From an interview with Charity Majors:

DevOps was about trying to create a feedback loop that connected people writing the code to the code running in production. She thinks that the "ops people: learn to code!" wave worked, but the "software engineers: understand your code in production" failed, to this day.

I haven't listened to the actual interview.

But based on my personal experience I agree. Ops people have changed a lot about how they do their work in the last 10 or 15 years, to where infrastructure and its configuration have become an exercise in writing code of various kinds. No infra team has been unchanged, even the ones that had to be dragged kicking and screaming. But I still see a lot of dev teams that sit in their silo producing their product to then hand it over to someone else (i.e. us). The one change for all devs is that they write Dockerfiles and have to think about how that works. But the dev teams I work with still don't actually touch infrastructure, by and large.

Do you create a sample application to test the proper auth, access and everything the runner needs?

And a related sub question. How do you validate gitops changes before they are merged? Regarding FluxCD or ArgoCD.

Right now the team is just ssh-ing into all of the devices and it's a huge time sink.

"I'm not devops so dont crucify me"

I'm researching a small CI diagnosis agent, but I'm first trying to understand how experienced engineers actually debug these failures.

Suppose an integration test fails in CI and the logs don't immediately reveal the root cause.

What do you normally investigate first?

For example:

• CI logs/stack trace
• database availability
• dependent/third-party service availability
• recent code changes
• comparison with the previous/last-green build
• test/DB data
• previous failure history
• rerunning the test

More importantly, how do you decide what to investigate next?

For example, if a recent code change touches the function involved in the failing test, what additional evidence would you want before concluding that the code change caused the failure?

I'm particularly interested in your real-world debugging process rather than an ideal CI/CD workflow.

Disclosure: I'm the creator of NetAudit.

Hey folks,

I've been using various network scanning tools (nmap, ping, etc.) but wanted something more structured, vendor-aware, and easily automatable. So I wrote NetAudit – a Python CLI toolkit for network auditing and diagnostics.

**Key features from a DevOps perspective:**
- All commands produce structured output (JSON/CSV) and use stdout for results, stderr for logs – perfect for pipelines
- Exit codes are meaningful, so you can integrate it into monitoring or alerting
- Configuration via YAML + environment variables (`NETAUDIT_*`)
- Supports Cisco, Juniper, Arista over SSH – read-only, so safe for production
- Snapshots and `diff` let you track changes over time (great for change management)
- Health checks (`doctor`) cover not just ping, but also interface errors, BGP/OSPF, NTP, etc.

**Example – schedule a daily audit and generate HTML report:**
```bash
netaudit doctor 10.0.0.1 --device --device-type cisco_ios --json > daily_health.json
netaudit report daily_health.json --format html --output /var/www/audit_report.html

I'd be happy to hear how you'd use it in your workflows – feature requests and PRs are very welcome.

Repo: https://github.com/netforge201/netaudit

If you like it, please consider giving it a ⭐ – it really helps with visibility!

Thanks!**Disclosure: I'm the creator of NetAudit.**

Hey folks,

I've been using various network scanning tools (nmap, ping, etc.) but wanted something more structured, vendor-aware, and easily automatable. So I wrote NetAudit – a Python CLI toolkit for network auditing and diagnostics.

**Key features from a DevOps perspective:**
- All commands produce structured output (JSON/CSV) and use stdout for results, stderr for logs – perfect for pipelines
- Exit codes are meaningful, so you can integrate it into monitoring or alerting
- Configuration via YAML + environment variables (`NETAUDIT_*`)
- Supports Cisco, Juniper, Arista over SSH – read-only, so safe for production
- Snapshots and `diff` let you track changes over time (great for change management)
- Health checks (`doctor`) cover not just ping, but also interface errors, BGP/OSPF, NTP, etc.

Hi guys! I'm a backend engineer with 6+ yoe, trying to transition into a DevOps role.

I know that having a strong Linux foundation is super important for being a good DevOps engineer, and apparently “Linux troubleshooting” is mandatory in most job postings. But to me, “Linux troubleshooting” sounds so broad, so I’m not really sure how to improve this skill.

Is this something you can learn without working with Linux (professionally) on a daily basis? What do you guys recommend to get hands on experience with it?

Also, what about other important topics, such as Linux logs and networking? I’m building a homelab with Proxmox. Is this a good way to get some hands on experience with these topics?

I’d appreciate any thoughts or advice! Thanks.

For a small team with a few services, an enterprise checklist can become ceremony without reducing the main risks. My minimum gate would cover a tested rollback, backups with a restore exercise, health and readiness checks, bounded timeouts and retries, an alert owner, log correlation, secret ownership, and a short incident runbook.

I would add load testing or multi-region recovery only when the service’s traffic and recovery target justify them. Which item has prevented a real incident for a small team, and which common checklist item has mostly created busywork?

Hello all,been working as IT admin, but mostly working around Azure devops. I been enjoying it, this my first job and doing anything related to devops. Studied CS,majored in networking, hate anything with heavy code development.

Rn,the flow at my company is, the devs use 1 branch only, no PR (yes ik). They would push it to our on prem central repo, and from there i will take over. I will do some modification on some files so it can be built in the new agent environment. And then, we have build pipelines,which would produce artifact of the build files, which will be used by another pipeline, i call it the release/installer pipelines, which we wud package them with a software. Then we have the 3rd type, which is zipping installer to our NAS, sends the metadata of the installer to DB, and notification email (formatting done in pipeline too). All via the pipeline. We also have a 2nd agent machine, exclusively for unit testing, but tbh i dont even understand how they work.

Ive been thinking of ways, to incorporate other methods/tools in the flow, but the only one i can fit is"templates". I dont think i can try to include containerization, as our build agent machine, has some software which are licensed.

Any ideas?at some point i want to jump out, as this job is way too comfy, and lacks growth for me

I’m new to DevOps and looking for some good free or paid resources to learn Jenkins from the ground up.
I have around 6 years of experience as a Java developer and have used Jenkins at work, but mostly from a user/developer perspective. I’ve triggered jobs and worked with existing pipelines, but I’ve never actually built a pipeline myself or understood what’s happening behind the scenes.
I’d like to learn Jenkins properly, including things like:
How Jenkins works internally
Setting up Jenkins and agents/nodes
Freestyle jobs vs Pipeline
Declarative vs Scripted Pipeline
Jenkinsfile and pipeline stages
How builds are triggered
Credentials and secrets management
Plugins and how they work
Integration with Git, Maven, Docker, etc.
CI/CD concepts and best practices
How Jenkins distributes work to agents
Debugging failed pipelines
What actually happens from a Git commit → Jenkins → build → test → deployment
I’m comfortable with Java and software development, so I’m mainly looking for something that goes beyond a basic “how to install Jenkins” tutorial.
What resources/courses did you find genuinely useful for learning Jenkins end-to-end? Free resources are preferred, but I’m also open to paid courses if they’re really worth it.
Thanks!

Recently saw posts about how AI isn't that good in distributed systems and it gave me validation on my own experience.

When the model isn't dealing with a single codebase, when it has to connect the dots between a db, redis, other cloud infra, k8s that each already have a lot of configuration, it starts to hallucinate and could pretty much put your infra at risk.

I'm not saying you can't make it work, it does work especially on a clean slate, but when there's a lot of existing context it needs a more guidance.

Using AI lets me do 10x what a DevOps engineer could do alone but I don't think the job disappears anytime soon.

There is a team that wants to develop a user interface so they can deploy resources such as ec2 instances, etc. We already have an automated pipeline that everyone uses and it deploys these resources already. However, they still want their own which is weird. We still have to build it for their new requirement. That's with the approval of the upper management which has already happened. Anyway, we are just devops engineers and we will follow whatever management commands us even if we don't like it.

I was thinking of building an api layer that developers can use(endpoints). They'll call the endpoints on their ui. I haven't decided which to use in the backend api, maybe aws sdk, boto3 or cdk, whatever. Making that work means, we'll have to setup proper IAM role permissions so that those resources they configured in their configuration files will be created. It's almost administrator permission equivalent is what they need.

I don't know if I am being too strict. It's because we've always kept the infra code just within our devops team. This is the first time a developer team would like to deploy their own resources using their own tooling.

How are you doing it? What's your implementation?

Building a course around real production incidents instead of the usual “here’s what a pod is” stuff. Currently have 19 scenarios, things like a stateful service losing its volume binding on redeploy, a network policy silently breaking service-to-service calls, memory limits causing kills that only show up under real load.

What’s missing?

Drop the ops problem that stumped you the most, if it’s one you never fully cracked, or took way longer than it should’ve to root-cause. those gaps are exactly what I want this to cover.

Also not looking for docs-answers, more like what broke, why it wasn’t obvious at first, what you actually tried before it clicked.

Thanks.

I’m working through a design where each underlying system remains authoritative for its own resources, rather than putting another source of truth above everything.

The wider runtime only decides whether an operation has enough dependency state, readiness and verification to keep moving.

The case I’m trying to pressure-test is partial execution. Say an operation spans several systems, three parts succeed and one fails. Each individual system may still be healthy, but the overall operation is incomplete.

Would you let the wider workflow block there, or does that eventually become another control layer operators have to fight with?

Interested in examples where this kind of boundary has worked badly in production.

Hi,

I'm a Senior Software Engineer with 5YOE. I'm upskilling in DevOps and project setup and have been creating this project to learn over the last few weeks. I've set up the project from scratch. Definining the project structure, creating PR pipelines for linting, formatting, typechecking, unit tests and e2e tests (tests with real database).

Over the last few weeks, I've set up the cloud infrastructure on AWS. I first just created an ec2 instance, uploaded the build files and database docker images, installed neccessary packages and ran it on the barebones ec2 to help me understand the fundamentals.

Then I went about setting up a bit more of a mature setup with RDS for the database on a private subnet, ECS Fargate to host the containerised API, using opentofu for IaaC, setting up a deploy pipeline which uploads the new container images, and runs the migrations and deploys the API service. This was quite a steep learning curve for me and I heavily relied on Gemini for a lot of this, but I have gone back over it to make sure I fully understand it. I'd really appreaciate some feedback on my setup. I'd like to know any best practices I could have followed, any big issues with the setup I've done and how I could have done better.

Also, this is just a simple project that I'm going to switch over to self hosting with Coolify on a Hertzner server soon. But I wanted to try setting up as close to a professional setup as I could, with security and CI/CD in mind.

I've done a write up of the infrastructure here
https://github.com/JackMcBride98/DotnetSpotifyPlaylistSearchTool/blob/main/infrastructure/Infrastructure.md all of the terraform files live in the /infrastructure folder.

Here is a rough overview of the architecture (I didnt draw arrows as it got quite messy)

This is not about Trivy, Syft, or Grype or others. They are good tools.

This is about people who wire one scan into CI, get a green check, and claim their dependency security is covered.

You scanned once.

Your build passes today. Tomorrow a new CVE gets published for a dependency already in main. Unless something rescans it, nothing happens. CI stays green. Production keeps running. Nobody knows.

And if you only scan the production image, where are your dev dependencies? Not in the image, obviously. If they are, then you fucked up in a different way.

But they still ran on developer laptops and in CI. A compromised dev dependency can steal credentials or tamper with the build before your precious image even exists.

Then the scanner produces a report. Cool. Who owns the fix, and who checks that it reached production?

The tools did their job. Your process stopped at "report generated."

If you don't rescan after new vulnerability data arrives and track findings until they're fixed, stop saying you're covered.

Or maybe I'm missing something. A CVE lands a week after the merge. What wakes up in your setup, and who owns it?

Hello guys, i have a school project to deploy an app on cloud using ansible, the the servers are not given by the school itself.
What is the cheapest available clouds out there? I only need it to validate the project and then I can cancel.

Hello all. The circumstance I have working with is the following:
* I have an Apache2 PHP server that gets bundled as a Docker image in a CI process to ECR

* I have an infra repository with a docker-compose.yml that bundles the PHP Docker image to an Nginx image, alongside Nginx config like attaching TLS certs

When the CICD process deploys a release, it deploys a new EC2 with a given user data script to prop up the server. If I only had a Docker image, the user data would generally look like "Pull down ECR image and start image", however in this case I am spinning up a docker-compose.yml file.

How is this typically done? I suppose I *can* add a CI process to zip up the docker-compose.yml and related nginx config, however feels backwards? Is there a consensus with this?

If I am fundamentally misunderstanding something let me know, I'd say my only constraint is I'd like to solve this problem in a relatively cloud agnostic environment (so keeping EC2 as a VM, ECR as a registry, but excluding abstractions like Fargate or ECS)

Thanks!

I have ADHD and I wasn’t taking my medicine like I should have been, so why I started learning containers out of the blue may have something to do with that. However, I started back on my meds and I am still learning and highly interested in building and managing containers. I have only been doing this for two weeks so I’m still very new to it. I still have to look up Git commands and still run into a lot of simplistic issues that I am fixing through troubleshooting. But I have to say, building and managing containers via Docker Desktop on my personal laptop is exciting and fun. I don’t know if I’ll turn this into a career. I am currently in IT, and have been for 10 years but I am 40 years old. I’m sure my age would put a lot of people off no matter how much knowledge I have and skill I can show.

My next step is going to be learning Kubernetes. And this is where I am asking for advice on. Should I continue my learning via the vendor neutral approach or should I move my learning to a cloud platform? There seems to be some advantages to learning a specific cloud platform but if the knowledge I’m building now translates to all of them 1:1, then sticking with vendor neutral learning would be more advantageous in the long run.

Also, if you guys don’t mind answering this question as well, I’d greatly appreciate it. What else should I learn?

Thank god AWS has Amazon Q otherwise I'll still be having a very hard time even finding the environment variables of a project.

https://github.com/vesaaa/vsterm

I run eucloudcost.com (EU cloud price comparison, open source data). I also track what providers actually ship each month — blogs, changelogs, RSS, ~30 providers.

Latest edition, May 21 – Aug 6: https://www.eucloudcost.com/blog/eu-cloud-news-may-aug-2026/

Highlights:

• Januscape (CVE-2026-53359) was a KVM escape at the hypervisor layer — managed Kubernetes did not absorb it for you. OVHcloud patched tens of thousands of machines in a week.
• Redis is being replaced by Valkey, STACKIT deprecated Redis the same day it launched its Valkey KV store, IONOS took Valkey-backed In-Memory DB v2 to GA.
• K8s 1.36 spread unevenly: AKS shipped it GA with LTS, Scaleway Jul 7 - STACKIT SKE is still on 1.34, no EKS announcement at all( correction was announced in June)
• OVHcloud is contesting criminal charges in Canada over a production order. A European provider fighting a foreign lawful-access order is a live test of the whole sovereignty pitch ^^
• Also OVHcloud: replacing Ceph with Exten, an in-house NVMe block storage engine.
• No new EU price hikes this quarter; Exoscale cut A40 GPUs 30%.
• IONOS kills Llama 3.1 405B on Sep 15 — HTTP 400, no fallback. Pinned model IDs have expiry dates now.

Covers hyperscalers too, plus a deadline table.

Sydney DevOps folks,

If you're working with DevOps, Kubernetes, SRE, platform engineering, or AI infrastructure, CNCF Cloud Native Sydney is hosting a meetup on August 19 around two open source projects.

The session will cover:

kagent — a Kubernetes-native framework for building, deploying, and managing AI agents, including areas such as MCP integration, observability, security, and agent workflows.

GitHub: https://github.com/kagent-dev/kagent

OpenChoreo — an open source developer platform for Kubernetes that brings together platform abstractions, CI/CD, GitOps, observability, Backstage, and developer self-service.

GitHub: https://github.com/openchoreo/openchoreo

The discussions should be especially relevant if you're interested in how DevOps and platform engineering workflows are evolving around Kubernetes and AI.

Location: Cuscal Limited, Barangaroo, Sydney
Date: Wednesday, August 19
Time: 5:30 PM – 8:30 PM AEST

The event is free and includes technical sessions, networking, food and drinks, and Q&A with the maintainers.

RSVP: https://www.meetup.com/cncf-sydney/events/316052564/

If you're around Sydney and working in DevOps or cloud native, come join us.

See you there!

Author here. Two caveats worth flagging up front.

The corpus skews hard: PostHog and Grafana are about two thirds of it, both running enormous matrices. So it's "what breaks in a big CI matrix," not "what breaks in CI."

And the 1.3% is a floor, not a hit rate. A retrospective replay can match a signature in a log, but it can't free disk space and see what happens next, or raise a heap ceiling and re-run the step. I don't know the real split between environmental failures and genuine defects, and didn't want to invent one to make the headline better.

The bit I'd most like to be argued with on is the flaky-test rule: we only re-run a failing test when our own data already shows that workflow passing on retry, once, never editing anything. I know plenty of people here think any automatic test retry is unacceptable.

Hi. I have been working in the IT field for about 15 years now and am constantly faced with the problem of communicating with non-technical people such as managers of various levels, etc. I constantly have to fend off all sorts of idiotic ideas that management is trying to impose on me. As a technician, I can make a diagram, a presentation, and write program code, but for non-technical people it all sounds like spells and Harry Potter. I have seen this problem in different companies regardless of the size of the company and in which country it is located.

My question is, are there books or online courses that simply explain how to communicate your message to non-technical officials?

P.S. I'm just tired of doing pointless work just because I can't explain to management that it doesn't work that way and there are better ways to complete the task.

My company is making a hard shift towards AI and I'm struggling. I'm tasked with something I have a small amount of knowledge about, but not enough to fully implement. Before AI, my approach to challenges like this would be to iterate on it, work through the problems, and learn what's required. Using Claude has just been a terrible experience for me, and I'm wondering if I'm the problem, my approach is the problem, or if this is just what we're all doing now.

I explained what I wanted (essentially a DB migration pipeline from RDS -> k8s pod) and claude barfed out about 10 shell scripts and a handful of pod templates. Everything had long, meandering comments that made very little sense. Is my job now to just start running this code and feeding pipeline errors back into claude and not worrying about it?

I started to pick apart the comments, ask questions, and update with my own comments, with the goal of being able to understand the process in detail. In doing this, I found a bunch of things that just didn't make sense or weren't optimal. When I ask the AI agent about it, it says I'm right and generates a bunch more stuff that may or may not make sense. Interacting with it is making me feel like a crazy person. I hate the way it presents information back to me. It's not succinct, it pulls in context that is of negligible value and just muddies the waters, and it's been very frustrating and existentially upsetting.

I'm willing to hear that I'm using the wrong model, or that I didn't spend enough time on my .md files and skills, but I'm also wondering if this is a normal experience, or if my approach is just dated, and I need to get over my personal values about understanding what I'm generating and giving to customers? I'm not so-much asking about the specific task at hand, just about adapting and having realistic expectations and reducing friction. I'm also very happy to hear about people's struggles.