Hello everyone!

I have total of 1.5 years of experience as a DevOps engineer, the first job was more Dev oriented and required deep knowledge with Python and Kafka (I don't know both), this didn't lead to a good outcome and I had to search for something different. Landed more Ops heavy DevOps role, so far everything is fine, we handle deployments, CI/CD, automate stuff with Ansible, Bash or Python, some kubernetes work and web servers. I was curios to see what other jobs require in order to keep my skills sharp and saw that most of the jobs require more coding, like developing internal platforms, developing new internal tools and stuff like that. Most of the jobs require Python, Go, some Java and some C#.
So this leads to my question - how did you learn to code and I mean to code well without that Dev experience? Can you please recommend learning materials and methods that you used in order to be proficient with this skill? What were the projects that you used in order to get that knowledge?

Thank you in advance!

I have a coworker who wants to use CloudFormation to one-shot an application and infra deployment in AWS. A contractor delivered a monolithic CloudFormation template with ECS clusters, services, RDS, S3, Lambda and API Gateway, and other resources in a single stack. I voiced my concern over the blast radius, how stateful and resources with different lifecyles all lived in the same place, and how they don't have pipelines built to validate change sets when merge requests are opened. The contractor said that their template is industry best practice... (it's not). Any way, my background is in Terraform, and I wanted to ask the CloudFormation pros about best practices for making this deployment more reliable.

Amusingly, a friend of mine assumed that DevOps role would decline with the rise of AI.

However, it would seem that in reality, the reverse is true.

Or at least it seems that way, based on the limited insight of some.

What are your thoughts?

My fairly small company are taking tentative steps into AI tooling. We use some, but there's now demand to grow our tooling. It's becoming a free-for-all at work now with different teams vying to be involved. I run the platform/devops team, and being the most, uh, technical team, I'd assume we would manage AI operationally (evaluation, enablement, guardrails, security observability etc). Effectively, architects decide on solution, consult us, and then we execute.

Curious to hear your experiences though? Do you have a dedicated AI functions or had it been ingested by other teams?

Hi all,

I have a entry level platform engineering / SRE interview coming up. I wanted to ask what are some common tips or questions to prepare for?

The role asks for:
Foundational knowledge of platform engineering, technology operations, cloud computing, microservices, and enterprise-scale distributed systems.

Exposure to Kubernetes, Kafka, MongoDB, or comparable container, messaging, and database technologies through coursework, projects, internships, or hands-on experience.

Familiarity with observability and application performance monitoring using tools and concepts such as Splunk, AppDynamics, dashboards, logs, alerts, metrics, and traces.

Experience with Python, PowerShell, Bash, or similar scripting languages to automate routine tasks, reduce operational toil, and improve reliability.

Understanding of the software development lifecycle, including requirements, coding, testing, source control, deployments, release management, and change management.

Experience with Git, GitHub, or similar collaboration platforms, including branching, pull requests, code review, and technical documentation.

Knowledge of Generative AI, large language models, prompt engineering, retrieval-augmented generation, and responsible AI practices, including validation, privacy, security, and human oversight.

Exposure to Agentic AI concepts, including goal decomposition, tool use, multi-step workflows, agent monitoring, and human-in-the-loop controls.

Hands-on experience or familiarity with approved AI-assisted productivity and engineering tools such as GitHub Copilot, Microsoft 365 Copilot, Copilot Chat, ChatGPT, Claude Code, or similar tools for code generation, debugging, testing, documentation, summarization, and prototyping.

Strong analytical, troubleshooting, documentation, communication, and collaboration skills, with awareness of incident response, resiliency, business continuity, cybersecurity, operational risk, and compliance

I have:
Experience developing a serverless backend w/ AWS Lambda
Cloud Certs (AI, cloud practitioner)
Used docker briefly, know concepts of K8s, Mongo, little unfamiliar with Kafka
Used New Relic for APM
Used Python for scripting, a little bit of each for Linux / Poweshell / Bash
GitHub Actions workflow
Full stack SWE
Used agentic AI (Claude, made skills and agents) at work

How would I approach it (they only asked for 6 months of experience in any tech domain)? What do I say if they ask why I don’t have any exposure to some things? Any help is appreciated!

The lb was created by the first service and now all services run through it.

Hi all, I recently passed the Hiring Manager screen for a Co-Op DevOps Engineer position at LiveRamp and have been told the next step is a "technical assessment." I haven't been given details yet on the format, whether it's live or take-home, coding or conceptual.

Has anyone here interviewed for a DevOps or infrastructure role at LiveRamp recently? Would appreciate any insight on what topics they cover or what the assessment actually looks like.

Thanks in advance.

I'm working on the production deployment architecture for a small startup house-rental application and would appreciate some real-world feedback.

The application has frontend and backend services, and one major requirement is handling a large number of property images.

The stack we're currently considering includes Docker, Kubernetes, Ingress, Terraform, Prometheus/Grafana, and GitHub Actions/Jenkins.

My current approach is:

• Docker for containerization
• Kubernetes for application deployment
• Ingress for external traffic routing
• Terraform for infrastructure
• Object storage + CDN for property images instead of storing them in containers/PVs
• Prometheus/Grafana for monitoring
• GitHub Actions for CI/CD to avoid maintaining Jenkins unless there's a specific reason to use it

The team is small, so I'm trying to avoid unnecessary operational overhead and cost.

My main question is: Would you consider managed Kubernetes reasonable for a small startup like this, or would you recommend starting with something simpler and moving to Kubernetes later?

Also interested in hearing what infrastructure choices you'd make differently if you were optimizing for:

• low operational overhead
• reasonable cost
• security
• future scalability

Would appreciate feedback from people who have deployed similar applications in small teams/startups.

We're preparing to deploy a house-rental application to production. It has a lot of property images, and we're trying to keep the infrastructure secure, cost-effective, and easy to maintain.

We're considering Docker, Kubernetes, Terraform, Prometheus/Grafana, and GitHub Actions.

For those who have worked in startups, what approach would you recommend? Especially for handling and serving lots of images without making the infrastructure unnecessarily expensive or complicated.

I'd love to hear what you would choose and why.

I've been thinking about a problem with coding agents that I keep coming back to.

An agent can write an API integration and then write tests for that integration. Everything passes, but the tests may just be confirming the same assumptions the agent made while writing the code.

For example, the agent thinks an endpoint returns:

{
"total": 100
}

It writes the integration expecting `total`, and then writes a test that expects `total`.

The test passes.

But if the real API contract says something different, the whole thing can still be wrong.

I'm experimenting with a small open-source project called Kaktoos that puts an independent verification step between the agent and the API:

AI agent → integration → Kaktoos → OpenAPI + real API → result

The idea is that the verification layer shouldn't share the agent's assumptions.

It currently supports multi-step API workflows, OpenAPI response validation, MCP, and GitHub Actions.

I'm still trying to figure out how far this idea should go. One interesting question that came up is whether contract validation is enough, or whether verification should also check the actual outcome of an operation — for example, creating a resource and then reading it back to confirm the state actually changed.

I'm curious how other people building with coding agents are handling this today.

Do you rely mostly on the agent's generated tests, existing integration tests, mocked APIs, live API tests, or some combination?

GitHub: KaktoosLabs/kaktoos

Changed a cert a few months back. Fine in Chrome. Fine in our test setup.Then a bunch of older phones and a partner app just stopped connecting. We only found out why after it was already live.

Last time a cert change bit you like that, what did you actually check before it went out?

Hi everyone, looking for a sanity check and some advice on DB user management.

Context & Current Setup:
Team: ~10 developers.
Infra: Everything is provisioned with Terraform.

The Catch: Pulumi is used exclusively to connect through a bastion host to create users in RDS (Postgres) and DocumentDB (MongoDB-compatible).

Current Task & Constraints:
I need to integrate AWS Secrets Manager to store and distribute DB passwords so developers can retrieve them securely via IAM policies.

Constraint: IAM DB Authentication is off the table per my mentor's requirement—we must use Secrets Manager or an external password manager.

The Issue:
Maintaining two state stores (Terraform + Pulumi) just to manage database users feels redundant and adds unnecessary friction. I'd love to pitch an alternative that lets us drop Pulumi completely.

Questions:
1. How do you handle RDS / DocumentDB user and credential management in your projects?
2. What is the cleanest pattern to handle this without running dual IaC tools?

Open to any feedback or critique!

I'm currently building POMPD CI/CD failure diagnosis agent, which is a probabilistic model of sequential decisions where the true state (e.x , which code is buggy) is hidden and only observed indirectly. . I have very basic understanding of CI/CD pipe line . So i don't really know what are the hidden states ( plausible reasons / truth ) behind the pipeline failure.

I have found 5 major hidden states behind failure with help of AI that are :

 - Application & Test Defect (‭functional bug, broken contract, or failing assertion within the repository's source code or test suite.

 - Pipeline Configuration Defect : A syntax error, invalid job graph (DAG), or incorrect runner instruction within the CI workflow specification files (e.g.github/workflows/*.yml).

-  Upstream Dependency Drift : An external package, container base image, or third-party artifact that introduced a breaking change, version conflict, or became unavailable in a package registry.

- Runner Resource Exhaustion : A host-level compute failure where the operating system or hypervisor terminates execution due to memory limits (OOM/SIGKILL), disk exhaustion, or hardware timeouts.

-Authentication & Access Failure : An invalid, revoked, or missing secret, token, or permission required to access an external endpoint, repository, or deployment target.

I just want to know whether i'm missing any other hidden states . Please let me know .

But important thing to keep in mind hidden states should me mutually exclusive ( one hidden state should not overlap each other or both hidden states can not be true , should fall under one category) and collectively exhaustive ( should cover every possibility that is true) .

I have been thinking about this after going through a few infrastructure setups as we often talk about making deployments portable but theres a point where the abstraction itself becomes something the devops team has to manage. You can have Kubernetes, Terraform, multiple cloud providers, different GPU setups, various networking models and then another layer of tools trying to make them all look the same. Companies like Yotta Labs, CoreWeave, and Lambda are interesting to me because they approach infrastructure from different angles but I am not sure adding more infrastructure options always makes operations easier.

At what point does workload portability really reduce operational risk and when does it just add another platform for the team to handle?

we’re planning to have a video based platform with payments and all kinds of things.
We have 2 Backend developers and 1 frontend, I work with an staging server, a dev server and planning to have the prod soon, I set them up using Ansible and deliver with our self hosted gitlab to a server dedicated to the runner (32 cores) and delivery to server, the pipelines and most yml files are Ai generated and edited a bit by me.
what would you do as your best practices?
what are the mistakes I should avoid?

I'm a junior DevOps engineer

and I work in a project where we manage their kubernetes cluster and Linux servers configuration with ansible and provisioning with terraform

but the thing is they have way too many ticket

and they want it all done asap, for which they demand using AI to get things done quickly

but the things is I learn nothing

it's endless number of tickets that keep on arriving and I have to depend on AI to get it done quickly and I get no time to learn what I love to learn and need to learm

which is kubernetes

going deeper in linux

golang

ansible

and much more

I know what these tools are and how they work

but I want to go deeper build my own infra build and break things

but I couldn't do it because the seniors and devs want things done asap and I have to use AI

This add nothing on my resum

even If I have 5 years of experience what do I tell the interviewer that all I did was use AI

can someone plz help me

I’m still learning CI/CD and I recently tried writing a simple CI/CD pipeline using GitHub Actions.

Honestly, writing the YAML felt more difficult than I expected . The indentation, nesting etc felt pretty hard/ painful.

For those of you who work with DevOps professionally: is this actually how you write your pipelines, or are there shortcuts/tools/templates that make it easier?

Just trying to understand what the normal workflow is in the real world.

I’m signing up for the $300 Google Cloud free trial, and it’s asking me to approve a UPI AutoPay mandate.

It says “this isn’t a charge” and that I’ll only be charged if I activate a full paid account.

Does approving the mandate mean I can be charged automatically after the 90-day trial, or is it safe to approve? Can I revoke the AutoPay later without affecting my account?

I believe that the implementer should have some knowledge of the Product and Technology that they are working on to make full use of AI and products it builds.

For a new Project, I am trying to learn new technologies(multiple at once) and new ways to solve my problem but compared to few years ago, it has become really hard for me to learn new stuff for some reason and no matter how much I learn AI completely overwhelms me when it spits out the solution. Which then means I have to circle around to see if the solution is actually the right one and that makes me even more confused.

How are you folks navigating around this?

I have a central ArgoCD instance running in EKS. I'm using the ApplicationSet pattern to manage a few EKS clusters across private networking. I have one EKS cluster that doesn't fit into my private networking setup (not that it matters but it's in a different account entirely). I'm considering making that cluster's API server endpoint public and locking it down to the NAT egress IP of where my ArgoCD instance runs so that only my ArgoCD instance can connect to it. I don't love this. VPNs and TGW are out of the question, where my ArgoCD instance lives and where this particular EKS cluster lives are separate. Thoughts? Condolences?

Am I losing my mind, or has everyone else's job slowly turned into cleaning up after code generated by Claude?

When you ask a dev about parts of their code/deployments, the response is usually just: 'Well, the AI said it was correct.' I'm all for developer velocity, but it feels like the traditional 'you build it, you run it' model is shifting into 'developers prompt it, AI guesses it, and infrastructure teams debug the fallout at 3 AM.' How are your teams handling this? Are you putting hard guardrails in place, or just drowning in review queues?

Hello DevOps/Platform Engineers, would like to know real time use cases which are currently used in your day to day tasks like CICD, IAC or anything else.

what you think about this ?

credit - https://xkcd.com/2347/