LambdaTest is hosting TestMu, the world’s biggest virtual software testing conference featuring 80+ powerhouse speakers from Google, Amazon, Accenture, and beyond.

Created by the community, for the community, it’s a space to grow, connect and lead together. We’ll have deep-dive sessions on emerging trends in engineering, DevOps and Agentic and AI powered Software Testing.

3 days of power-packed sessions with 80+ speakers and 60+ sessions, you will also get an opportunity to connect and engage with 50k+ attendees from 120+ countries.

You’ll gain cutting-edge insights from world-class speakers on AI, automation, and the future of testing and get a chance to explore next-gen tools, frameworks, and strategies to transform your testing workflows and accelerate innovation.

All registered attendees will have access to the recordings as well.

Showcase your skills in live challenges and quizzes for a chance to win prizes worth up to $10,000 and gain global recognition.

https://redd.it/1mscsvd
@r_devops

I built an open source AI workflow orchestrator with GitOps-friendly YAML DSL

Hey DevOps folks, I wanted to share an open source (Apache 2.0) project that bridges the gap between AI capabilities and DevOps practices.

Lacquer (https://github.com/lacquerai/lacquer) is an AI orchestration engine that brings Infrastructure-as-Code principles to AI workflows. Define complex AI pipelines in YAML, version control them alongside your infrastructure code, test in dev environments, and deploy to production with confidence. Here's simple example that summarizes a given pr:

version: "1.0"

agents:
  code_reviewer:
    provider: openai
    model: gpt-4
    temperature: 0.3
    system_prompt: You are an expert code reviewer who analyses pull requests.

inputs:
  pr_number:
    type: integer
    description: Pull request number to review
    required: true

workflow:
  steps:
    - id: fetch_pr
      run: node scripts/fetch_pr.js
      with:
        pr_number: ${{ inputs.pr_number }}

    - id: analyze_changes
      agent: code_reviewer
      prompt: |
        Please analyze this pull request and help me review it:
        
        ${{ steps.fetch_pr.outputs.diff }}
        
        Please provide:
        1. **Summary**: What does this PR do in simple terms?
        2. **Key Changes**: What are the main files/functions modified?
        3. **Potential Concerns**: Any issues or risks to be aware of?
        
        Keep explanations clear and accessible.

  outputs:
    pr_analysis: "${{ steps.analyze_changes.output }}"

I built this because I was tired of AI tools that don't fit into modern DevOps workflows - no version control, no reproducible deployments, no proper testing environments. Lacquer changes that by treating AI workflows as infrastructure:

- GitOps Ready: All workflows are YAML files that live in your repos
- CI/CD Integration: Run as part of your existing pipelines (Jenkins, GitLab CI, GitHub Actions, etc.)
- Single Binary Deployment: Ships as one Go binary - no complex dependencies or container orchestration needed
- Environment Parity: Test locally, stage in dev, deploy to prod with the same configuration
- Observability Built-in: Structured logging and metrics for monitoring AI operations

Perfect for automating incident response, PR reviews, documentation generation, or any repetitive task that needs intelligence. You can trigger workflows via CLI, REST API, or embed directly into your automation scripts.

It's early days, but I'd love feedback and suggestions about what you'd like to see from a project like this.

GitHub: https://github.com/lacquerai/lacquer | Website: https://lacquer.ai | Docs: https://lacquer.ai/docs

Thanks for checking it out!

https://redd.it/1mslngo
@r_devops

UPDATE: 24 hours later - built the terminal assistant you told me to build

Hey r/devops!

Yesterday I asked about building a local terminal tool for plain English commands. You gave me brutal (helpful) feedback. Today I have a working demo.

# What you demanded, what I built:

"Don't auto-execute, show me the command first" → Done
"Explain what each part does so people learn" → Added --learn mode
"Make it actually local, not another ChatGPT wrapper" → Curated command database
"Prove it works with a real example" → Live demo at praxis.hezico.com

# Here's what 24 hours of coding looks like:

Built a visual terminal demo showing exactly how this would work - you're troubleshooting a container that keeps crashing at 3AM.

The animated walkthrough shows:

Plain English: "which container keeps restarting and why"
Tool analyzes and suggests kubectl diagnostic commands
Safety prompt: "Execute this safe, read-only diagnostic plan? \[y/N\]"
Shows realistic output finding the OOMKilled container
Demonstrates the --learn command that explains syntax
Shows the business value (faster resolution, air-gap friendly, etc.)

# The visual demo: praxis.hezico.com

60-second terminal animation showing the complete incident workflow and how the confirmation/explanation system works.

# What I learned from your feedback:

1. Security concerns are real \- so everything is read-only diagnostics first
2. Learning curve matters \- so there's explicit explanation mode
3. Trust is earned \- so I show exactly what commands run and why
4. Context matters \- so it suggests logical diagnostic approaches

# Current status:

✅ Working visual demo showing the interaction model
✅ Waitlist to gauge interest and validate demand
🔄 Building the actual CLI tool based on this design
🔄 Adding more command patterns (Docker, systemd, networking, etc.)

# Still want your input:

1. Watch the demo \- does this interaction model make sense?
2. What command scenarios should I prioritize? (Docker debugging? Log analysis?)
3. Would your security team approve this approach vs external AI tools?

If this resonates: praxis.hezico.com \- join the waitlist to stay updated on development

Thanks for the reality check yesterday. This concept is way better because you told me what was broken.

24 hours from idea to concept demo. Worth building the real thing?\# UPDATE: Built the terminal assistant based on your feedback - here's what changed

Hey r/devops and r/sysadmin!

A few days ago I asked about building a local terminal tool that converts plain English to commands. Got tons of feedback (some brutal, all helpful) and spent the weekend building based on your suggestions.

# What you told me to fix:

"Don't auto-execute commands, that's dangerous" ✅ Fixed
"Show the command and explain what it does" ✅ Fixed
"This will make juniors dumber" ✅ Added --learn mode
"What if something breaks and they don't understand it?" ✅ Read-only commands first
"It's just a ChatGPT wrapper" ✅ It's a curated local database, no LLM calls

# Here's what it actually looks like now:

$ ask "which container keeps restarting and why"

🔍 Searching local command database...

📋 Suggested Command:
kubectl get pods --all-namespaces | grep -v Running && kubectl get events --sort-by=.metadata.creationTimestamp

📚 Explanation:
• kubectl get pods --all-namespaces - List all pods across namespaces
• grep -v Running - Filter out healthy running pods
• kubectl get events - Show recent cluster events
• --sort-by=.metadata.creationTimestamp - Sort events chronologically

⚠️ This is a READ-ONLY diagnostic command. Continue? y/N


> y

🚀 Executing...

nginx-prod 0/1 CrashLoopBackOff 47 3h
redis-cache 0/1 ImagePullBackOff 12 1h

Recent Events:
47m Warning BackOff

UPDATE: 24 hours later - built the terminal assistant you told me to build

Hey r/devops!

Yesterday I asked about building a local terminal tool for plain English commands. You gave me brutal (helpful) feedback. Today I have a working demo.

# What you demanded, what I built:

**"Don't auto-execute, show me the command first"** → Done
**"Explain what each part does so people learn"** → Added --learn mode
**"Make it actually local, not another ChatGPT wrapper"** → Curated command database
**"Prove it works with a real example"** → Live demo at [praxis.hezico.com](https://praxis.hezico.com)

# Here's what 24 hours of coding looks like:

Built a visual terminal demo showing exactly how this would work - you're troubleshooting a container that keeps crashing at 3AM.

The animated walkthrough shows:

* Plain English: "which container keeps restarting and why"
* Tool analyzes and suggests kubectl diagnostic commands
* Safety prompt: "Execute this safe, read-only diagnostic plan? \[y/N\]"
* Shows realistic output finding the OOMKilled container
* Demonstrates the --learn command that explains syntax
* Shows the business value (faster resolution, air-gap friendly, etc.)

# The visual demo: [praxis.hezico.com](https://praxis.hezico.com/)

60-second terminal animation showing the complete incident workflow and how the confirmation/explanation system works.

# What I learned from your feedback:

1. **Security concerns are real** \- so everything is read-only diagnostics first
2. **Learning curve matters** \- so there's explicit explanation mode
3. **Trust is earned** \- so I show exactly what commands run and why
4. **Context matters** \- so it suggests logical diagnostic approaches

# Current status:

* ✅ Working visual demo showing the interaction model
* ✅ Waitlist to gauge interest and validate demand
* 🔄 Building the actual CLI tool based on this design
* 🔄 Adding more command patterns (Docker, systemd, networking, etc.)

# Still want your input:

1. **Watch the demo** \- does this interaction model make sense?
2. **What command scenarios** should I prioritize? (Docker debugging? Log analysis?)
3. **Would your security team** approve this approach vs external AI tools?

**If this resonates**: [praxis.hezico.com](https://praxis.hezico.com/) \- join the waitlist to stay updated on development

Thanks for the reality check yesterday. This concept is way better because you told me what was broken.

*24 hours from idea to concept demo. Worth building the real thing?*\# UPDATE: Built the terminal assistant based on your feedback - here's what changed

Hey r/devops and r/sysadmin!

A few days ago I asked about building a local terminal tool that converts plain English to commands. Got tons of feedback (some brutal, all helpful) and spent the weekend building based on your suggestions.

# What you told me to fix:

**"Don't auto-execute commands, that's dangerous"** ✅ Fixed
**"Show the command and explain what it does"** ✅ Fixed
**"This will make juniors dumber"** ✅ Added --learn mode
**"What if something breaks and they don't understand it?"** ✅ Read-only commands first
**"It's just a ChatGPT wrapper"** ✅ It's a curated local database, no LLM calls

# Here's what it actually looks like now:

$ ask "which container keeps restarting and why"

🔍 Searching local command database...

📋 Suggested Command:
kubectl get pods --all-namespaces | grep -v Running && kubectl get events --sort-by=.metadata.creationTimestamp

📚 Explanation:
• kubectl get pods --all-namespaces - List all pods across namespaces
• grep -v Running - Filter out healthy running pods
• kubectl get events - Show recent cluster events
• --sort-by=.metadata.creationTimestamp - Sort events chronologically

⚠️ This is a READ-ONLY diagnostic command. Continue? [y/N]


> y

🚀 Executing...

nginx-prod 0/1 CrashLoopBackOff 47 3h
redis-cache 0/1 ImagePullBackOff 12 1h

Recent Events:
47m Warning BackOff

pod/nginx-prod Back-off restarting failed container
46m Warning Failed pod/nginx-prod Error: OOMKilled

✅ Found your problem: nginx-prod is getting killed for using too much memory
💡 Try: kubectl describe pod nginx-prod -n default


# For the "juniors won't learn" concern:

$ ask "show memory usage" --learn

📚 Command Breakdown:
free -h

• free - Display memory usage statistics
• -h - Human readable format (GB/MB instead of bytes)

💡 Related concepts:
• Virtual vs Physical memory
• Buffer/cache vs actually used memory
• When to worry about memory pressure

📖 Want to learn more? Try: man free


# What makes this different from "just use ChatGPT":

* **Actually local** \- works in air-gapped/secure environments where external AI is blocked
* **Curated commands** \- no hallucinations, just vetted syntax patterns
* **Instant response** \- no API calls, sub-second results
* **Context aware** \- understands your environment and suggests appropriate flags
* **Educational** \- designed to teach, not just execute

# I built a quick demo page: [praxis.hezico.com](https://praxis.hezico.com/)

Shows the full flow with a realistic 3AM incident scenario.

# The honest questions I still have:

1. **Would you actually use this?** Or is the learning curve of a new tool not worth it?
2. **Security teams**: Would you approve a local-only tool vs external AI assistants?
3. **For complex environments**: How important is customization vs out-of-the-box commands?
4. **Pricing model**: One-time purchase, subscription, or freemium?

# What I'm building next:

* **Alpha version** with 100+ common DevOps command patterns
* **Custom command support** \- add your own organization's specific commands
* **Audit logging** \- everything gets logged for security/compliance
* **Plugin system** \- extend with your own command databases

If you want to try the alpha: [praxis.hezico.com](https://praxis.hezico.com/)

Looking for 50-100 DevOps/SRE folks who deal with this pain daily and want to test it in real environments.

**TL;DR**: Took your feedback seriously. Built confirmation prompts, explanations, educational mode, and local-only processing. Still think it's stupid? Let me know why.

*Thanks to everyone who gave honest feedback on the original post. This is way better because of your input.*

https://redd.it/1mso7mb
@r_devops

Looking for a Study Partner in DevOps & AWS

Hello everyone 👋
I’m a junior backend developer with some hands-on experience in managing servers, which sparked my passion for DevOps.

I’m currently transitioning into the DevOps field and have started studying AWS. Alongside this journey, I’m also looking for an internship or junior-level opportunity where I can gain real-world experience and grow further.

💡 To stay motivated and make the learning process more effective, I’d love to connect with someone who shares the same interest in DevOps / Cloud (AWS) to do pair studying — exchanging knowledge, practicing together, and keeping each other accountable.

If you’re on a similar path or know someone who is, let’s connect! 🤝

https://redd.it/1msrv3o
@r_devops

What AI tools are you actually using in DevOps?

What AI tools everyone's implementing in their DevOps workflows these days.

Drop your experiences on wins, failures, whatever! Really interested in what's actually delivering value vs just AI hype.?

https://redd.it/1msts8r
@r_devops

Best udemy course?

Can someone please suggest a good udemy course to learn devops?

https://redd.it/1msue9z
@r_devops

How do your teams coordinate usage of shared dev/test environments?

Hey,
I’ve been thinking about a challenge I’ve seen in a few dev teams and wanted to hear how others handle it.

When devs, testers, or even sales people share the same environments, collisions happen.

For example:
- Someone deploys a new version while someone else is testing a bugfix on that environement
- a dev tests a feature and cleans everything up, while another dev wanted to present something on the same environment to stakeholders
- 2 devs test e.g an integration with the same IoT Device and affect each others tests

We‘ve tried to book an environment in a Spreadsheet / Wiki for a certain timeframe, but still some problems happen, that devs forget to check, if someone has booked an environment.

Recently I wanted to present a feature to the customer, but couldn‘t do it, since someone has overwritten my deployment.

Sure, we could have more environments / or an environment for each dev / team, but this would be more expensive and would require more maintenance.

Do someone else have this problem? How do you solve it?




https://redd.it/1msrf14
@r_devops

A script to quickly launch KIND clusters with ingress support

Made a small Bash script to make working with KIND (Kubernetes IN Docker) a bit easier. It interactively sets up a KIND cluster where you can:

Pick cluster name, control-plane, and worker nodes

Enable and deploy the NGINX ingress controller with one click

Expose custom ports from your cluster to localhost

It also auto-generates a kind-config.yaml so you can see (or reuse) the cluster configuration.

GitHub repo: https://github.com/sujal8976/kind-cluster-launcher

Note: If you want LoadBalancer or ingress to work properly, make sure you’re running cloud-provider-kind in the background.

Would love feedback from anyone who tries it out!

A few months into my DevOps journey — open to any advice or recommendations to boost my learning!

https://redd.it/1msxebi
@r_devops

How to deal with cloud formation IAM permissions (AWS)

I'm giving the cloud formation an IAM role.

It gives "insufficient permissions" error during template validation, during the setup process and the most cancer is in the middle of building up.

Any advice/trick on how to cover all of the necessary permissions?
A tool that can evaluate what permissions are needed?

Or just give all of the necessary permissions and just limit it by the resource tags?

https://redd.it/1mswwig
@r_devops

Need assistance and guidance on DEVOPS

I'm working on an IT currently (associate), I'm interested in DEVOPS, and I know the basics of clouds and I have basic hands-on Azure and AWS and AZ900 certification. I need some guidance or opinions as I'm trying to switch roles, Can I switch roles. You can share your experience.

I started on the computer networks as of now, and will try to update the learning path

https://redd.it/1msyf5e
@r_devops

I'm interested in Devops, what courses (paid or free) should I get to get started?

As title, I've decided to focus my career to devops, while I'm still relatively early on it, I want to start focusing to DevOps, what are courses, post-graduations or specializations I should take?

https://redd.it/1msvf6x
@r_devops

People are raving about AI but I don't see any "major" help in the DevOps realm

I have been using AI somewhat extensively and enjoy it a lot. I see it as Stackoverflow 2.0 and Google search 2.0. I use on a daily basis Gemini Flash 2.5, Gemini Pro 2.5, Qwen 3 Coder, and Claude Sonnet 4.0. My goto is always Gemini Flash 2.5 and Qwen 3 Coder for the price.

It's helped me in the following:

* Create boilerplates for different flavors of apps for a one-an-done deal. This includes Terraform/Opentofu, Helm, and Kustomize templates and directories.
* Debug syntax and small issues that I'm having errors with.
* Create small scripts of Python/Bash/Golang.


My Director and manager are pushing our DevOps team to host AI in our workplace like creating an [AWS MCP Server](https://github.com/awslabs/mcp). However, I am not a fan of this. Allowing a server to be in control of our infrastructure? (I'm still new to this but this sounds like a terrible idea. As long as it's for read-only purposes then I could see it beneficial but not truly.) Also, they are mentioning, "Why don't we look into a pipeline process using n8n, flowise, or [make.com](https://make.com) to simplify this process for developers in asking for DevOps help." This is where I shot back and said, "I don't see a purpose on utilizing AI yet in any way, especially pipelines. That is a world of hurt whoever is going to be managing that. Our process works just fine where they can follow our documentation. Unless you want the AI to summarize like Cliffnotes what it entails?" They did not like that our team did not have any ideas but I feel AI is still in it's infancy where everyone in their mother is riding the cash grab roller coaster.

Maybe I'm missing something eye-opening from AI so would appreciate any stories in how AI is used in your workplace. Interested in any ideas being implemented.

Brings me back to my original question, "Is there really any major additions that AI can do better in the DevOps realm?"

https://redd.it/1mt4krd
@r_devops

What are some small improvements that you did to a backend or frontend repo?

What are some small improvements that you did to a backend or frontend repo? Trying to figure out if there are little things I can do to make development better or faster for my team.

https://redd.it/1mt5klq
@r_devops

Someone created a DevOps version of Cards Against Humanity called "Clusters Against Humanity"

Im assuming many of you are familiar with the "Cards Against Humanity" game. Well, someone created a game called "Clusters Against Humanity" for Devops/Elasticsearch/Opensearch folks - looks pretty well done and thought the folks here would get a laugh out of it https://clustersagainsthumanity.com/

https://redd.it/1mtea9u
@r_devops

OCR struggling with Greek historical newspaper text - need advice on preprocessing/recognition techniques

Hi everyone!

I'm working on digitizing Greek historical newspapers from the 1980s and looking for advice on improving OCR accuracy for challenging text.

# What I'm working with:

* Scanned Greek newspaper pages (see attached image)
* Mix of Greek text with occasional Latin characters
* Poor print quality, some fading, typical newspaper scanning artifacts
* Historical typography that doesn't match modern fonts

# Current approach:

* **Tesseract with** `ell+eng` **language models** using various PSM modes (3, 4, 6)
* **Preprocessing pipeline:**
* Grayscale conversion + upscaling (2x-3x using INTER\_CUBIC)
* Noise reduction (Gaussian blur vs bilateral filtering)
* Binarization (Otsu vs adaptive thresholding)
* Morphological operations for cleanup
* **Post-processing** with regex patterns for common Greek character corrections

# Looking for advice on:

1. **Better OCR engines** \- Has anyone had success with PaddleOCR, EasyOCR, or cloud APIs (Google Vision, AWS Textract) for Greek historical documents?
2. **Advanced preprocessing** \- Any specific techniques for newspaper scans? Different binarization methods, contrast enhancement, or specialized denoising approaches?
3. **Training custom models** \- Is it worth training on similar Greek newspaper text, or are there existing models optimized for historical Greek typography?
4. **Workflow optimization** \- Should I be doing text region segmentation first? Any benefits to processing columns/paragraphs separately?
5. **Language model considerations** \- Better to use Greek-only models vs mixed Greek+English for newspapers that occasionally have Latin text?

**Context:** Planning to scale this to thousands of pages, so looking for approaches that balance accuracy with processing efficiency.

Any insights from folks who've tackled similar historical document OCR challenges would be greatly appreciated!

**Tech stack:** Python, OpenCV, Tesseract, PIL (open to alternatives)

# you may check an image sample from here [https://imgur.com/a/tVgHWFq](https://imgur.com/a/tVgHWFq)

https://redd.it/1mtelkx
@r_devops

What does AI really look like in data engineering?

You guys might have noticed… there’s a lot of hype about “AI-ready data stacks.” But, it definitely isn't simple to achieve. Freshness, reliability, orchestration, the bar is just different when theres LLMs involved.

After a lot of brainstorming and chats with industry experts, we set up a 45 mins webinar with Hugo Lu (ex-data infra engineer, now founder @ Orchestra) where he’ll share his take on how AI is changing data ops, and what pipelines need to look like when LLMs are involved

Its totally free so if youre interested or just want to know the implications of AI in your stack, do join us 🙂

Its on  Aug 21, 1 PM ET

Register here!



https://redd.it/1mtfxl7
@r_devops

How to handle version numbers when promoting Docker Images from SNAPSHOT to Release

I have a CI/CD pipeline split into two stages:

Pre-Production Pipeline:

Builds & tests my Java Spring Boot application
Creates Docker image with -SNAPSHOT version (e.g., myapp:1.23.7-SNAPSHOT)
Deploys to Docker registry for testing

Release Pipeline:

Should promote the tested SNAPSHOT image to production with clean version (e.g., myapp:1.23.7)
Creates Git release tag
Deploys to production registry

The Problem: I want to follow the best practice of promoting already-tested artifacts rather than rebuilding. However, some files inside the Docker image contain version strings that will show -SNAPSHOT in production. For example:

`manifest.json` (visible in browser dev tools for frontend apps)
package.json
JAR manifest files
Any compiled assets with embedded version info

Current Environment:

Jenkins with Docker Pipeline Plugin
Nexus Docker Registry
Java Spring Boot backend
Angular frontend

The Core Question: How do you handle version string cleanup when promoting Docker images from SNAPSHOT to release without rebuilding the entire application?

https://redd.it/1mtip41
@r_devops

What's the next certification to goal?

I obtain the KCNA certification two weeks ago and i think CKA is the next certification to goal. It's natural course the CKA certification and i have pratical experience for one year and half with kubernets. Althought no provide the cluster, just delivery the applications by argocd and gilab. It's the better choice CKA exam? Or it's better investing in some cloud certification? I mean, not about the knowledge but the profile improvement, it's better choice Kuberstraount follow or some cloud provider like AWS to goal?
I have solid background like as sysadmin and obserbability, but still not considered myself a specialist.

https://redd.it/1mtjete
@r_devops