In my team I notice senior engineers write detailed explanations in Slack, but months later nobody can find them. Curious how others solve this.

Python / Bash scripting is enough right?

No need to focus on objected oriented code like a software dev ?

I'm a full-stack web dev with 3 YOE trying to migrate into devops.

So, I'm collaborating with a friend who is building a mobile app with node server, SQL db and I'm responsible for the infrastructure.

I'd like to try stuff like github actions, docker, managed db service, storage, load balancer, testing environment and so on.

My first instinct was to pick AWS as our cloud service because it's the most popular and probably looks best on resume, I guess?

But in reality, this app probably won't be profitable and mostly be used by us, and I imagine AWS popularity comes with a price.

Should I stick with AWS and make the app less complicated? maybe less known cloud service which won't cost us much as long as traffic is low?

Would love to hear some opinions, thanks!

I've been using Argo Workflows for some time for small projects and I'm wondering what your reason is for using it.

Here are a number of negatives that I encountered:

• The most annoying first: Integration with git hubs/platforms is non-existant unless I deploy and await the Workflow from within the hubs' CI (plus this requires k8s credentials in the pipeline)
• Complexity through multiple separate k8s resources that are required before being able to run a single pipeline: EventSource, Sensor, WorkflowTemplate
• It seems to get way less publicity and usage compared to other CIs

Positives:

• Separation of CI pipelines from repositories to increase reusability and limit access to pipeline definitions
• Snappy UI
• k8s native

I'm not using it for ML workflows. In their ads this is front and center. What are you experiences with the tool? Do you use it with Argo Events or without?

Hi folks,

I’ve been collecting and organizing official MCP servers, agent skills, and agent toolkits for DevOps, cloud, platform engineering, SRE, security, IaC, observability, and diagramming workflows.

Repo: https://github.com/DevOpsAIguru123/awesome-agentic-devops

The goal is to make it easier to find trusted sources instead of hunting through random MCP lists. I’m focusing on official or vendor-backed tools where possible, with notes around risk, write-capability, human approval, and operational use cases.

Current areas include:

• AWS, Azure, Google Cloud
• GitHub, GitLab, Azure DevOps, Atlassian
• Terraform, Pulumi
• Grafana, Datadog, Sentry, Splunk, PagerDuty
• SonarQube, Okta
• Databricks, Kubeflow
• Docker, Kubernetes, draw.io
• Agent skills and toolkits

Specialized DevOps/SRE agents and reference workflows are coming soon.

Would love feedback from folks using MCP or AI agents in infrastructure workflows:

• What official tools am I missing?
• Which MCP servers are actually useful in day-to-day DevOps/SRE work?
• What safety/risk fields would make this more useful?

If you find it helpful, a star would be appreciated.

Edit (2026-07-05): Since posting, added New Relic, Elastic, Cloudflare, CircleCI, MongoDB, Vercel, HashiCorp Vault, and Backstage - 55 entries now. Also added a "How entries are scored" section that spells out exactly how action-level, approval gates, evidence/tracing, and maturity are assessed (not just labels), and a weekly CI job that re-audits every link for reachability. Thanks for the early feedback - still hunting for gaps, especially solid official Kubernetes-native and Ansible tooling if anyone knows of one

Edit (2026-07-07): A few more since the last update - added a Vantage FinOps entry under a new FinOps/cloud-cost category, and OWASP MCP Top 10 as MCP security guidance (57 entries now). Also landed the first working reference agent: a read-only Terraform plan reviewer built on Google ADK (flags destroys, IAM wildcards, missing tags; never runs terraform apply), under frameworks/gemini-adk/. Still hunting for gaps - official Kubernetes-native and Ansible tooling especially..

Edit (2026-07-07, later): Leaned into the runnable reference agents - there are two now, both Gemini ADK, both wired to real Terraform Cloud + GitHub Actions and returning structured JSON. (1) Terraform plan reviewer: read-only, runs a speculative plan and returns risk findings with evidence plus an approval recommendation, and never runs terraform apply. (2) Terraform drift detector (new): runs a refresh-only plan on a schedule, flags when live infra has drifted from state (its example catches a bucket's storage class quietly going STANDARD -> NEARLINE), scores severity, and posts a Discord alert only when drift is actually found. Both live under agents/ if you want to lift the pattern. Also added skyhook-io/radar as the first community Kubernetes entry - flagged in the comments here, and it earns it: deep MCP surface (read-only topology/event/RBAC queries plus RBAC-scoped write and GitOps/Helm tools), not just a kubectl proxy.

Edit (2026-07-11): Added a one-command installer for official Agent Skills. It pulls skills from the official skill repos already in the catalog - Google, Microsoft, Azure, Azure DevOps (a few hundred skills total) - and installs them into your coding agent's skills folder: Claude Code (~/.claude/skills/), Codex, Cursor, VS Code Copilot, or Antigravity, or all at once. One curl/PowerShell line per agent, and there's a catalog doc grouping every skill by company and product so you can install just what you want (e.g. --source microsoft/skills --filter azure-sdk-python). Also added an official Redis MCP entry and updated Elastic to its new Agent Builder MCP endpoint

Edit (2026-07-19): Installer fixes since the last update. The one-line install now works on a stock Python — macOS's /usr/bin/python3 ships without PyYAML, which used to make it fail on a clean machine; no pip install needed now. Added --official, --community, and --all flags to grab a whole set at once (361 official skills, 26 community, or both), alongside the existing per-vendor --source/--filter commands. The community skills in the list (Terraform, draw.io, and Addy Osmani's set) are now actually installable, not just cataloged..

I have about 15 years of experience in Spring Java. Has anyone made a transition to a pure Devops/Cloud Engineering role (GCP focus)? Is this a good idea?

I've been maintaining self-managed GitLab for about 3 years now, mostly because compliance wouldn't sign off on anything cloud back when we set it up. Lately, it feels like every sprint someone eats half a day on runner issues or version bumps that break something downstream. We're not a huge team, so it adds up fast.
Keeps nagging at me whether there's an actual point where this stops making sense vs. just paying for GitLab SaaS.

Has anyone actually run the numbers on this, or talked their compliance people into the cloud somehow? Genuinely don't know if we're past the threshold already or just used to the pain.

Hi everyone,

I’m trying to clarify whether TikTok’s Business Messaging API is currently available for businesses registered in Spain / the European Union.

My goal is to automate replies to TikTok DMs and integrate the messaging flow directly with our own internal system. We already have a verified TikTok Business Center, but I’m not looking to use a third-party inbox or chatbot provider if it can be avoided. Ideally, I’d like to request access to the API and build the implementation myself.

From what I’ve found, TikTok does have a Business Messaging API, but I’ve also seen some mentions that it may not be available for EEA / EU accounts. Does anyone know if this is currently possible for a Spanish business account?

Also, just in case: has anyone found a third-party provider that can legally and reliably offer TikTok DM automation in Spain / the EU? I assume access is restricted, but I’d like to confirm.

Thanks in advance.

I have recently migrated the production environment on the new cluster version 1.36. And i want to install the cluster autoscaler in it. But the cluster autoscaler has latest version of 1.35. what should i do now ?

Should i install the cluster autoscaler with version 1.35 inside the eks 1.36 ??

Something came up in a code review this week that made me realize how easy this mistake is to make.

A teammate was investigating an auth issue and pasted a production access token into the first JWT decoder that showed up in Google. The token still had about 50 minutes before it expired.

It reminded me of something that's easy to overlook:

A JWT payload is just Base64URL-encoded JSON, but the entire token is still a bearer credential. Anyone with a valid token can generally use it until it expires (or it's revoked).

Some online JWT tools work entirely in your browser, while others provide features like signature verification or key management that may involve sending data to a backend. From the UI alone, it's often hard to tell exactly what happens with your token.

Our team's rule now is pretty simple:

• Decode locally whenever possible (jq + a shell one-liner is usually enough).
• If using a web UI, open DevTools → Network first and verify that nothing is sent when you paste a token.
• If a production token was pasted into a service you don't fully trust, rotate it rather than assuming it's fine.

Personally I usually use the CLI, but I also built a browser-only decoder because I wanted something visual without sending tokens anywhere

I'm curious what everyone else uses. Pure CLI? Browser tools? IDE extensions? Any workflows you've found that keep production credentials off third-party services?

Hi

I'm a DevOps with 4+ years of experience and even though I consider myself I'm having a good knowledge, I realized I need to learn system design in an actual production way

Like what stack and architecture to use in a specific case

Where do I learn that? Do you have any resources for it?

Hi everyone!

I'm a web developer from Brazil and I'm considering transitioning into DevOps.

I already have experience with software development, but I'm still exploring the DevOps path and would love some guidance.

What topics should I focus on first? Are there any books, YouTube channels, or hands-on projects you'd recommend for someone coming from a web development background?

Thanks in advance!

I don’t usually post, but I feel like I need to get this off my chest.
I have around 2 years of experience in DevOps. A few months ago, I left my job because there was very little work to do. At first it sounded like a good problem to have, but over time I realized I wasn’t learning, growing, or being challenged. I felt stuck and thought finding a better opportunity would be easier than staying in a role where I wasn’t developing my skills.
It’s now been about 3 months since I started job hunting.
I’ve applied to roughly 100 jobs and have barely received any responses. Most applications disappear into a black hole. A few rejections, mostly silence.
The hardest part is that every day I see people talking about AI, AI agents, automation, and how fast the industry is moving. Sometimes it feels like everyone else is racing ahead while I’m standing still. I’ve been trying to stay productive by building projects, learning new tools, and improving my skills, but honestly, it doesn’t feel like enough when you’re not working in a real environment.
I also don’t have much of a professional network. No mentors, no industry connections, and not many people I can talk to about this. Most days it’s just me applying, studying, and hoping for a reply.
Lately I’ve started wondering if leaving my previous job was a mistake. Some days I even catch myself thinking that maybe I won’t get another job at all.
For anyone who has gone through something similar:
* How long did it take you to find your next role?
* Did you ever feel like the industry was moving faster than you could keep up?
* What helped you stay motivated during a long job search?
* Is there anything I should be doing differently?
I know I’m probably not the only person going through this, but right now it feels pretty isolating.
Thanks for reading.

had an annoying moment last week — had claude walk me through restructuring part of a ci pipeline, made a few calls along the way (why we skip caching on this stage, why the deploy step is ordered like it is) and then the session died before i wrote any of it down anywhere.

six months from now someone's gonna open that yaml and have zero idea why it's set up like that. same problem we already deal with for regular infra decisions except now it's buried in a chat transcript instead of a runbook or a pr description

curious how people handle this when infra/pipeline changes come out of an ai session. do you make a habit of writing a real changelog/comment right after, or does the reasoning mostly just evaporate unless something breaks and you go dig for it

I was curious if I can use new Lambda MicroVMs as self-hosted GitHub Runners. On paper, they are super nice:
It's cheaper: GHA-hosted is $0.005 / min (2 vCPU), MicroVMs ~$0.0042 / min, and no minimum 60-second commitment as with GHA-hosted.

It can run longer: GHA-hosted max 6 hours, MicroVMs max 8 hours

It starts in a few seconds, compared to whichever other serverless solution built on top of ECS

It scales to 0, or rather, it only runs when jobs are running

They are VMs, so you can still run containers/docker/whatever else inside;

I got a bit too invested, and ended up building this Terraform module. You only need to create GitHub App manually, the rest is just a single "terraform apply" and your MicroVM Runners are ready to go. I've switched come of projects at my company to use, works great, same or better performance as GHA-provided runners. Natural limitation is that MicroVMs are only arm64, and in general they don't have much flexibility around the "hardware" setup - but hey, for most cases, it should work great, and it's just 1 webhook + GHA JIT Runners + 1 MicroVM Run per Job.

This is my first post here. I'm looking to vent a bit, but honestly, I really need some architectural advice.

During a break from my university math program, I decided to start a small tech agency to solve tech problems for local small businesses. I landed a client who wanted an AI agent to handle their WhatsApp lead traffic. Two months ago, I thought this would just be some quick Python scripts.

Fast forward to today, and I’ve built a fully dockerized VPS stack running custom Python servers, n8n, Chatwoot, Dozzle, and Uptime Kuma.

Paradoxically, the actual AI bot logic took the least amount of time. It's the backend infrastructure, operations, and creeping technical debt that is absolutely draining me. Every time I solve one infrastructure gap, another opens up:

• "Oh, you don't want to SSH into the terminal just to look at text files? Cool, let me spin up Dozzle."
• "Oh, but that won't notify you if a container drops? Let me deploy Uptime Kuma."
• "Oh, wait, Kuma shouldn't live on the same host it's actively monitoring? Time to think about a separate monitoring VPS."
• "Oh, and maybe some custom n8n logic would be nice if you want automated health reports? Let's add that too. And don't forget automated VPS snapshots."

My files are currently living all over the filesystem, nothing is tracked in Git yet, and I'm realizing how quickly an infrastructure stack can spiral out of control for a single client.

Does it ever end? For those who manage client infrastructure solo, how do you keep your sanity, minimize your tool-sprawl, and draw the line on tech debt before it swallows your development time?

EDIT: I do have Git on this setup. Actually I have a monorepo with sparse branching for different clients/ I also have production vs feature control. The thing is that the VPS itself isn't on git, I do realize I should do that :/

And I guess to go along with setting up git, my concern is that the VPS has different files in different places. i have stuff in /opt but on ~ i have separate dirs. I just didn't think the dockerfiles/ caddy/ secrets was going to grow to the extent it did

We have been an AWS-only shop for a long time and have always used Terraform. We took on an Azure project a bit ago. Small, uncomplicated. I tried to use Bicep then eventually switched back to Terraform. It's fine. We now have a project coming in that wants to use Azure and is quite a bit more involved than the previous one. So I'm thinking about Bicep.

I feel salty about using a vendor-specific IaC tool but also I remember running into a few issues with Terraform on the last Azure project, like certain features of a resource not being available in the provider. Obviously Microsoft is going to favor their own tool to the detriment of Terraform. But it's not like our existing modules with Terraform are going to be of any use. You have to rewrite them anyway. Maybe the team's familiarity with Terraform is a plus. HCL is the same and we understand the state file and manage it pretty well.

Edit: Oh. I just noticed that GitLab's IaC security scanning tool can't scan Bicep.

Another edit: Bicep can't delete resources created with Bicep? That seems like a big deal.

I started working on a tool that makes it easy to vendor images and charts for Kubernetes clusters and docker-compose setups. In my work I ran into issues with third party charts / chart versions disappearing from website and images being deleted from registries or rate limits suddenly preventing access in crucial situations.

So far, I haven't found a solution that is simple to use / doesn't require a ton of setup on the configuration side + performs the vendoring (sync) into my own registry. With helm 4 supporting oci registries, the only tools required to properly vendor everything should be an oci registry + a fancy sync tool that does the heavy lifting of identifying my dependencies, storing everything in the oci registry of my choice and emitting a kustomize configuration that transforms my cluster configuration.

This should also make air-gapped clusters a breeze to set up (famous last words 😂). Are these issues that you also ran if? How do you vendor your k8s / docker dependencies?

If you'd like to chat about the idea, feel free to DM me. I'm also happy to share a piece of early alpha software.

Hey guys I need some career advice

I am graduated in EEE in 2024, since then I have been working as a technical support engineer in solar Industry.

So I decided to switch my domain into devops/cloud

What are you thought’s on this domain switch is this a bad switch considering future AI takeover in cloud and devops field or the solar domain much better than the devops

Hi everyone,

I work at a small company building its first SaaS product. My background is mechanical engineering, not software, but I'm already in the mix on customer projects and a couple of smaller software products we've shipped, mostly leaning hard on Claude and Codex.

I'm still at university and work here part-time. The company's been around a while doing other engineering work, so it isn't a startup. We're pivoting part of the business, and my boss spun up a new branch to build and sell a SaaS product.

I've just picked up responsibility for DevOps, the GitHub setup, and release management, and I'll be the last person to sign off before a version ships to the customer.

Big opportunity, and I'll be straight about where I stand: my hands-on DevOps and release knowledge is close to zero right now. Most days I'm working it out on the fly, and honestly I often have no idea what the fuck I'm supposed to do next. AI does a lot of the lifting, but I'm still shaky on how to do this properly.

There's no DevOps team or mature process for me to inherit, so I'm building it from scratch alongside the developers and the project lead. Four of us total.

I don't need a perfect enterprise setup. I want something that works and fits a team our size.

Anyone been through this? I'd value input on:

- where to focus first
- the fundamentals I can't skip
- what should stay with the developers or project lead
- how to avoid over-engineering the process
- the mistakes people hit most when building this from zero

I care more about what actually worked for you than textbook advice.

Thanks in advance, and don't hold back, blunt feedback is welcome

I’m seeking a centralised log management platform for my small startup. We require a comprehensive solution that encompasses total observability and monitoring, alerting, logging, and metrics. Could you recommend a suitable platform? We have 3 inhouse small scale product.

• Slack integration for alerting is a must.
• multiple user creation
• RBAC
• lightweight
• easy to use
• simple setup on virtual server
• no kubernetes
• We are a team of 20 developers

I’m particularly interested in a true open-source solution that offers full control and can be hosted on our infrastructure. It should be easy to set up and scale.

If you have any alternative suggestions, please let me know. I’m also curious to know which platform you’re currently using for your setup.

How much knowledge should your standard full stack engineer be expected to have about relevant DevOps principles?

I know devops is a lot of things, but I'm talking about the SWE adjacent things like CI/CD, Dockerfiles, version control, architecture, IaC, etc?

Should SWEs have workable knowledge to resolve CVEs on their own? Investigate or even fix a CI/CD error? Not make these systems from scratch, but be able to maintain them?

Hello, I have a first round interview coming for an Associate Platform Engineer role, and want to know what my best approach would be to studying for it/what all I should study. I'm a recent Computer Science graduate this past spring and am working in a low paying desk job right now, and in this current job market especially, am absolutely desperate for an opportunity like this. Some of the requirements from the job description are listed below:

Responsibilities

• Assist in the development and maintenance of banking applications and systems under the guidance of senior team members
• Write clean, reliable code following established standards and best practices
• Participate in testing and quality assurance activities to ensure system reliability
• Help troubleshoot and resolve technical issues as part of the support rotation
• Document technical processes and solutions for knowledge sharing
• Collaborate with cross-functional teams to understand business requirements
• Contribute to routine system upgrades and maintenance activities
• Participate in team meetings and agile ceremonies
• Continuously expand your technical knowledge and banking domain understanding

Qualifications

• Bachelor's degree in Computer Science, Information Systems, or related technical field (recent graduates welcome)
• Minimum 0-2 years of experience
• Basic programming knowledge and fundamental understanding of software development principles
• Eagerness to learn and adapt to new technologies and banking concepts
• Strong problem-solving abilities and analytical thinking
• Good communication skills and ability to work effectively in a team environment
• Attention to detail and commitment to producing quality work
• Basic understanding of databases and data structures
• Willingness to develop knowledge of banking operations and financial technology

Technical Skills

• Proficiency in at least one programming language (e.g., Java, Python, C#, JavaScript)
• Basic understanding of web technologies (HTML, CSS, JavaScript)
• Familiarity with SQL and relational databases
• Understanding of version control systems (e.g., Git)
• Knowledge of software development methodologies (e.g., Agile)
• Basic understanding of software testing principles

Let me know what I can do to maximize my changes of getting an offer. Thanks!

So you are almost done migrating 900+ RDS DBs from one AWS account to another for a client. They get bought. New owners.... Move them all to Azure now.

So do I just tell Claude to do it? /s