Hello everyone. I want to publish my Medium blog to Medium publication that related with AWS but I confused. Here anyone can recommended me to publish?

I'm specifically not shopping for another CNAPP.
Assume posture, misconfiguration and exposure visibility are covered.
The layer I'm trying to compare is access reduction: start from what is actually used, understand effective reachability across identity and network and simulate the proposed change before it reaches prod.
My criteria are getting pretty simple:
observed usage/history that includes low frequency patterns
identity + network reachability, not IAM alone
simulation before enforcement
straightforward exception management after enforcement
Main goal is reducing unnecessary reachability and blast radius without adding another posture layer.

​

Hi everyone! I have **2 years of professional experience as a Blockchain Developer** and am considering transitioning into Cloud Engineering. I have basic Linux/networking knowledge, no hands-on cloud experience.

My planned path:

1. Linux, networking and troubleshooting

2. Python, Bash and Git

3. Cloud fundamentals

4. AWS or Azure

5. Docker, Terraform, CI/CD, monitoring and Kubernetes (Any two)

6. Practical projects and a relevant certification

I'd appreciate honest advice:

- What level of job can I realistically target?

- where do i fit in the market trend ?

- What skills are companies actually demanding for junior roles?

- Which certification would give me the biggest advantage?

- What salary and job-search timeline should I expect?

I'm looking for realistic advice from people working in the field. **I'm genuinely confused about whether this is the right move, and even a few honest words of guidance would mean a lot to me.** Thank you! 🙏

I graduated from Computer Engineering this year and am yet to get a job. I want to get into DevOps/Admin roles and know linux, containers, vms etc.

I'm thinking of getting certs for one of the major cloud platforms in order to get job opportunities, and am confused as to which one to pick and learn.

AWS:

I used up my free trial for AWS while in college for coursework and didn't rewlly learn much. Add to that, AWS cert costs are higher, and I'd have to pay to learn it too.

Azure:

I think I can get the 12 month free tier for this via the Github Student Developer Pack, which has not ended yet. Same thing can be used for 50% discount on AZ-900 cert which is already cheaper in India. Also, renewals are free.

GCP:

I honestly have no idea about this one, it has a higher cost for certification, and also has high costs for renewals. And I'm not clear on how long the free tier for this one will last as it's $300 free credits for 6 months or something.

I'm leaning towards Azure for the reasons mentioned above, but have heard that AWS is more popular and support is leagues ahead of Azure.

I want to know your opinions on what would be better based on your experiences with either, and your own career. I was thinking of going for learning azure with the free tier, and AZ-900 and AZ-104 certs, while learning AWS from videos and free resources online while trying them out in a local emulated environment like localstack, ministack or floci, to be able to put AWS on the resume and later get AWS certs from an employer or own salary if needed.

Also, while I'm at it, could you share your opinions on Zoho Catalyst? I just came across it recently and it has a seemingly good free tier, but doesn't seem as popular.

Turning off dev environments overnight sounds sensible until someone needs one late. A cheaper instance looks great until it slows down the build everyone is waiting for.

Have you made a cloud cost change that you eventually reversed because the savings weren’t worth the extra work or disruption?

What happened, and did you find a better compromise?

Writing a smaller IAM policy is not usually the bit that scares people, removing access from production is.

Configured permissions tell you what can happen. Usage tells you what actually happened, and that feels like the right starting point.

Then add network reachability, resource-side controls and enough business context to recognize monthly, quarterly, DR or incident-only patterns.

The actual question becomes less "can we write least-privilege JSON?" and more "what is actually used, what is reachable, and what can we safely narrow?"

That's why I think a lot of least-privilege projects stall when those signals live in different places rather than because policy syntax is hard.

Strongest counterargument?

Disclosure: I'm building Cloady at NeptoLab.

Cloady is a hosting platform for developers who want to deploy a small backend and operate it from one dashboard. Think a Node.js API, PostgreSQL, and uploaded files that must survive a redeploy.

You can deploy from GitHub, a Docker image, an uploaded folder, or a template. The workspace includes browser-based Terminal, Files, Database, Git, and Monitor tools, plus custom domains and automatic TLS. There's a CLI, REST API, Terraform provider, and MCP integration if you'd rather work from your existing tools.

The pricing model is capacity-based: choose CPU, RAM, and SSD, and the calculator shows the price. Reserved paid capacity costs money even while idle. A free workspace provides 0.5 vCPU, 1 GB RAM, 5 GB SSD, and 200 GB traffic without a card; paid presets start at $6/month. Once you exceed the free allowance, the full selected capacity is billed.

For anyone trying it, a useful first test is deploying an app with a database, persisting a file, redeploying, and checking that you can inspect logs and recover from a broken configuration. That's the everyday workflow Cloady is intended to simplify.

Product and calculator: https://cloady.com

Short promo video: https://www.youtube.com/watch?v=ctBCeewIin0

Questions and feedback are welcome here or in our Discord: https://discord.gg/3ZNVyUGse

Hello Everyone, I just accepted a new IT opportunity at a mid sized company with around 1000 employees. I previously was a IT Support Engineer II (It was mostly doing helpdesk support remotely along with basic M365 administration). In this new position, it is a pretty flat IT department and I will be owning tools such as Nerdio for AVD (since all users use thin clients), Cisco Meraki, Infrastructure monitoring using Site24x7, decomissioning servers, Entra ID/Intune Adminstration, and Claude deployment. Obviously this is a step up from my previous position. Based on these responsibilities, what would be an accurate title for me?

Hey everyone,

I’m primarily an embedded/hardware guy and could really use some direction from people who actually manage cloud infra.

The use case:

• Building cellular loggers connected to industrial flowmeters (RS-485 Modbus) + GNSS modules.
• Each device pushes flow rate, totalizer, GPS coordinates, and status over HTTPS/MQTT every ~10 minutes.
• Scale: Starting around 200 devices and ~100 external clients/users.
• Strict multi-tenancy is non-negotiable: Client A can only see their meters on a map and historical flow graphs; Client B only sees theirs.
• Monthly infra budget is tight , i want to keep it as less as possible

Where I'm stuck: I know virtually nothing about cloud architectures, databases, or DevOps. So idk from where to begin building such solution

First of all, I’m still a student, so I’ve got some time to learn (it’s not like I’m in my last uni year)

I’ve got decent CS knowledge, I’m running linux as my daily, and I have a server/home lab py is also my favorite language

im not trying to underestimate how difficult it is to get into cloud engineering, which is why I’m asking: is getting your first job in a cloud-related role really that difficult? Is there a more direct path into the field, even if it takes a lot of hard work?

if you have got any tips please leave them below

Ty

I have been a software developer for about 8 years, now I see more and more AI implemented in the full cycle. Using open-spec or other tools that make you pretty much just review code generated by AI.

I just finished the AWS Solutions Architect Associate course and, while looking at Cloud Formation, made wonder if the cloud will eventually suffer the same fate. AI will help create infrastructure easier using IaC I believe.

Do you think, like in software, it will become harder to enter cloud roles now? I know Solutions Architect isn't a junior role, but even as a cloud engineer, I don't see it become easier.

It seems like everything that touches software will have less and less people overall. After your certifications, maybe not AI or MLOps, have you feel it's tough for you to land a job? How is the day to day in your Cloud environment?

I'm currently working in a Google Cloud migrations role, but I feel that it is very niche because not that many companies use Google Cloud. I have multiple certifications Azure, Google Cloud, and CCNA. I have been in tech for 6 and a half years. I've worked in help desk, system administration, and cloud admin/migration roles.

I'm deciding between applying to cloud admin, devops, or switch to data analytics. I am having trouble getting any interviews when applying. I'll have some recruiters from linkedin for devops roles. They will ask about Terraform, Kubernetes, KQL, Azure, AWS, AD, vmware, kubectl command, yaml commands, powershell, pipelines, Azure Devops artifacts, etc all in the same interview. It can be overwhelming and it feels like a test where you never know what they will ask. I worry about on call work with devops roles. Devops does feel like the next step.

I'm looking because my current company has done a layoff a year for the past 4 years. The workload has been really high with 3 to 10 projects at a time plus other goals. Most of the other jobs I interview for are for smaller startupish companies that I don't trust the stability of long term.

I have had some friends suggest data analytics because they said the interviews and daily work may be more straight forward and not as difficult as devops. My biggest worry is if I'll even be able to get an interview for a data analytics role, or if I'd have to take a pay cut. I make around 100k in my current role. What's bad is there's no opportunity for promotion at my current role because I'm on a really small team within a small division.

Would it be possible for me to switch to data analytics? Would data analytics actually be better work life balance and interview wise, or are my friends just being overly helpful? A third option is software engineering, but I hear horror stories online of leetcode and whiteboard interviews. However, there are more jobs for software engineering positions. Based on my situation what type of roles would you suggest I apply to Devops, SWE, or Data Analytics?

i know tons of this have been answered in here, but they're not tailored to my specific situation.

Im currently IT Service Desk L2, and studying for AWS SAA C03.

My daily work involves Microsoft though, troubleshooting Win7 / Win10 embedded systems and using powershell batch scripts to fix problems and install stuff.

My goal is to transition into a Cloud Engr or Cloud Systems Admin in 3 years, but i'm thinking maybe going Azure route will help bolster my experience before shifting into cloud roles due to the nature of my work.

However, I love Linux. I lose myself tinkering on the terminal for different commands and combine them to do different things.

Help me sort out my thoughts for future career, Im a 40 yr old guy but still indecisive and i feel ashamed because i have a son and cant even iron out my ideas for something like this.

your help is appreciated

I was reading about how many early-stage unfunded startups get accepted to AWS for more cloud credits than the Activate one worth 1k, how is that done ? Thank you

Hey i am starting cloud computing ik basic like linux, networking but i want to know the main cloud computing thing now.. I asked ai it suggested me aws, Microsoft and google free courses or something like that but I want some free video-oriented course where I can see what's going on and how to do this thing... Please help me thank you

Every agent turn is carrying tool_count=28 even after the router narrows the task to 4 relevant tools, so 24 schemas stay in the context window for no reason. We’re a 3 person team running 1800 sessions a week and each turn logs input_tokens=7600 from the schema block. The average session lasts 9 turns, which leaves 68400 schema tokens riding through each session. Our average session costs < $1 and the weekly < $1000. I checked the provider accounting and our request logs twice before blaming tool schema inflation, the numbers kept pointing back to the same block. 

The pruning estimate gets us to about $0.19 per session and $342 per week, but I don’t want to save money by making tool selection less reliable. We’re comparing Datadog and Braintrust for span level token attribution and trace filtering on tool_count, plus a clean task quality comparison before and after pruning so the input token cost doesn’t become the only metric that matters.

p95 cost should tell me whether the long sessions improve too and I’m testing whether the router can expose only the 4 selected schemas without breaking recovery when the first choice is wrong. Any suggestions on pruning tool definitions without making the agent worse at selecting a fallback appreciated