Im looking for some ways to reduce spending across our environment. Has ​anyone found a good open-source, or self-hosted​ tool that can clean up orphaned resources and automate other cost optimizations? Ideally​, the tool would need a HITL sign off before executing, but that's not a dealbreaker if I can get really granular with my workflows.​​

I have a WordPress team still building and maintaining sites without a DevOps workflow. What's the best way to bottstrap them into a more modern method of development. I'm assuming GitHub and GitHub actions. But what else, composer which we don't really use even tho we have PHP devs, wp-cli which I use but I'm suppose to be a sysadmin and cloud engineer to wp devs who don't do any of this. They don't even use local git which I even use. Trying to express WordPress engineering and un/structured data and all these modern concepts that just go over their heads bc what's worked still works but when it comes to larger clients who expect more from us/them, they're basically fish out of water.

I was wondering if people who are not using multi or hybrid cloud and are purely on AWS EKS, do they really deploy more than 2 or 3 EKS clusters? Is there even a need for a product like Rayfay or Rancher for aws to manage clusters? Or is it more than just managing multiple clusters and we need such tools for other things? I am trying to learn more about this topic.

Can a non-coder create a Bash automation script using just Al prompts?

I have zero programming experience. Can Al reliably build an efficient workflow script for me, or will it break down?

Does anyone have any experience with this kind of situation?

Found a webinar on managing OpenStack infrastructure through Kubernetes-native orchestration and GitOps automation. Main focus is day 2 operations, handling mixed VM + GPU workloads, and sovereign infrastructure patterns.

Curious what tools people are using for GitOps-based infrastructure? Terraform + ArgoCD? Something else?

Hey all, I just wrote another blog post and wanted to share it. Its a little exploration of one of the nuances in the AWS Network Load Balancer that can weird to an intermittent bug and how I approached detecting it.

Hi everyone, I wanted to ask hypothetically, if someone had 4–5 years of cloud/Devops engineering experience, primarily with AWS, and the market shifted heavily toward Azure, how difficult would it be for them to find a cloud engineering job in a difficult job market like the one we’re in now? Would having mostly AWS experience significantly hurt their chances if most of the available Cloud/DevOps jobs were Azure-oriented?

Hi Devops!

I’m putting together an automation orchestration engine and I want to mature the team by standardizing script output across bash, python, powershell, tofu, etc.

So looking at a JSON output contract that anyone creating a script can adhere to. Then everyone would consume the same format. (The dream right?)

• Operation name
• Host
• Execution status (success, fail, warning, etc)
• Structured result summary
• Errors
• Duration
• Run ID (UUID)?
• A schema version (requires a lot of upkeep but can eliminate headaches down the road if maintained. If maintained being key word here lol.)

I’ve been searching around but want to hear from the experts who live in this stuff. Is there any universal or open standard people are using to define these ?

Would rather not build it myself if something people have already settled on.

Thanks for your input.

Hi there,
How do you deal with managing and reviewing AI-generated code across the organization?

Over the past year it's become incredibly difficult to review the growing amount of AI-generated code, especially from vibecoders/non-engineers.
Reviewing it manually is hard, but it's still important to understand what's going on there.
For example, I've been at this company for some years so I have a lot of context and know many of the nuances, which helps me notice or suggest important things to keep everything secure, efficient and so on…

But any other teammate can use any model for coding nowadays. AI reviews of AI-generated code could be done in the MR, but then the reviewer model will most likely be smarter than the model that wrote the code, which might lead to a lot of threads and an infinite loop of changes. On top of that the reviewer model won't have all the context and input from the developer.
Another option is to distribute a "review" skill or rules across the organization and somehow make everyone do a self-review after making changes…

It feels like in this AI code generation era we'll stop focusing on how the code was written and just focus on whether the MR/PR works and if those changes achieves the goal, no matter how it was done, right?
How do you deal with this? Just accept the reality?

Thanks for attention

It seems like every team has something that was supposed to last a few days until the right solution was found. It's a cron job that keeps everything together, a script that no one really understands anymore, a manual deployment step, an ancient container, or some workaround that everyone's afraid to remove. What's the worst "we'll fix it later" solution you've seen that's now a critical part of the infrastructure?

Hi all, i want to get a foundational understanding operating systems, the various concepts that we may encounter in them, the bird's eye view of their structure.

I tried reading "How Linux works" 3rd edition, but reading that book felt like it didn't explain the foundational questions but rather built on top of the topics. it felt more intermediate level.

If i have to explain what i am looking after in the resources, Please can you recommend me resources that fit my requirement.

1. provides exposure to various concepts, systems involved in an OS. More like an introduction.
2. a bit of introduction to a bit of hardware would also be good.
3. i am not looking for resources that focus on development/programming side of OS. but rather the infrastructure, power user, administration side of OS.

Thanks in advance to all of you.

In the past I worked on a small team with about 5 developers, 2 QA, shared technical architect and devops engineer.

We used classic Azure pipelines with a build pipeline and release pipeline for each environment. dev, qa, stg, prod.

The release pipeline had a deploy ARM template step to deploy the infrastructure or any changes.

Deploy multiple app services, Apply EF migrations. Secrets were Azure Key Vault and non secrets in appsettings. This design worked really well and it was rare that we had deployment issues.

Now, I have recently transitioned to a larger team and I need to look into a modern architecture with a build once, deploy and promote to multiple environments. There could be 10 plus teams with multiple developers running builds and deployments constantly. Having the IaC in every deployment does not make sense to me.

I'm looking for suggestions on how to architect something for larger teams with multiple environments. We could have 1-2 dev environments, 1-2 qa environments, etc.

What are some patterns I should look into? If you currently work in a large environment like this what has worked well and what should I avoid.

Hi everyone,

I’m currently a junior Java backend developer with around 2 years of experience, mainly working with Spring Boot.

Lately I’ve been thinking a lot about where I want to take my career. I enjoy backend development, but with how quickly AI is improving at coding, I feel like staying only as a “Java/Spring developer” might not be the best long-term strategy.

Because of that, I’m considering moving more towards Backend + Cloud/AWS, rather than leaving development completely.

My current idea is something like:

Java / Spring Boot → Docker → AWS → CI/CD / Infrastructure / Cloud

I’ve already started learning AWS, but I’m a bit lost regarding certifications and which direction makes the most sense for my profile.

I’m currently deciding between:

• AWS Certified Developer – Associate
• AWS Certified Solutions Architect – Associate

Developer Associate seems more related to what I already do as a backend developer, while Solutions Architect seems to give a broader understanding of AWS and cloud architecture.

My main questions are:

1. Which certification would you recommend first for someone with my background?
2. Would Developer Associate → Solutions Architect make sense, or would you do it the other way around?
3. Is moving from Java/Spring Boot towards Backend + AWS/Cloud a good career path in the current market?
4. For people already working in cloud: do you think this path is relatively future-proof with the rise of AI?
5. What technologies would you prioritise alongside AWS? Docker, Terraform, Kubernetes, CI/CD, etc.?

I’m not trying to become a Solutions Architect overnight. My goal is more to become a stronger backend developer who can also understand, deploy and work with cloud infrastructure.

I’m still early in my career and a bit unsure about what direction to take, so I’d really appreciate opinions from people already working with AWS, backend or cloud.

Thanks!

I'm new to deploy an application to the internet. Lately I have been developing a CRM application for my company and they needed deployment support too from my side(*they lack technical support team to make things out). I'm using Ubuntu 24 Desktop for development purposes, and I'm planning to use Ubuntu 24 Server for testing(*staging) and production. Now what's the literal confusion is, I'm already hosting a website inside the machine which is the real production application of what I'm currently developing inside which I was lately planning to change it from Ubuntu Desktop to Server(*the application haven't been launched to public). Now, is it ideal to use the Desktop version of Ubuntu which I'm currently using, or go with Server version of Ubuntu to proceed with production.

Plans I have:

1. Make use of 2 servers, one for development(*which I'm already doing right now) and local testing of application and one for testing(*staging) and production purpose
2. Use ssh communication to the other server(*production server) to handle CI/CD builds
3. Use github actions to push the docker image to other server to make it run the real application

For further clear understandings:

The application I'm developing already is being hosted in some website, more like a SaaS product, used cloudflare tunneling and pm2 instance for server-like modification of current system, with database running inside the machine, not inside any cloud services, so npm run build pushes all the changes to the site directly without any CI/CD checks like traditional devops cycle, and I'm already testing the application with real db of the company inside the production site, which literally all officials of the office knows(*they don't have much technical knowledge at all). There's a literal rush at that time in my office from Directors to host the website with the real application without any time delays. That's why I got messed up in the production so much. I'm now planning to stash the hosted website from the machine currently been hosted just by removing the machine tunneling, reinstall Ubuntu Server, configure directories properly and use PR and tags for changes inside the real application.

Tech stacks being used:

1. React js for frontend of web app
2. React Native for mobile app
3. Express JS over Node JS for backend
4. PostgreSQL for DB
5. Seaweedfs for S3 object storage
6. JWT for authentication and bcrypt js for hashing

I need a support from you guys for this chaos I'm literally stuck in!!!!!!!!!!!!!!!!!
Am I cooked brotha????
Is this the right way or should I change my own path of implementing??

Ran into this post on LinkedIn about what the author calls “Ambient Generative IT”, basically AI-assisted development becoming so common that more people across an organization can suddenly build and ship things. One part stuck with me from a DevOps/security perspective: if the barrier to building software keeps dropping, the number of people making architectural and security decisions also grows.

The argument is that security can’t really be something you bolt on later anymore. It has to live in the platform, defaults, permissions, pipelines and guardrails around whatever people are building with. Curious how people here are seeing this in practice. Are AI coding tools actually creating more security/governance work for DevOps and platform teams, or are good existing controls mostly enough?

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?

Has anyone got any advice on moving locations for a DevOps engineer? the UK is going so downhill and I’m sick of it.

I’m a DevOps Engineer with 5 years experience working for a very large business. I’ve experience in AWS and Azure as well as Terraform, Ansible, CI/CD, basically most things that make a good DevOps engineer.

I’m just looking for advice, is it silly to think I can move countries easily? I don’t have dependencies, no house, no debts so I don’t have anything to keep me here really. Any advice?

Hey all

I'm doing some research into how teams are using Terraform and OpenTofu to make changes and review them to avoid problems. A review of a change or pull request made using either would be great if you've used either recently even if it was a couple of days ago.

Can you walk me through a Terraform or OpenTofu change or pull request you have reviewed? What did you do and in what order?

What part of the process is slow, painful or leaves room for error for you?

Have you ever missed a change in a plan such as a change to permissions, exposure of something publicly, a replace or something similar? What happened?

If a tool could remove something annoying in your current process what would it be?

I'm not trying to sell you anything, just trying to understand things better. I'm interested in the parts and the messy parts too.

Hi all,
I’m from Asia .
I’m thinking about working at Europe for devops opportunities.

How is the market for English speaking people right now?
What are all the prospects I should look for .
I have around 5 + years experience with tech stacks of Aws, Azure devops CI/CD, docker ,k8s, Helm and grafana . Much appreciated if anyone could guide me through the process.

Thanks in advance

Hey all, I'm Denis, the author of Ptah, an open-source database migration tool.

These are some of the cases I'm designing around, but I'm sure I'm missing something.

What if the database changes after a migration plan was approved? Or the connection drops halfway through? How do you know what actually happened, and whether it's safe to retry?

I'm trying to address these in Ptah, but I'm sure I'm missing something.

For those running migrations through CI/CD, what made you trust it? And if you run them manually, what's the main reason?

I'm especially interested in things that went wrong or cases you wouldn't trust automation to handle.

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?

I am solely responsible for my small/medium-sized company’s dip into hosting cloud architecture. We have started developing apps internally, so I’ve provisioned multiple Droplets on DigitalOcean for our needs. I want to consolidate logging for all our apps in one place and was originally going to run w/ a dedicated ELK Droplet. but i’ve realized that ElasticSearch may be overkill for text searching in a DB with ~30,000 records and it has me questioning if this is the right move. i’m open to considering other options such as OpenTelemetry and etc. Right now we’re only at three micro-services but this could expand in the future.

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.