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Reddit DevOps. #devops Thanks @reddit2telegram and @r_channels
Anyone else interested in connecting with DevOps folks from different parts of the world?
I recently came across a post on Reddit from someone looking to connect with other DevOps professionals. I reached out, and we ended up moving the conversation to Discord.
It's been pretty nice having a place where we can talk about things we're working on, exchange ideas, troubleshoot things, and share what we've learned.
But the conversations haven't really stayed limited to DevOps - which is actually what I've enjoyed most.
We sometimes end up talking about things like:
How DevOps/tech work differs between countries
Differences in workplace and engineering culture
How people got into the industry and how their careers have developed
Tools, practices, and technologies people are using
Interesting things we're currently learning or building
And occasionally just completely random stuff 😄
I'd like to meet more people from different parts of the world and have more of these conversations.
You don't need to be a DevOps expert or have 10+ years of experience. If you work somewhere around DevOps, SRE, cloud, infrastructure, platform engineering, sysadmin, or software engineering - or you're simply learning in that direction - feel free to reach out.
We're currently a small group, and we'd like to keep it that way rather than turn it into a huge server full of people who never talk.
If you'd be interested in chatting and getting to know some other people in the space, leave a comment or send me a DM. Happy to connect.
https://redd.it/1w6w8c3
@r_devops
Jenkins Tutorial recommendations required
So mu company has asked me to learn Jenkians. Which tutorials or playlist would you suggest to learn jenkins? Please give your suggestions.
Also, how much time does it require to learn so that I can start writing basic CI/CD pipelines.
https://redd.it/1w6fkhr
@r_devops
Moving to DEVOPS
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.
https://redd.it/1w65k4c
@r_devops
What's actually stopping your agent from doing something stupid in prod?
Every time this comes up the answer is "run it in a sandbox." Which, sure. But the stuff I actually want an agent for lives in staging and prod. Sandboxing it kind of just means it can't do the thing I wanted it to do in the first place.
So right now my entire safety net is me reading the command before I hit approve, and I'm not going to pretend I'm still reading those properly by the tenth one.
Is anyone running something that's actually built for this? As in it sees a dangerous command and kills it on its own, instead of me being the last line of defense. Or is everyone just living with it, because from the outside that's kind of what it looks like.
https://redd.it/1w5yefy
@r_devops
Stuck with progression
Been in IT for about 2 years. Have Compia Trifecta, CCNA, jamf pro. After the CCNA been stuck not knowing what to do. Been studying the MD-102 Endpoint cert for about a month but it’s so boring and I don’t think I want to become an endpoint/Intune admin. Prob will drop it.
My thought process is to create a very basic full stack web app (don’t know any frontend and only had a month studying python) -> then host it on my proxmox using terraform and docker as a learning tool for devops
Is this fine? Any suggestion? Creating the web app will take a while 1000%, and devops don’t need frontend knowledge, but there’s an itch in my brain to learn it though.
https://redd.it/1w5x0v0
@r_devops
Anyone else nervous about what coding agents can actually run?
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.
https://redd.it/1w5p8cl
@r_devops
Need Help , please some one give KT
Need help?
Anyone with experience in Devops, CI CD, pipelines, docker , kubernetes , Linux, Terraform, AI infra or any relevant Devops skill .
Can you guys please give me KT in your Free time over weekend or weekdays at your comfortable time...
I am having experience in IT , learning devops but lacking hands-on experience or real project experience in devops... And due to this I am not having confidence in interview. I fumble when I am asked about project experience.
I can buy you coffee or beer 🙏
https://redd.it/1w5kd34
@r_devops
Brain fart moment this morning
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
https://redd.it/1w5ewk5
@r_devops
How do I protect my IP for on prem/byoc deployments
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?
https://redd.it/1w5117k
@r_devops
Undergraduate Thesis Subject
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.
https://redd.it/1w50zb2
@r_devops
Built this tool to simulate real world scenarios and incidents in local kubernetes
https://github.com/sagar2395/snowopslabs
https://redd.it/1w4ll7u
@r_devops
Built a small 3-node Kubernetes homelab
https://redd.it/1w4hanq
@r_devops
i'm struggling with the evidence side of secret detection
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.
https://redd.it/1w4c1cl
@r_devops
How to quickly provision a website/company stack? domain/email/hosting
Say I want to establish a company or an organization's representation on the web quickly. The 'stack' involves domain, email in/out, public-facing website, (optionally) social media presence.
Are there any products out there that unify the 'stack' and allow provisioning from minimal amount of steps? Or do I need to set up each item through separate providers?
Ideal if the solution has an MCP or cli.
Thanks ❤️
https://redd.it/1w44la7
@r_devops
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?
https://redd.it/1w42bxs
@r_devops
CV Review — DevSecOps / Cloud Security internship
https://redd.it/1w6odjd
@r_devops
Do you build your own pipelines?
I work in a small team that deploys some internal products. No big user pool or database for me! We build small web apps that automate inner processes, and some scripts and jobs. Most of it runs on k8s, the apps and the jobs. We mostly run everyting on Azure. And everyting is managed by code, obviously, so we've build GitHub actions pipeline that live with the code to deploy our infra via bicep and the code via a test-build-deploy-promote pipeline, started on push.
But I've seen some platforms that propose full CI/CD as a services, and it feels like managing the pipeline yourself these days is a bad thing. To me, deploying a solution, especially in the containerization era, is simple; Run the tests, build the image, deploy the workload. Add some customization related to the app if needed (specific parameters, logging or testing jobs, etc). But is managing this code yourself a bad practice? I have to say, it does make for a lot of duplicate code in a lot of repos. The pipelines are very similar.
Should we always aim to use a standardized CI/CD platform? What tools do you use for CI/CD? How different do you handle it from a small project to a more important service?
https://redd.it/1w6cb8k
@r_devops
Help on picking which one to choose between data engineering and devops to transition from QA
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
https://redd.it/1w612ly
@r_devops
Cloud Ops engineer (~2 YOE) - are these the right things to be building to grow into DevOps/SRE, or am I stacking resume-bait?
I run Cloud Operations across 8+ AWS accounts and I'm trying to deepen into real DevOps/SRE engineering rather than stay in monitoring-and-tickets land.
Looking for an honest technical gut-check on whether my day-to-day and my side projects are pointed at the right skills, or whether I'm building shallow things that look good on paper but don't hold up.
What the day job actually involves:
CloudWatch + EventBridge/SNS alerting across 8+ client accounts, SLA-based incident triage and root-cause isolation
Python (Boto3) and Bash automation for recurring housekeeping and health checks
SSM Patch Manager patching across \~15–20 Linux/Windows servers, plus in-place Windows Server upgrades
IAM least-privilege, KMS encryption, security groups, Site-to-Site VPN setup
AMI lifecycle management and a cross-region migration (AMI replication + post-migration validation)
Side projects (all Terraform + GitHub Actions):
An AIOps incident-diagnosis agent on AWS Bedrock — reads CloudWatch alarms, runs RAG over runbooks, returns a ranked remediation plan. Read-only, human approval before anything executes. Event-driven stack (Step Functions, Lambda, Bedrock KBs, Guardrails).
A CI/CD pipeline to ECS Fargate — GitHub Actions tests/builds/scans/pushes a Spring Boot API to ECR, deploys behind an ALB. Provisioned VPC + endpoints with Terraform, debugged endpoint/S3-gateway/ALB health-check timing to get zero-downtime deploys.
A self-healing EKS platform — multi-replica Deployments, Prometheus/Grafana, HPA scaling on CPU with automatic pod recovery.
The questions I actually want answered:
Depth vs breadth — is this a coherent skill progression, or three unrelated demos? Would you rather see one of these taken much deeper (real load, chaos testing, actual SLOs) than three at surface level?
The AIOps/Bedrock project — does a GenAI ops agent read as genuinely useful, or as hype-chasing that experienced engineers roll their eyes at?
What's missing — if you were leveling someone from Cloud Ops toward a solid AWS/DevOps/SRE bar, what's the single biggest gap in the above? (Observability depth? Real SLO/error-budget work? Networking? Something I'm not even naming?)
What would you build next in my position, and why?
Happy to share my resume personally if needed.
https://redd.it/1w5va0w
@r_devops
How do you solve long-term memory in AI automation workflows?
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.
https://redd.it/1w5iqqo
@r_devops
CKA additional resources
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!
https://redd.it/1w5ovti
@r_devops
honest worry or impostor syndrome
This is officially my first post on a reddit sub, i tend to just be reading a lot from here, but getting to the issue, recently i got a new role as DevOps Engineer mid level, chill job, making a new architecture to migrate one heavy ETL app, just one month in, not much meetings, no manager pursuing me every day, a really chill job (OBS: 8yoe on IT \~2-3yoe in Sysadmin/DevOps)
My biggest worry is on how i work, months from now i was an Tech Lead on a AI first company, so i got pretty used to use AI for a lot (ngl it facilitates it, really). Today i use it to make Pipelines templates, bicep templates and even troubleshoot some stuff regarding infrastructure.
The problem is, im using it too much and i dont know if this is a standard nowadays (i mean, it accelerate things BY A LOT if you know how to properly use it) or if im just being a dumbass that is outsourcing my own work just to not touch code.
How is it going for you guys? Maybe i should stop a bit and do something 100% by hand, intentionally delay stuff so i can get work on my own... Really confuse if this is acting correctly and i'm just with impostor syndrome (again).
https://redd.it/1w5jucj
@r_devops
do I need a devops person or a sysadmin?
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
https://redd.it/1w58lld
@r_devops
Looking for DevOps Freelance / Part-Time Work
Hi everyone,
I’m a DevOps/Cloud Engineer with around 4 years of hands-on experience and I’m currently looking for freelance, part-time, or short-term remote DevOps work.
I can help with:
AWS & cloud infrastructure
Docker & Kubernetes
Terraform / Infrastructure as Code
Jenkins / GitLab CI/CD
Ansible
Linux administration
Python & Shell scripting
CI/CD pipeline setup and automation
Troubleshooting and deployment automation
I’m open to small tasks, ongoing freelance work, or project-based work.
If you or your team needs help with DevOps/automation, feel free to DM me. Happy to discuss the requirement and see if I can help.
Thanks!
https://redd.it/1w50ger
@r_devops
github windows self hosted runners (on k8s). Is it supported?
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?
https://redd.it/1w4ulqz
@r_devops
How do you handle CI/CD credentials? Using GitHub Actions made me realize static encrypted secrets aren’t very safe.
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?
https://redd.it/1w4h8lc
@r_devops
Openshift devops?
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?
https://redd.it/1w478tg
@r_devops
Datadog Bill just spiked and we are evaluating alternative -what did you switch to?
Small team just got hit with a much bigger bill than expected starting to look at alternatives but there is a lot of noise out there
What did you move to and what made you trust it enough to actually switch and did you find anything that helped with actually diagnosing issues faster not just storing logs cheaper
https://redd.it/1w4askj
@r_devops
My 3-month journey to becoming a Kubestronaut
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:
prateekjain.dev/my-3-month-journey-to-becoming-a-kubestronaut-c722c4a7cf75?sk=eb78b3ef703262f787f746cc6969d8f1">prateekjain.dev/my-3-month-journey-to-becoming-a-kubestronaut-c722c4a7cf75?sk=eb78b3ef703262f787f746cc6969d8f1" rel="nofollow">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.
https://redd.it/1w45n0z
@r_devops
Anyone else seeing AI make DevOps/infra the bottleneck?
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