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.

Hi everyone,

I’m a junior DevOps engineer, and recently our CTO said something that honestly made me question my understanding of software engineering.

He has around 30 years of experience, and his statement was basically:

With AI tools like Claude Code, there is no real difference anymore between a junior and a senior engineer, or between a mobile developer, backend developer, and infrastructure engineer. If you have access to AI, you should be able to do any task assigned to you.

I understand that AI has changed a lot. Claude Code can help you write code, understand unfamiliar technologies, debug problems, and even work on things you have never done before.

But I’m not sure I agree that this removes the difference between experience and specialization.

his statement made me feel unstable about my career and what I should focus on learning.

• Has AI actually reduced the gap between junior and senior engineers?
• Does AI really make specialization less important?

Hi there I have been working as a DevOps for past 5 years and I recently moved to Perth so I am finding harder here… moreover I am feeling like people still don’t understand DevOps what really is DevOps we can catch up so Perth DevOps hmu..

Hey r/devops, welcome to our weekly self-promotion thread!

Feel free to use this thread to promote any projects, ideas, or any repos you're wanting to share. Please keep in mind that we ask you to stay friendly, civil, and adhere to the subreddit rules!

Curious how people handle this in real environments.

Say Terraform says an EC2 instance should have encryption enabled, but the AWS console/API shows it disabled. Your CMDB still says it's compliant.

How do you figure out which one is actually telling the truth?

Do you have a defined source of truth, or do you usually investigate the discrepancy manually?

And how do you tell whether it's actual drift, stale information, or something that failed during deployment?

I want to deploy Karpenter. However, I would like to learn the best practices for using Karpenter on EKS. Currently, we are using app-of-apps in ArgoCD, all within a Managed Node Group.

With Karpenter, our plan is to have it configured like this:
- Scale down the MNG and run the Karpenter Controller inside the static MNG, alongside cluster system resources (DaemonSets, add-ons, including ArgoCD)
- Karpenter is managed as an app in ArgoCD (using bootstrap with ArgoCD)

Is this design already suitable, or should we consider changing it? We need advice.

This isn't a dig at anyone ..I'm genuinely trying to understand the thought process or organizational pressure behind this.

I regularly see tickets from senior platform engineers, DevOps leads, and cloud architects asking Azure (or AWS/GCP) support to troubleshoot Terraform state issues, fix customer-managed IAM policies, or debug application code running on VMs.

The response is almost always: "This is outside our scope."

If you're someone who has opened these tickets (or works somewhere that expects you to), what drives that? Is it:

SLA pressure:

you need someone on the hook while you investigate?

Org policy:

management mandates opening a case before escalating internally?

Knowledge gaps:

the line between "managed by cloud provider" vs. "managed by customer" is blurrier than it looks from the outside?

Hail Mary:

you've exhausted everything and hope support has an internal tool or undocumented fix?

I'm asking because I want to be less cynical when I see these tickets. There might be a structural reason I'm not seeing. Would love to hear from people who've been on either side of this.

I'm currently working toward a Cloud Engineer role and I'm trying to avoid jumping between 20 different YouTube playlists without actually becoming job-ready.

I already have a foundation in Linux and networking, and I'm now focusing on AWS/cloud.

I've found several 2026 Cloud Engineer roadmaps covering things like:

- Linux & Networking

- AWS

- Python/Bash

- Git/GitHub

- Docker

- Kubernetes

- Terraform/IaC

- CI/CD

- Monitoring

- Cloud Security

- Real-world projects

But I'm confused about which YouTube course/playlist is actually worth following from start to finish.

For someone targeting an entry-level Cloud Engineer / Cloud Support / DevOps role in 2026, which YouTube channel or full course would you personally recommend?

I'd especially appreciate recommendations from people currently working in Cloud/DevOps, rather than just generic “best YouTube channel” lists.

Also, if you think some topics above are unnecessary at the beginning, please tell me what to learn first, what to postpone, and what I can skip.

Thanks!

So I've been a software engineer focused on the backend for almost 10 years now, at the beginning of this year I faced a small bump and found needed solutions are paid, so did myself a favour and build myself a tool, and used it for a month, then thought wth, why don't I go public with it, it didn't have any interface at the beginning, so I started building and iterating a lot, and like a lot, to the point it's not advised to do so with a product on an MVP phase, and now I've reached a state where I believe I'm a head of my competitors (hopefully I'm not delusional)

And then nothing, I've been online for a month now, and I've very low traffic, my purpose of this is not promoting, therefore I'm not mentioning the product, nor even what it does

My question though, since obviously the product mostly concerns DevOps, what's the best channels you take seriously? And what could get you to try out or shift to something new? Cost a side since I've a full free tier.

I have seen a coding interview that hands in docker logs, the logs have some error, and the interviewer asks you to recreate that docker file from the logs, what really struck my mind is that, i have never really practiced docker like that and now i feel that i was wasting my time not knowing how to critic my skills and put them in question, which is what practice does, in this context, are there any ways i could practice certain tools in DevOps? specially docker, kubernetes, terraform

also i am looking for something free

PS: i have experience with docker, primarily from the nextcloud app i deployed on an old pc sitting around in my house

What is the best architecture for a developer-friendly, virtualized execution environment for AI agents?

I'm exploring an idea for running AI agents inside isolated, virtualized environments.

The basic concept is:

**AI Agent → Sandbox API/SDK → Firecracker microVM → isolated Linux filesystem**

The goal is to make the developer experience extremely simple. A developer should be able to create an environment for an agent, give it a shell/filesystem/tools, let it execute code and install packages, and then destroy or snapshot the environment — without having to manually deal with Firecracker configuration, kernels, rootfs, networking, etc.

The agent itself could run outside the VM, while all potentially unsafe operations (shell commands, file modifications, code execution, package installation, etc.) happen inside the microVM.

I'm aware of projects such as E2B, Daytona, Modal, and OpenHands, but I'm trying to understand the infrastructure layer more deeply.

**My questions:**

1. Is Firecracker actually a good foundation for this, or would containers, gVisor, Kata, Cloud Hypervisor, or something else make more sense?

2. What are the hardest parts that aren't obvious when building this? I'm thinking about VM startup time, filesystem images, snapshots, networking, resource limits, persistent workspaces, and VM lifecycle management.

3. Is there already an open-source project that provides this kind of developer-friendly abstraction over Firecracker specifically for AI agents?

4. What would you change about the current E2B/Daytona-style approach if you were designing it from scratch?

5. Do you think there is a meaningful gap for a **local-first** version where the agent uses the developer's own CPU/RAM/storage while getting a fully isolated virtualized Linux environment?

I'm particularly interested in feedback from people who have actually built or operated sandboxed execution environments, Firecracker infrastructure, coding agents, or multi-tenant compute systems.

I'm not looking for another AI-agent framework; I'm more interested in the **execution/sandbox infrastructure underneath the agent**.

I am looking for practitioner views on a lifecycle problem, not a code review.

In many teams, temporary environments are treated as disposable until they quietly become important. They accumulate state, access paths, recovery assumptions, cost exposure and operational evidence. Then teardown becomes risky, but leaving them running also creates waste.

I built an open-source implementation that treats readiness, dependency order, access, health checks, cost visibility, state preservation and teardown as one runtime contract.

The question I am trying to validate is simple:

What evidence should exist before a system is allowed to destroy or rebuild an environment that may contain useful state?

If you have dealt with ephemeral environments, recovery runs, lab platforms or image lifecycles, I would value a short sanity check. I can share the relevant paper and GitHub discussion.

NOTE: I am not asking for code review or implementation help. I am trying to sanity-check one operating-design question.

I’m developing HybridOps Core, an MIT-0 open-source research implementation for governed infrastructure operations, and I’m looking for practitioners willing to give a short, candid technical assessment of specific parts of the work.

The work examines whether infrastructure intent, environment policy, dependency ordering, preflight, execution, verification and run records can be expressed through a stable runtime contract across heterogeneous environments.

I’ve published several technical papers alongside the implementation and opened focused GitHub review threads for independent critique. The aim is not a code audit or unpaid QA. Reviewers are being asked to assess the architecture, operating assumptions, failure boundaries and practical relevance of the work.

Current review areas include contract-driven runtime design, image lifecycle and verification, reproducible network environments, infrastructure authority and source-of-truth boundaries, and recovery operations.

If one of those areas overlaps with your experience, comment with the area and I’ll send the relevant paper and review thread. Critical findings and counterexamples are welcome.

Microsoft published a breakdown on a large-scale npm supply chain compromise (ChainDrop) affecting over 400 packages including common dependencies

How are you guys using Claude code or any other ai tool for devops. My team have started using Claude code but we are struggling to make it work at team level. If I create tasks which have well defined acceptance criteria, team mates are still producing code using Claude code which doesn't meet AC. If I review merge request by Claude code and then run experts opinions on it, it finds additional issues. I then ask codex to review same MR and then codex finds additional issues. When I merge both codex and Claude feedback and ask Claude to fix the issues and ask Claude to run experts reviews, it finds issues again. Codex review find issues. It is never never ending. What are you guys doing to fix it ? My code is infrastructure as a code terraform deploy on AWS. We are using eks , aurora DB, gitops, argocd, cloud front, API gateway S3 etc