Be honest. I am going through a Python course and can tell I am not going to use most of it on the actual job. Give me boto3, a for loop, a while loop for pagination, and functions and I am good to go. This changes if using a tool like Pulumi, but there are so few jobs requiring it that I feel it is a non-starter for the YAML vs coding argument.

So I've been working on this microservices project ( repo ) for a few months. I have it fully running locally using kind, now I want to learn how to deploy it on AWS or GCP following best practices to get hands on experience and record demo videos for my resume.

Here's the stack:

• Microservices: 5 Node.js services (built and pushed to GHCR )
• Stateful Services: 5 Postgres databases (one per service), Redis, and Kafka.
• Observability: Prometheus, Grafana, Loki, and ( Alloy deployed using Helm charts).
• K8s & Networking: I wrote custom manifests using StatefulSets and Headless Services for the stateful components. I am using the Gateway API for ingress, backed by cloud-provider-kind locally.

Being a college student my budget is pretty tight. My plan is to write Terraform scripts so I can spin the whole infrastructure up, record a few demos for my resume, and immediately tear it down to keep costs minimal.

The questions I have:

1. Which cloud provider to choose and how to deploy? Should I use managed K8s like EKS, or just run Kubernetes (or lightweight K8s like k3s) myself on a single EC2/VM instance? What's actually standard practice that I'd use on a real job?
2. How to handle routing in the cloud cheap? Locally I use Gateway API with cloud-provider-kind. What’s the most cost-effective way to handle this on AWS/GCP without getting hit with expensive cloud load balancer fees?
3. Is running DBs/Kafka in the cluster okay? Is keeping Postgres, Kafka, and monitoring inside K8s fine for a portfolio project, or do recruiters/hiring managers specifically look for managed cloud services ?

Any direction, best practices, or potential pitfalls to avoid would be greatly appreciated!

I've been looking at workflow orchestration tools for systems where the workflow itself is becoming a real part of the backend, rather than just scheduling a few jobs.

The distinction I'm trying to understand is where people draw the line between something like Airflow/Prefect, Temporal, Step Functions, Conductor, etc. and a more general workflow orchestration platform.

For me, the requirements are roughly: multi-step workflows across APIs and services retries/timeouts without rebuilding the logic around every API call long-running workflows and scheduled execution branching, parallel execution and dependencies visibility into individual workflow runs and failed steps human approval/manual intervention when required ideally support for AI/LLM steps without making the whole workflow agentic

The last point is becoming more relevant too. A lot of the newer ""AI workflow"" tooling seems to assume that the model should make most of the decisions. I'm more interested in workflows where deterministic code handles deterministic things and AI is just one step when it actually adds value.

For teams running this in production, what are you using today? And more importantly, what made you choose it over the other workflow orchestration tools you evaluated?

Hello, I’m having an issue with GitHub Actions in one of my private repositories. I have a workflow configured to run daily, but it is not being triggered.

I tested the same thing in another repository by configuring a workflow to run every 5 minutes, but same thing happens there as well.

has anyone experienced this recently? Could this be an issue on GitHub’s side?

Workflow: https://github.com/ahmedhesham301/autoscaling-hetzner/blob/main/.github/workflows/snyk-security.yml

I've been digging into load testing tools recently (k6, JMeter, Locust, Speedscale, Azure Load Testing) and noticed something consistent, recording a session and turning it into a script is the easy part. The painful part seems to be:

• Correlation (session tokens, CSRF, IDs from one response feeding the next request)
• Scripts breaking every time the UI or API changes
• Flows that aren't linear. e.g a list page where the "next" request depends on which item a simulated user picks

Curious how people here actually deal with this day to day:

• Do you write correlation logic by hand every time, or has anything automated it well for you?
• How do you handle flows where the test needs to pick from a dynamic list (search results, a user's own records, etc.) instead of hitting a fixed ID?
• How often do your load test scripts break when the app changes, and how much time does fixing them eat up?

Do you guys have a solution for this, or know of a tool that actually handles it well?

Hi,

Idk but I was always a terminal person and my main editor was and is nvim. Lately I got into homelab stuff and I have been loving the experience of having full control of stuff. I know docker, bash, linux as I use it on gaming pc. I have theoratical knowledge about k8bs but never used it in production. I know aws but I only used like common stuff in production(ec2, lambdas, s3, rds). I dont know terraforms and other devops infra stuff. Can you share your experience in transitionaing and what should I focus on to work as a devops or platform eng

Hey everyone,

I keep seeing tons of posts endorsing all these new AI skills, extensions, and plugins like they’re absolutely mandatory if you want to keep up. It definitely gives me a bit of FOMO, but whenever I look into them, I just don't get the hype.

As a DevOps engineer, it feels like 99% of these extra tools don't really matter for my actual day-to-day workflow. Honestly, the only thing I’ve found genuinely useful is Superpowers by Obra. It does exactly what I need, and adding anything else just feels like extra bloat or a fancy wrapper for stuff I can already do.

Am I missing out on something awesome by ignoring the rest of the plugin ecosystem, or is everyone else just caught up in the hype cycle? Would love to hear how other DevOps folks are handling the AI tool explosion.

The API is written in PHP and hosted on DigitalOcean.

This is a manual setup, but ready to scale...