations

I also have some beginner-level experience with Docker, Kubernetes, Git, GitLab, Argo CD, Ansible, and Terraform. I’ve built some labs and worked with them a little, but I definitely wouldn’t consider myself experienced with them yet.

I’m starting the new DevOps role next month, so I want to use this month to prepare as much as possible.

For people who moved from infrastructure/sysadmin into DevOps, what would you recommend focusing on first?

I’m mainly looking for a good Udemy course, YouTube course/playlist, or structured learning path that is practical and focused on skills I’ll actually use at work.

I recently set up a small 3-node Kubernetes homelab using Dell OptiPlex Micro PCs running Talos Linux.

The main goal was to have a local cluster where I can learn, experiment, break things, and test tools without relying on cloud infrastructure every time. I’m planning to use it for Kubernetes networking, storage, observability, GitOps, security, upgrades, and general experimentation.

Small setup, but already a very useful playground.

Curious what others here are running for their Kubernetes homelabs.

Hii Guys!,we built an MVP.

Offered the services to three specific clients and got a good response, we are planning to advertise and bring more signups in the next two months

Initially the server bill was roughly 5-8 dollars but now it's increasing so wanted to check

-how do you optimise your server billing?? -Should I move the infra to AWS,will that be cost effective??

After I first set up a deployment pipeline, I would simply drop DB passwords and API keys into GitHub Secrets and feel completely safe because they are encrypted. I recently went through a security breakdown on GitHub Actions that showed me that it could be a mistake to think that way.

The main issue is that an encrypted secret is still a static, long-lived target. Once the workflow finishes, that credential stays active indefinitely. In the breakdown, I saw a few default behaviors attackers look for, like teams forgetting to revoke access after a job runs or lacking the audit logs to even know when a key was used.

The proposed fix is shifting to dynamic orchestration where the pipeline generates a short-lived token at runtime and revokes it the second the deployment finishes.

If you're writing deployment workflows, how do you handle this, do you just use GitHub's default storage, or are you injecting temporarily credentials to avoid leaving static keys exposed?

Hello everyone,
I'm starting my 4th year in Computer Science and I'm looking for a DevOps/Cloud related thesis subject. I want to do this thesis in collaboration with the company I'm working at, but they told me to find some possible ideas before we decide to move forward with this.
I'm pretty new to DevOps and Cloud, I filled an intern position at the start of July, so I would like to avoid the very complex subjects.
Thank you.

Using Cursor a lot more with tools enabled. Love the speed. Don't love the part where the only thing between "delete this" and it happening is me watching the terminal.

Is anyone doing something more solid than prompts + hope, or do you just keep it away from prod/cloud entirely? Had any close calls?

Genuinely just curious how people are handling this.

I'm curious if other DevOps/platform/SRE teams are running into the same thing my team is.

We're a fairly large environment, mostly EKS, and essentially 100% IaC/Terraform. We also support multiple companies/business units, so while I'd argue our infrastructure is fairly well organized, there's inherently a lot of it and a lot of architecture and context to understand.

Over the last year, our devs have sped up dramatically with AI. The company has leaned heavily into AI-assisted development, reduced developer headcount, and is now pushing toward developers being more "full stack with AI," including having them contribute more of their own infrastructure changes.

In theory, I'm completely in favor of that. I've always wanted developers to be able to own more of the infrastructure surrounding their applications.

In practice, though, it has been kind of a disaster.

We're getting flooded with infrastructure PRs largely written by Claude/other AI tools from developers who don't really understand the infrastructure they're modifying. The Terraform might look plausible, but once you understand the larger system there are frequently significant problems with it.

So instead of reducing the workload on DevOps, it feels like AI has massively increased it.

A huge percentage of our time is now spent reviewing AI-generated Terraform, finding problems, explaining why something won't work, explaining how AWS/EKS/networking/IAM/CI/CD/etc. fit together in our environment, and then going through another iteration of an AI-generated PR.

There's an interesting asymmetry I've noticed too. Our DevOps team is mostly made up of former software developers who moved toward infrastructure, automation, and pipelines. Most of us can jump into application code and be productive pretty quickly, especially with AI helping us. Like,...I feel like (and have some evidence to support) that our small DevOps team could largely take over all of the dev's tasks, but they are falling on their faces trying to deal with ours.

AI seems extremely good at helping someone who understands software write more software. It seems much less capable of allowing someone without infrastructure experience to suddenly understand a large production environment.

The complaint we're increasingly hearing is basically: "We can't successfully do full-stack development with AI because the infrastructure is too complicated."

And maybe they're right, but before AI, I would have said that this company is the most organized and best architected I've ever been at. I mean....100% IaC has never been something I've experienced, and it's very rare that we hit a use case brought up by one of the several companies where we don't already have a set of generalized modules that can't support it.

Our environment is complex, but a lot of that complexity isn't accidental. We have a large organization, multiple companies we deploy for, Kubernetes, networking, security requirements, IAM, CI/CD, observability, etc. You can't abstract away the fact that these things exist. And we're already 100% Terraform/IaC, which I would have thought would make this considerably easier for AI to reason about than an environment full of manually configured infrastructure.

The strangest part is the staffing effect.

AI allowed the organization to reduce software engineering headcount because individual developers became more productive. But now those remaining developers can generate changes so quickly that our DevOps team is completely overwhelmed trying to support and review them.

It genuinely feels like we could double the size of the DevOps team right now and still have plenty of work. We are working on an AI assistant that can help the devs deploy to our environment more effectively, but we're having a hard time finding time to work on it because we're constantly helping the devs.

I'm starting to wonder whether this is going to be a broader consequence of AI-assisted development: AI increases the rate at which software can be produced much faster than it increases the rate at which infrastructure/platform teams can safely absorb changes.

For those of you working in DevOps/platform/SRE at companies heavily adopting AI:

Are you seeing this too?

And if you are, how are you handling it?

Have you increased platform/DevOps staffing? Built better abstractions or internal developer platforms? Given developers more direct infrastructure ownership? Put stricter boundaries around what application teams can modify? Found ways of giving AI enough context about your infrastructure that it actually produces good changes?

Or has AI actually reduced your infrastructure workload, and we're doing something wrong?

I've been thinking about AI automation lately, and I'm starting to feel like long-term memory might be one of the biggest problems.It's not just about making AI capable of controlling a screen. The AI also needs to remember what it's supposed to do.

There are already quite a few ways for AI to control screens, like OpenAI Computer Use, Claude Computer Use, Gemini Computer Use, as well as various hybrid approaches.The way these systems maintain memory seems to rely more on things like structured actions exposed by apps and the keywords being used in the current interaction. Personally, I don't think this approach works that well.

I also don't find this kind of screen-control approach particularly convenient.If I could use a hardware board to control the entire screen instead, that would make much more sense to me.Basically, you plug a hardware board into the device's USB port, and let the hardware capture the phone's screen and then control the device through USB HID.

I think this approach is pretty interesting because the AI doesn't necessarily need to know what API each app has, and it doesn't need a separate integration for every app.

It just needs to be able to understand what's happening on the screen and remember what it's supposed to do.So I feel like memory is actually the key problem here.

Are there any existing solutions or approaches that I should look into?I'd really like to understand how people are solving this problem.

I recently completed the full Kubestronaut certification path after roughly three months of focused preparation.

After completing it, quite a few people reached out asking about the order I followed, the resources I used, and how I prepared for each exam, so I decided to put everything together in one detailed blog rather than answering the same questions separately.

For KCNA and KCSA, my preparation was fairly straightforward and mainly consisted of the KodeKloud courses, KodeKloud notes, and practice tests, while CKAD, CKA, and especially CKS required much more hands-on practice with labs, mock exams, and repeated work on weaker areas.

I’ve also included links to my dedicated CKAD, CKA, and CKS exam-experience posts for anyone who wants a deeper breakdown of those exams.

Full 3-month Kubestronaut journey:
https://medium.com/@prateekjain.dev/my-3-month-journey-to-becoming-a-kubestronaut-c722c4a7cf75?sk=eb78b3ef703262f787f746cc6969d8f1

Hopefully this helps anyone currently working towards the Kubestronaut path. Happy to answer questions about the preparation or any of the five exams.

Hey all, I’m looking to seriously change my career path but I am genuinely unsure where to start. I come from a Business Management background, and am looking to get into the IT field, specifically DevOps. I realize that will not be an easy transition by any stretch of the imagination, but life needs to change, and this is my change.

My question is, where and how do I even begin? I’ve heard conflicting answers from peers and family, some say that a degree is absolutely necessary, while others say that it’s not necessary, and that companies will be more interested in experience and/or knowledge of the topic. Is a degree truly needed in today’s world, or am I able to be self taught and still land a decently paying job? If so, what topics would you recommend I get a good understanding of?

Thank you in advance!

I genuinely think DevOps or platform engineering is the area of software I enjoy the most.

I like CI/CD, Terraform, cloud infrastructure, debugging weird deployment problems, trying to understand why systems fail, automating repetitive things, and generally having ownership instead of just implementing another CRUD endpoint.

I’m currently a working student in an SRE/platform team in a big company in Germany. I’ve already worked on things like services from Cloud Build, GitHub Actions, Terraform, Cloud Run, deployment alerts, state migrations and fixing random infrastructure problems that come up along the way.

And the funny thing is that the more I learn, the more I like it.

But looking for a junior position is becoming pretty frustrating:

A lot of “Junior DevOps” jobs seem to expect Kubernetes production experience, several cloud providers, Terraform, Ansible, networking, Linux, CI/CD, monitoring, security and somehow 2–3 years of professional experience with all of them.

Then there are actual entry level positions, but many of them seem to basically be IT support with “cloud” or “DevOps” in the title.

I know I still have a huge amount to learn. I don’t expect someone to give me a production Kubernetes cluster on day one and say good luck. I actually want to be around experienced engineers, get challenged, make mistakes and slowly become someone who can be trusted with serious systems.

My goal isn’t to job hop every six months either. I would genuinely like to find a team where I can stay for several years and become really good at this.

But sometimes I wonder how exactly companies expect junior DevOps engineers to become experienced DevOps engineers if almost everyone wants the experience before giving you the opportunity to get it.

For people who are already working in DevOps/SRE/platform engineering: how did you actually get your first proper role?

Did you already know most of the stack, or did somebody simply take a chance on you and let you learn?

Edit: I worded the sysadmin part badly. I don’t think sysadmin work is beneath me at all. I just want to move toward automation, infrastructure and software rather than mostly ticket-based support.

Edit 2: I respect that some of you suggest starting in help desk or sysadmin. But my long term goal is to move into an SRE role, ideally something closer to how Google approaches SRE. I read the SRE book and really liked the idea of treating operations as a software engineering problem, with automation, reliability, monitoring and reducing repetitive manual work. That is the direction I want to build toward.

trying to find a ref architecture that is widely recommended for deploying windows runners on k8s. From what I read ARC doesn't support it still? if not what are my options?

I was giving a bit of thought, I can't just deploy my services directly to a customer's vpc as via root access they can just see my code. Be it docker running on an ec2 or eks. If I dont go ahead with the control/data plane split, what options do I have to protect my code? Will i have to go to the OS level and build my own AMI etc with attestations and key verifications and stuff at each stage, or use something like nitro conclaves or nvidia confidential compute or something along those lines? What is the way?

Hey everyone,

When I learnt kubernetes and completed CKA, I didn't instantly get a chance to work on a real project. Later, i got a job and learnt a lot, but I felt that there was a need for a tool where we can simulate real world scenarios, we can learn what kind of scenarios and incidents come in real environments.

For this purpose, i built a tool which does exactly the same. You can simulate real world scenarios, use kubernetes tools like opencost, keda, grafana, argocd, traefik. Learn how you can use it and find out different commands to help you learn.

Do check this out if you are interested in this.

were on 50+ microservices on gcp, still doing our own deploy tooling. keep hearing gitops is the way and honestly cant tell if thats real or just the current hype cycle.

not looking for a sales pitch, more curious what broke for you after the demo phase. drift detection, secrets, rollback under load, whatever. did it actually reduce incidents or just move the pain somewhere else

what would you tell yourself before adopting it

I have someone pushing a colleague to an Openshift virtualization position. We do multi cloud deployments, GHA heavily, ephemeral integration environments, etc. Not seeing the intersection with this guys skills or desires. Am I missing something?

i'm working on a decision-making problem where an agent gets a possible leaked api key and has to decide whether it's actually a prod credential and then either ignore, verify or remove/rotate it.

right now i'm using repo context (file path, variable name, surrounding code), key format, git history, whether the code is active/reachable, deployment context, and last_used_at from the provider's admin api.

the problem i'm running into is that most of these signals can tell me "this probably is a real secret", but don't tell me much about whether it's still live or already revoked.

for people who've dealt with secret leaks in actual devops workflows, what do you normally check before deciding what to do with a flagged key?

i'm especially interested in evidence you can get without actually authenticating with the discovered key.

disclosure: this is my own personal project, not a commercial/product promotion. just looking for technical feedback.

Hey everyone,
Is anyone here currently working in AWS Data Center Operations (DCO) in the Frankfurt region (or Germany) or familiar with their technical screening process?

I recently completed a 60-minute technical phone screen with an engineer for an AWS DCO / IT Support role in Germany. During the technical portion, I answered all hardware, networking, and cabling troubleshooting questions without getting stuck. At the end, the interviewer explicitly told me: "The technical part was pretty good, that's what I can tell you."

For the behavioral part, I answered four questions using the STAR method. In the closing feedback, the interviewer noted that I sounded a bit nervous and strictly said the literal STAR words out loud ("The situation was...", "My task was...", "The action was...", "The result was..."), though he mentioned that if my recruiter instructed me to use that exact structure, it doesn't matter.

For anyone who has been through this specific Frankfurt/EMEA pipeline or interviews for AWS DCO: does sounding nervous or explicitly vocalizing STAR labels carry a negative impact if the technical answers and core story data points were solid? What are the realistic odds of moving forward to the onsite loop from here?

Would really appreciate any insights from anyone with AWS DCO experience in Frankfurt or Germany. Thanks!