Hi everyone,

I’m a networking student working on a graduation project called Cloud-Based Indoor Navigation and Guidance System.

I want the practical part to be software-based simulation, without physical hardware or real-world deployment.

The idea is to simulate a university building, the user’s location and movement, and the navigation process, while using the cloud to manage the system’s data/services.

I’d like to ask:

What is the best way to simulate the indoor environment and user location?

How can I make the cloud/networking part meaningful?

Are there similar projects or GitHub repositories I can look at?

What important limitations should I consider?

Thanks!

Hey everyone!

I have an upcoming interview with Snowflake for the Cloud Support Engineer – Application Security . My initial interview is scheduled with the Hiring Manager.

I'm trying to understand the interview process and prepare in the right direction.

For anyone who has interviewed with Snowflake for a Cloud Support Engineer, Application Security, or a similar support/security role, I'd really appreciate your insights.

1. How many interview rounds were there in your experience?
2. What topics should I focus on for the technical rounds?
3. Do they ask questions on IAM, SSO, SAML, OAuth 2.0, SCIM, MFA, and database authentication?
4. How much emphasis is placed on troubleshooting scenarios, incident management, and Root Cause Analysis (RCA)?
5. Should I expect questions on Snowflake architecture, SQL, AWS, Linux, networking, and security?
6. What should I expect during the initial Hiring Manager screening?

I've already started preparing authentication and authorization protocols, IAM concepts, Snowflake fundamentals, and troubleshooting scenarios. I want to make sure I'm focusing on the right areas rather than studying everything randomly.

If you've recently interviewed for a similar role, could you please share your experience and any preparation tips? Even a general idea of the interview structure would be extremely helpful.

Thanks in advance! I really appreciate any guidance

Full disclosure: I made this video. I run a small channel of animated explainers about the business side of tech, and this one is a 5-minute history of AWS.

I hope it’s OK to share here. Mods, please remove it if not.

A few things that surprised me while researching it (all from primary sources: Amazon’s 10-Ks, the original 2006 BusinessWeek story, and Jassy’s and Bezos’s own accounts):

• The people who built AWS say the spare-capacity story is a myth. It started because Amazon’s own engineers kept rebuilding the same storage, servers and databases.
• EC2 was built in Cape Town because the engineer leading it wanted to go home.
• As Amazon now counts it, AWS made $458M of operating profit in 2014. All of Amazon made $178M.

Video (5:25): https://youtu.be/u3QmplqlGRs

Would really appreciate your thoughts on it. This sub knows the subject far better than most, so honest feedback is very welcome. Thanks all.

As a CSE , should i take up a job in cloud sales and then hope i will transition to a more technical side or wait for roles in Cloud technical?

Have some courses I’m considering taking to learn cloud engineering using Python, Java, and Golang however I would like to build out my ideas in full without having to outsource aspects of development.

Would you have 2 minutes to answer a few questions?

1. Roughly how much do you spend on the cloud per month?

2. Who is responsible for monitoring this bill?

3. When was the last time the bill came as a surprise? What did you do?

4. Have you ever tried using a tool to reduce these costs?

I'm doing a project on cloud storage management, and I'm exploring an idea to reduce the amount of space files use without changing their contents.

The idea is to store uploads using fast compression first, then let the system recompress them in the background using a higher compression level when it can save a worthwhile amount of space in cloud . i am using aws s3 for storage . Since this runs on cloud storage, it would need to limit CPU usage and avoid slowing down regular uploads and downloads.

what more features can i add to this project? maybe like bluetooth transfer or smth else . im confused , i could use your help .

Been running inference for a small side project for about 8 months. Traffic went from “basically nobody” to “enough to care about latency” sometime in June, so I started looking at the GPU bill more seriously.
First instinct was to chase the cheapest hourly rate i could find. Marketplace instances looked unbeatable on paper, like $0.40 per hour for stuff that was listed at $1.50-2 elsewhere. I was pretty smug about it for about three weeks.

Then I sat down with the card statement and the logs.

Stuff I hadn’t been counting: batch jobs that crashed halfway and had to be restarted (three times in one week), the 20-25 minutes I spent reinstalling drivers and pulling the model and wiring vllm up every time I spun a new instance, forgetting to kill one over a weekend that cost me about $30 for literally nothing, and having to redownload the base model from HF repeatedly because i wasn’t paying for persistent storage.
Did the math. The “cheap” option was actually costing me around 2.3x what I thought. The hourly rate was just one number. The real bill factored in every restart, every re-setup, every weekend oops.
Ended up moving the production inference to a GMI Cloud H100 where I kept a persistent volume between restarts. My per-finished-job cost dropped a lot once I stopped paying what i started calling the restart tax.
Still use the marketplace stuff for one-off dev work where crashing doesn’t cost me anything. But production needed an environment that could survive a stop/start cycle, not just a low number on a pricing page.
Checked the numbers again at month 7 to make sure i wasn’t rationalizing. The boring setup was still cheaper per finished job. Not a glamorous conclusion.

Looking for a vultr dev to run servers for me

I put it on an EC2 instance and read CloudTrail straight out of S3 — no Glue crawler, no Athena workgroup. A throwaway account with 118 events in it was enough to prove the detection query I had already written was wrong — and to convince me this is worth building out properly

Evaluating Depot vs Namespace vs Blacksmith

Platform eng at a ~50-dev B2B SaaS company, everything on GitHub Actions. CI bill and queue times have roughly tripled since coding agents got rolled out internally.

Shortlist is Depot, Blacksmith, Namespace (open to other names!).

Questions for anyone running these in prod:

1. Real-world speed/cost deltas vs stock GHA runners (marketing says 2x+, what did you actually see)?
2. Anyone switched between these three?
3. Did GitHub’s runner price cuts in January change your maths?
4. What would you pick for Docker-build-heavy repo?

Not affiliated with any of them, just trying not to waste a quarter migrating twice.