I’m building AI for Cloud Engineers — a community focused on real, practical cloud engineering with AI. Not theory.Not recycled LinkedIn content.Real things engineers can use: - AWS / Azure / GCP tips - Terraform examples - automation scripts - cloud cost optimization - AI prompts for engineers - troubleshooting workflows - infrastructure templates - real-world cloud scenarios I’m thinking about turning this into something bigger than just a community. Possible directions: - free knowledge hub - paid resource library - cloud engineering toolkit - affiliate offers - live workshops - consulting offers - partner-led content - small expert network If you are into cloud, DevOps, automation, AI, FinOps, security, or infrastructure, and you’d like to contribute, collaborate, create content, share tools, or help shape the direction of this community, let me know. I’m open to: - contributors - technical writers - cloud engineers - DevOps engineers - AI builders - FinOps people - affiliate/partnership ideas - people who want to build useful products for engineers The goal is simple: Build a practical place where cloud engineers can learn faster, automate more, and stay ahead of what’s coming. If this sounds interesting, comment below or send me a message.

But the real problem is not “can AI generate code?” The real problem is: - who reviews the output? - what guardrails does the repo give the AI? - how do you stop AI from creating insecure Terraform? - how do you keep CI/CD changes reviewable? - how do you prevent copy-paste automation from becoming production risk? I built a practical AI Coding / DevOps workflow around this idea: AI should speed up engineering work, but it should not bypass engineering judgment. The workflow I recommend: 1. Give AI clear repo instructions 2. Use a PR review checklist 3. Review Terraform/IaC for security and cost risk 4. Add CI/CD guardrails before merge 5. Keep human approval for anything production-facing If you are using ChatGPT, Cursor, Copilot, Claude, or any AI coding tool for cloud/devops work, this is the part most teams skip. I also made a free starter kit for IT/cloud people who want a safer AI workflow: https://store.cloudpeakify.com/products/ai-it-ops-quickstart-kit-free And for developers / DevOps engineers, I packaged the full AI Coding Bundle here: https://store.cloudpeakify.com/products/ai-coding-bundle-for-devs-and-devops Question for the group: Where are you already using AI in your workflow? - Terraform / IaC - CI/CD - scripts - PR reviews - incident notes - documentation - something else?

Most cloud engineers use AI like a search engine. Paste an error, get a generic answer, go back to Googling. That's the wrong approach. The right way: treat AI as a junior engineer who reads everything instantly but needs precise instructions to produce useful output. Vague prompts produce vague output. Specific prompts produce tools. Here are 6 prompts I use every week. Copy them, adjust for your cloud provider, use them on real work today. --- 1. TERRAFORM PLAN REVIEW Paste your terraform plan output and use this prompt: "You are a cloud security and cost engineer. Review this Terraform plan and identify: - Resources that will be publicly exposed (public IPs, open security groups, public S3 buckets) - - IAM permissions broader than the stated purpose requires - - Resources likely to cause unexpectedly high costs - - Missing tags that would block cost attribution Format: Risk | Severity (High/Med/Low) | Line Reference | Recommended Fix [paste terraform plan here]" You get a structured risk table in 10 seconds. Doing this manually takes 30+ minutes. --- 1. LOG ANALYSIS (CloudWatch / Azure Monitor / GCP Logs Explorer) When you're staring at 400 lines of log output: "You are a cloud operations engineer. Analyze these logs and: - Identify the root cause of any errors or warnings - - Show the sequence of events that led to the issue - - Suggest 3 specific remediation steps with CLI commands where possible [paste logs here]" Works identically with CloudWatch Logs Insights, Azure Monitor, or GCP Logs Explorer output. Just paste and run. --- 1. IAM POLICY AUDIT Paste any IAM policy JSON and ask: "You are an AWS IAM security specialist. Review this policy and identify: - Actions that grant broader access than the stated purpose requires - - Any privilege escalation paths - - Resources scoped too broadly (e.g., * where a specific ARN should be used) - - Specific changes needed to enforce least-privilege

Alright, let's kick things off properly. Starting today, we're running the 7-Day AI IT Challenge — one small task per day, designed to show you how AI actually works in real IT and cloud scenarios. No prior experience needed. No setup required (yet). Just your brain and access to ChatGPT (free tier is fine). --- How it works Every day for the next 7 days, I'll drop a challenge post in this community. Your job: try it, share your result (screenshot, output, one sentence about what happened), and react to what others post. That's it. Simple participation, real learning. --- The 7 challenges Day 1 — The Prompt Test Ask ChatGPT: "Write a PowerShell script that lists all running services on a Windows machine and exports them to a CSV file." Run it (or just review it). Screenshot the output. Post it here. Day 2 — Cloud Terminology Decoder Ask ChatGPT: "Explain the difference between AWS EC2, Azure VM, and GCP Compute Engine like I'm a sysadmin who's never used cloud before." Share the most useful part of the answer. Day 3 — Terraform Starter Ask ChatGPT: "Generate a Terraform configuration to create an S3 bucket on AWS with versioning enabled." Post the code block. Bonus: tell us if you actually ran it. Day 4 — Automate Something Annoying Think of one repetitive IT task you do manually. Ask ChatGPT to write a script for it. Share the task + the script. Extra points for the most creative answer. Day 5 — Azure CLI Speed Run Ask ChatGPT: "Give me the Azure CLI commands to create a resource group, storage account, and static website in under 5 steps." Try it in Azure Cloud Shell (free, browser-based). Screenshot the result. Day 6 — Security Check Ask ChatGPT: "What are the top 5 IAM mistakes people make on AWS and how do I fix them?" Share the one that surprised you most. Day 7 — Your AI Project Pitch Describe a real project you want to build on cloud. Ask ChatGPT to give you a step-by-step plan. Share the plan here. Best one gets a shoutout and early access to Week 2 content.

These are the 5 prompts I use most often. Copy them, save them, adjust them to your cloud of choice. Prompt 1 — Generate Terraform from scratch "I need Terraform code to deploy [describe your infrastructure]. Use best practices: variables.tf, outputs.tf, main.tf structure. Add comments explaining each block." Prompt 2 — Debug a cloud CLI error "I ran this command: [paste your command]. I got this error: [paste the error]. I'm using [AWS CLI / Azure CLI / gcloud]. What's wrong and how do I fix it? Give me the corrected command." Prompt 3 — Explain any cloud concept simply "Explain [concept, e.g. 'AWS IAM roles vs IAM users'] like I'm a sysadmin who knows on-prem but is new to cloud. Use an analogy. Then give me a real example of when I'd use each one." Prompt 4 — Write a PowerShell / Bash automation script "Write a [PowerShell / Bash] script that [describe what you want it to do]. Add error handling and comments." Prompt 5 — Review my config for security issues "Here is my [Terraform / ARM template / Kubernetes YAML / cloud config]: [paste your code]. Review it for security issues, misconfigurations, and cost inefficiencies. List each issue and how to fix it." Save these. Adjust the brackets to your situation. Which one do you use first? Drop it in the comments.