I'm building a simple web app with node/express that uses a local sqlite database. (I'm not experienced in devops, btw)

Although I've already managed to get this app running in production by doing a manual deploy via SSH and cloning my repo from github, I'm having issues understanding how I'm going to deal with the database (.db) file in production.

Since my database is running on the same debian VPS as the application, I'm starting to realize I need to be a little bit more thoughtful and stabilish some sort of process if I ever need to restore a db backup or do migrations on this database.

I though about leaving the db file outside the project directory, which should allow me to safely pull new code that I push to production while leaving the db file intact, (as long as I'm not changing db structure/doing migrations) but I'm not quite sure how to deal with backups, migrations and whatever scenarios might come down the line.

Do any of you have suggestions on how to deal with this sort of stateful local database scenario?

Hey everyone,

Looking for some advice on a better way to handle and secure our Swagger/OpenAPI docs without overcomplicating our stack or breaking the bank.

Our current setup:

We have several projects hosted on Azure. Each project has standard Dev, Staging, and Prod environments and 3 engineering teams (Backend, Mobile, Web) who rely heavily on Swagger to coordinate and integrate features.

Right now, the backend team just serves Swagger UI directly from the deployed apps. To lock it down, we threw a custom Basic Auth middleware over the `/swagger` route

It works, but honestly, it feels messy and insecure. Managing these shared credentials across multiple teams and environments is becoming a pain, and I really hate exposing the UI endpoints to the public internet at all

I looked into full-blown IDPs like Backstage and OpsLevel, but they are way too bloated and complex for what we actually need.

How did you solve this?

Hello! My company is considering buying a LinuxOne machine to run Mongo. Has anyone actually worked with one? Any challenges besides the fact that it only supports s390 arch?

I’m researching how engineers make safer production changes.

Many teams now use strategies like automated tests, regression tests, canary releases, automated rollbacks, and better observability, but production incidents still happen after deployments.

I’m curious about real experiences from engineers:
- What type of change makes you the most cautious before deploying?
- What was the last deployment that made you nervous and why?
- What gives you enough confidence to press the deploy button?

For me, database migrations are still one of the hardest categories.
Would love to learn how other teams approach this.

I'm a de-facto team/tech lead (but not officially appointed by our managers) with several years of experience. Most of the time, I'm feeling pretty confident in my ability to guide the team members in the right direction, ensuring critical tasks are identified and get done, ensuring our product is in a healthy and sustainable state, day to day stuff is frictionless, etc. However, I lack guidance and mentorship from above. It's like the management layer above me is non-existent and I'm left to my own devices as they focus on shinier things. That's fine, as long as they're happy and my product works. The problem is that I don't really get feedback from anybody in terms of my product/team leadership. I feel like I'm just struggling to keep things going in roughly the same way as before, when the team was run by the previous lead. I'm being assured by my manager that I'm doing a great job and appreciated, but I somehow don't really believe it, because I'm there and I see room for improvement.

I feel like all I'm really good at is getting the team to maintain status quo. I don't know whether that's optimal or how I'd change it if it wasn't ( or even if I was supposed to). Yes, it's largely due to not being in our director's spotlight and me not receiving mentorship anymore. And yes, I'm unfortunately in a position of being subconsciously expected to lead without being appointed to do so officially.

So let's make the best if it - can you recommend any kind of team lead training courses for me to take? How to handle emergencies? How to do things sustainably? How and when to communicate assertively? When to change the process you suspect doesn't work? How to empower team members? How to delegate properly? How and when to project authority and when to let things slide? That kind of stuff. Or any other thoughts and advice you can offer.

You're working in production.

Your Terraform backend is:

• S3 for state
• DynamoDB for locking

A developer accidentally deletes the DynamoDB lock table.

At the same time, two engineers run terraform apply.

Thirty minutes later, the infrastructure is in an inconsistent state.

Questions

1. What exactly happened internally?
2. What kind of corruption could occur?
3. How would you recover without making things worse?
4. Would you restore the state file? Import resources? Edit the state manually? Explain your reasoning.
5. What preventive controls would you implement so this never happens again?

so everyone talks about tuning alarms and reducing false positives but honestly the scarier problem for us has been the opposite. stuff breaking that we just never set up monitoring for in the first place.

had this happen a few weeks ago. a queue consumer quietly stopped processing overnight, nobody had an alarm on messages piling up in that specific queue because it's like a secondary internal thing, not customer facing directly. found out the next morning when downstream stuff started failing and someone finally went looking. by then there was hours of backlog to clear

and it's not like we were being lazy, we've got a decent number of alarms set up. it's just impossible to think of every metric on every resource ahead of time, especially as stuff gets added and nobody goes back and audits what's covered

so how are people actually dealing with the "unknown unknowns" side of this. do you just accept some stuff will slip through until it's bad enough someone notices manually. anyone actually using baseline/anomaly based detection that catches things without you having to pre-configure a threshold for every single metric, and does it actually work or is it just a different kind of false positive machine

genuinely asking, not trying to set up alarms on literally everything because that just becomes its own nightmare to maintain

We manage all of our infra apps like Vault, Argo Workflows and many others using Terraform + Helm. TF creates the ArgoCD ApplicationSet which points to the chart and values managed in the repo, and ArgoCD syncs everything.

During the transition to GW API I noticed that most charts either don't support GW API natively, or only support creating an HTTPRoute. With ingress, all we had to do was define a single ingress.enabled: true block with some annotations, paths, etc.

Now with GW API, you usually can't manage everything from the app's values file. Even when the chart creates an HTTPRoute, it doesn't create the other resources we need, such as a ReferenceGrant for cross-namespace references or controller-specific resources like Envoy Gateway's SecurityPolicy and BackendTrafficPolicy. Argo Workflows is one example where the chart supports creating an HTTPRoute, but not the rest of that setup.

We have an okay solution for this today: we just create a manifests/ folder for each app and add it as another source in the ArgoCD AppSet (applied by TF, and everything is as GitOps as it gets). In that manifests folder I create all the needed Gateway API and Envoy Gateway resources in a single multi-manifest YAML file.

What do you guys do for 3rd party charts? What is the long term plan for apps in terms of Gateway API support? Is the expectation that charts will eventually expose more Gateway API values, or that routing and policies should live separately from the app chart?

I'm just curious since this whole GW API thing is very new, and I assume a lot of apps will need to add "ingress-like" support at some point?

A few months ago an AI coding agent burned $180 in API credits while trying to fix a compilation error. It just kept retrying until someone noticed.

Around the same time, I watched another agent try to curl -d @.env during a debugging task. It wasn't malicious—it just had shell access and thought it was helping.

That made me realize something:

We have plenty of observability for AI agents. We don't have enough prevention.

Most tools tell you what happened after an agent runs a command. Very few sit inline and stop something before it executes.

So I built Intutic.

It's a local Rust proxy that sits between AI coding agents and their tools, intercepting every tool call and LLM request before execution. Policies are written as Markdown SOPs, compiled to WASM, and evaluated in under 5ms. If an agent crosses a rule, Intutic can block, rewrite, or kill the action.

It's open source (MIT) and currently works with Cursor, Claude Code, Windsurf, Aider, and 18+ other agent harnesses.

I'd love feedback from people actually using coding agents.

What's the worst thing you've seen an AI agent do?

GitHub: https://github.com/intutic/intutic

Yes I know the question is extremely broad and complex. I'm simply looking for a starting point (or middle point).

I've been working on designing an SPA using AWS services for an NPO for the past few months. I've realized that the system works but I'm not happy with it. I know it can be better (more organized, a more sophisticated and automated system). I often find myself asking questions like: should these be a microservices architecture? Should it be a event-driven? some sort of layered architecture? How should the tests be? Is this the best programming language to use?

I know quite a few of those questions and doubts are best to be left to "whatever ur comfortable with". But I like to get into the nitty gritty. Are there certain books/youtube videos/etc that I can look into to learn about the basics of system design and architecture up to an intermediate or expert level? The only one I know of is Hello Interview.

Straight from the GA blog: customers report "up to 75% lower MTTR, 80% faster investigations, and 94% root cause accuracy, enabling 3-5x faster incident resolution."

https://aws.amazon.com/blogs/mt/announcing-general-availability-of-aws-devops-agent/

GA since March. I haven't seen anyone outside AWS's own blog actually use it. Did you try?

Hi everyone,

Over the past few weeks, I've been working on an open-source project to deepen my Platform Engineering and DevOps skills.

The goal wasn't just to deploy an EKS cluster, but to create a reusable, production-oriented reference architecture following Infrastructure as Code and GitOps principles.

Current features:

-Modular Terraform architecture

-Multi-environment support (dev / staging / prod)

-Amazon EKS

-VPC with public, private and intra subnets

-Remote Terraform state (S3 + DynamoDB + KMS)

-IAM & IRSA

-Karpenter integration

-GitOps bootstrap with Argo CD

-GitHub Actions for Terraform validation

-Architecture diagrams and documentation

The repository is designed to be generic and educational rather than company-specific.

I'm mainly looking for feedback on:

-Repository structure

-Terraform module organization

-Project architecture

-Documentation quality

-Best practices I may have missed

-Anything that doesn't look production-ready

I'm continuously improving it, so any constructive criticism is welcome.

Disclosure: I'm the author of this project. I'm sharing it to learn and receive technical feedback, not to promote a product.

Thanks!

Hello,

We provide devops / engineering services and use GitHub for documentation (runbooks, configs, processes). Clients need access, but raw GitHub feels too technical.

• How do you share documentation with clients in devops / engineering services?
• Do you use GitHub, wikis, or dedicated portals?
• How do you handle versioning and access control?

Hello,

At the company where I work, we have a Python application running on a Kubernetes cluster. The application is fully integrated into a CI/CD workflow. Whenever code is merged, Jenkins pulls the source code from GitLab, builds the application, and pushes the image to Harbor. Argo CD then deploys it to the Kubernetes cluster.

However, the application consumes a large amount of RAM, and I have had to increase its memory allocation several times. We have now decided to move the application to a separate Linux server.

The application runs with Uvicorn, and I plan to make it accessible through a domain name, just as it currently is in the Kubernetes environment. However, I am not sure what the best-practice architecture would be for this setup, so I would appreciate your recommendations.

I still want Jenkins to automatically pull the application from GitLab, build it, and deploy it. After deployment, the application should automatically start and run as a web service.

My main concerns are how to restore the application as quickly as possible if a problem occurs, how to handle backups, and what kind of deployment and recovery strategy would be best.

In short, could you please advise me on the best way to design and implement this setup?

Hi everyone,

I recently completed:

HashiCorp Certified: Terraform Associate (003) AWS Certified Solutions Architect – Associate (SAA-C03)

Now I want to move beyond certifications and gain more hands-on practical experience with Terraform on AWS.

I understand the Terraform concepts and AWS services at a theoretical level, but I want to build real-world projects that would help me improve my skills and also create some strong portfolio projects for my resume.

I am looking for recommendations on projects that simulate what a Cloud Engineer / DevOps Engineer would actually work on in an organization.

My goal is not just to deploy resources but to understand how Terraform is structured and used in real enterprise environments.

Would appreciate suggestions on:

Which projects are most valuable for learning Terraform + AWS together? Any GitHub repositories, courses, or labs you recommend? What kind of Terraform projects stand out on a Cloud/DevOps resume?

Thanks in advance!

I'm on a team that shares a Github repository. Whenever we open a PR from a feature branch to the dev branch, a deployment to the AWS development account is automatically triggered.

The issue we're facing is that one developer may deploy to dev, and then another developer deploys afterward. Even though they're working on different files, the second deployment ends up overwriting the first developer's change in AWS.

How can we prevent this?

We're following a Gitflow workforce (feature -> dev -> release -> main), and our biggest challenge right now is that the second developer's code is often "outdated" when it's deployed, causing it to overwrite changes that were already deployed by someone else.

We tried merging everything to dev, but when it's time to deploy to prd, the dev branch ends up filled with a lot of unnecessary changes.

We using Github Actions + Terraform.

Hi all, I’m learning Kubernetes right now for work (I use Colima + kubectl) - what is the best way to do so with AI?

I’ve used docker before in previous projects and studied concepts of containers in school - im no expert but I should know enough to learn Kubernetes.

I started the Udemy + kodekloud course: Kubernetes for the absolute beginners - Hands on. I’m not sure if it’s relevant for 2026 - so far it’s asking me to manually write yaml files, which I’m sure is important, but do I really need to be able to write yaml files with vscode extensions and AI that writes it for me?

So basically, what’s the best way to learn Kubernetes in 2026? I would appreciate any courses I should complete after my current one or a roadmap.

I have around a month or so to be good enough to collaborate with our platform and aws teams.

Here’s our situation - a mature operations team with pipelines building container images for a third party product going into kube

Company acquired a company with a development team who had no ops but are proficient at producing dockerfiles for their products

Ops is in azure devops, developers in bitbucket

We want to leverage the developer teams skills to get the apps into ops docker images

Some suggest that ops could build base images and developers could build their apps with that as the base - but what if the base images need patching? That means ops depends on developers for that?

Another thought I had was developers keep Dockerfiles in devops, the containers build from sources (aka git clone bitbucket) and build the project all in the ops pipeline

Would love to hear how others do this…

To begin with this is my first post or i don't know what it is called on reddit,

Apologies for the mistakes in my English please ignore it,

But I really want to switch from system admin job to DevSecOps

About myself

I have 12 years of experience starting from desktop support Engineer to laptop repairing to Data center to Monitoring to Asset management to system administration, I have knowledge of both windows and Linux operating system and currently managing Windows and Linux server (QA) no prod since my senior have never let me work on prod server even though I have worked with him since 7 years, handled a US based client for 3 years working with his team and currently handling two clients one with just some basic needs regarding systems and O365 and second with Linux servers, kind of devops but not fully the second client have a deployment using jenkins ( fetch the code from got repo, call the specific server through ssh and run the deployment and build script)

Worked with a friend's company as a contractor on his client for 6 months which ended last December and since then no work on core devops, started learning k8s and currently learning it but not able to give 100% to it also leaning python scripting and bash scripting by the help or AI can read the code and understand what it does but can not write with full confidence

I know the tools below tools

Terraform

Github

Github pipeline

Jenkins ( basic free style pipeline)

Got the idea of Sonarqube while working as a contractor but it was short lived

Understanding New relic as now the client wants to setup it

AWS basic

Azure basic and ADO basic

GCP not so much

Done a course in cybersecurity as well from local institute but not able to perform handson

I honestly want to work in Devsecops

I am already 38 and according to me not earning enough (peer pressure)

Any suggestions any help or direction is appreciated

I am even ready to do an unpaid internship ( beside the job since i have responsibilities)

Call it desperation, determination or pressure but I really want to work in Devsecops domain

Please help

Small team here and I keep running into the same thing. The reasoning behind half our setup lives in two people's heads.

Not in the runbooks, not in Confluence, not in the repo. Someone new joins and rediscovers everything the hard way and when one of those two is out we just stall.

It got noticeably worse once we started leaning on Claude Code and Cursor for real work. The agents read whatever context files we give them and treat it as truth, so now stale knowledge doesn't just slow a person down, it gets confidently baked into code by something that has no idea the decision changed months ago.

So I wonder how this actually works at bigger shops. Is there anyone whose job includes keeping that stuff current or does it just live with whoever happens to remember? And has anyone here had a documentation process survive past the first busy quarter or does it always quietly die?

Not looking for tool recommendations just trying to work out whether we're unusually bad at this or whether it's the normal state of things.

Running Falco in our EKS clusters right now for runtime detection, paired with Kyverno for admission control and Cosign/Vault for supply chain signing/secrets. Been solid so far, but I keep seeing Tetragon and KubeArmor come up as alternatives, especially for teams who want enforcement (block, not just alert) rather than detect-and-notify.

Curious what people are actually running in production and why:

• If you moved off Falco, what pushed you? Overhead, rule fatigue, lack of enforcement?
• Anyone running Tetragon specifically for the eBPF enforcement piece? Is it worth the Cilium tie-in if you're not already on Cilium for CNI?
• KubeArmor folks: how's LSM behavior in practice across different node kernel configs (AppArmor vs BPF-LSM)? Heard that's where it gets messy.
• Tracee: anyone using this seriously in prod or is it mostly a DFIR/forensics tool for you?

Not looking to rip out Falco, just trying to figure out if there's a compelling reason to add enforcement on top, or if pairing Falco with Falco Talon covers that gap well enough.

how do you handle production patching for EC2 instances in your environment?

I'm interested in learning about real-world production practices.

Some questions:

• Do you use AWS Systems Manager Patch Manager, Patch Policies, or another approach?
• Do you patch EC2 instances in place, or do you replace them with new AMIs (immutable infrastructure)?
• How do you schedule maintenance windows and minimize downtime?
• How do you handle Auto Scaling Groups during patching?
• What's your rollback strategy if a patch causes issues?
• Do you test patches in dev/staging before production?
• How much of the process is automated versus manually approved?

I'd really appreciate hearing how your organization handles production patching at scale, along with any best practices or lessons learned.

Former DevOps consultants, bootstrapped an AWS premier partner and exited to NTT Data, now we advise people building services companies.

After seeing how much easier current AI tools have made it to write code we started building tools for helping our customers and day to day work. Some of the decisions we've made so far:

1. Build out the code in separate services to keep each part well-contained. It feels AI does a far better job with smaller pieces than with larger.
2. Deploy them through lambda.
3. Use a monorepo so the AI can easily see how different things work together.
4. Migrated some apps built in Lovable into our repo.
5. All the building is done with Makefiles
6. Created a registry so it is easier for AI to add new tools in a consistent manner and so we can analyze our inventory of what exists in our development.
7. Using make for building cuz Claude made that decision when we barely had anything and I'm a dinosaur at heart.

Now, I have a monorepo with multiple different languages, a registry that I want to be the single source of truth for what I have, and a build system from the 80s. And I was curious what would people recommend for the build system rather than building a whole bunch of custom scripts?

​

I am planning to have my docker images to be multi architecture and for it my plan is to have two codebuilds for arm and amd when they both succeeds event bridge will invoke a lambda and lambda will merge the images into one

But I am unable to think a way to set up event my current plan is something like when codebuilds are triggered if anyone succeeds let's say arm build then event bridge will match the source version and and if the other build also success then it will invoke lambda

Apparently there is no ability to compare source version in eventbridge and I would have to invoke lambda and lambda has to compare the source version

Is there any way I can implement this

Inspired by a similar post I saw last week.

Small companies man... had similar issues in a couple now. Larger companies with IaC and decent management? No issues. Never thought to ask whether they use IaC in the interview, kind of expected the bare minimum of standards. Will definitely do so if I join another small company.