Got my undergrad in EE, doing CI/CD and DevOps work now that I love and want to grow in. ideal graduate programs would be:

1. Takes job experience as waivers for prerequisites I may not have (EE math transfers but I lack CS classic education)

2. 8 week courses (give or take)

3. If CS isn’t the best MS to pursue I’d love to hear alternatives

Thanks to all in advance!

I've recently met a former colleague who currently works as a DevOps engineer at a company that forces its employees to use AI (Claude, I guess). Each employee has an AI budget that he HAS TO use every month. I believe the goal is increasing productivity per employee.

I have a lot of concerns about this approach to work, especially from the perspective of security, privacy, data leaks, and having a non-open-source AI app installed locally. What if the tool is hacked? What if my credentials are leaked? What if AI hallucinates and deletes non-recoverable data?

On the other hand, I understand this might be the new industry standard. If yes, I don't think there's a point of resisting change since the train will leave without me.

I would like to see what the perspective of other industry experts is and how they navigate this topic. Are you an early adopter? Or do you prefer to wait until the storm passes?

As i always wanted to help and teach people, I wanted to do my part by teaching, by the way I'm working as a devops enginner.

FYI without money , for the first time I'm gonna do what I wanted to , let me know which topic has to be picked and interested people ping the comments.

I want to start the basics on kubernetes cuz I like kubernetes I want to start with this. Thank you. Not sure if this post will be retained or delayed.

my week in a meme

You add alerts because you want to catch problems early.

CPU spikes. Memory usage. Failed requests. Slow queries. Queue delays.

Eventually, the team starts receiving notifications throughout the day.

Some are important. Most aren't.

And that's when a dangerous habit develops: people start assuming the next alert probably doesn't matter either.

I'm curious how teams deal with this.

Have you ever deliberately removed a large number of alerts to make monitoring more useful?

How do you decide which alerts deserve to interrupt an engineer and which should just be visible on a dashboard?

Hey Everybody!

Does anyone know of any active Discord servers, Slack channels or study groups where beginners can connect, share resources, ask questions and support each other along the way?

Genuine question for anyone on a small team (2–10 engineers) where the people shipping features are also the people getting paged.

What I kept running into: an alert fires, and the alert is the easy part. Then comes 45 minutes of jumping between logs, traces, the error tracker, and the code, trying to work out whether the checkout failure is the problem or just a symptom of a database timeout somewhere else. Then you push a fix, and honestly, half the time you’re not sure it worked. You just notice the errors stopped and hope.

That last part bugs me the most. “I deployed a fix” and “the issue is resolved” aren’t the same thing, but most workflows treat them that way.

Full disclosure: I’ve been building a tool around this called Relia. The idea is that it pulls the relevant evidence together, gives you a probable root cause (and says when it isn’t confident), suggests a fix plan, and then checks production after deploy to see whether the original error actually went away. It can also pass that context to AI coding assistants through MCP, so you’re not copy-pasting stack traces into chat.

I’m not linking it here because I’m more interested in whether my assumptions are right:

1. How long does it typically take you to go from “alert fired” to “I know the actual cause”?
2. Do you explicitly verify fixes after deploying, or just watch for it to recur?
3. If you’ve tried AI debugging tools, where did they fall short? My guess is they confidently make things up when they lack context, but I’d like to hear real experiences.

Happy to share what I’ve learned building it if anyone’s curious. Mostly I want to hear how other people handle this, because I suspect a lot of us are just winging it.

We just published new research on using deception against AI attackers.

In controlled AWS tests, agents took up to 8× longer and cost 23× more per objective. With all nine techniques combined, Kimi, Qwen, DeepSeek and GPT-5.6 Cyber reached zero objectives across 40 runs.

We build a patch management product. Right now it uses Dynatrace to pull health signals from managed assets (things like whether a service or host is healthy before and after a patch is applied).

Before we commit further, I'd like to hear from people who use these tools every day. Which APM or monitoring tool does your team use (Dynatrace, Datadog, New Relic, AppDynamics, Elastic, Grafana/Prometheus, something else)?

I'm most curious about:

- How reliable the health and alerting data is in practice (false positives, gaps, lag)

- How comfortable your team is using it day to day

- How easy it was to integrate with other tools through its API

- Anything you regret or wish you'd known before choosing it

I'm not selling anything. I just want to understand what teams trust so we can support the right tools. Thanks in advance!

honestly i dont get why current multi-agent setups are built like this.

if you connect 4 or 5 mcp servers (github, postgres, filesystem, slack etc) you easily end up with 40-50+ tool schemas. right now the standard pattern in langchain or claude code is dumping all 4,000+ tokens of json schemas into the prompt on every single turn just so the llm can pick which function to run.

you end up paying token costs and waiting ~2 seconds of network latency just for what is basically an L7 routing lookup.

i spent the last few weeks testing if we can just decouple tool discovery completely from the llm using a local cpu vector mesh (all-MiniLM-L6-v2 + chromadb hnsw index) and a split execution daemon.

instead of embedding just the function name (which fails on synonyms), i combined the tool name, description, schema keys, and tags into a single profile vector. when a prompt comes in, it embeds the query on cpu and pulls the top match before ever touching the llm.

ran a cold-cache benchmark across 100,000 synthetic tool endpoints on my local machine (standard cpu, no gpu):

method top-1 acc avg latency p95 latency

---------------------------------------------------------------

naive keyword scan 38.1% 46.41 ms 57.60 ms

bm25 lexical index 40.4% 194.05 ms 247.25 ms

local vector mesh 70.7% 9.56 ms 11.44 ms

in-memory fast path n/a 0.082 ms 0.15 ms

I'm interested in Open Source Contribution and looking for recommendations for projects that are related to DevOps and Platform Engineering.

I'd like my contribution to be relevant to my everyday work.

I'm good with python, go, nodejs and typescript.

This will be my first contribution so, I'm also looking for something that's not so difficult to work on.

I'd appreciate any advice or recommendations

Hi everyone,

I’m a student in the early stages of a DevOps degree, and I have to complete an individual project within 5 months. My lecturer asked for a unique, new topic that is purely DevOps, not system engineering. I searched for a long time without luck, then used Perplexity to explore ideas and found one that my lecturer approved. He told me to start with a literature review and continue from there.

The topic is: I don't want to reveal it here, but I'll let you know when you are willing to help me.

Now that I’m starting, I’m honestly panicking. DevOps is new to me; the degree has only just begun, and many of the tools and terms are unfamiliar. I’m comfortable with practical work, but I’m worried about how much I need to learn in such a short time and whether I can finish on schedule.

I’d really appreciate help with:

1. How should I approach the literature review when I don’t know the terminology yet?
2. What core DevOps concepts and tools should I learn first (CI/CD, containers, IaC, etc.)?
3. Any tips for planning a 5-month project as a beginner?
4. Good free resources, communities, or books you’d recommend?

If you’ve been through something similar, I’d love to hear how you handled it. Thanks so much in advance!

Both the braces and sprintf-js security advisories have been withdrawn, despite previously being marked as GitHub Reviewed.

braces: CVE-2026-93687 / GHSA-vfj7-8cjw-p6xm
sprintf-js: CVE-2026-97058 / GHSA-hp3w-g68c-fv3c

I understand that third-party security risks are a pressing concern. Last week was particularly rough, with incidents and critical vulnerabilities involving LiteLLM, PHP, intermediate CAs, Brotli, and more. Security teams everywhere have been scrambling to assess their exposure and mitigate potential risks.

However, these two widely used npm packages, commonly pulled in through linters, test frameworks, and other development tools, have once again caused unnecessary alarm across the ecosystem.

Both advisories have now been withdrawn, but not before triggering security alerts and forcing developers to investigate issues that turned out not to be practically exploitable.

How much can we trust automated dependency audits when even reviewed security advisories can turn out to be false positives?

More importantly, what should change in how vulnerability reports are vetted, published, and propagated to downstream security tools?

Hey guys, quick question for anyone in the AI or infrastructure space.

I was never really interested in running AI models on my homelab cluster, but this job offer looked too good to pass up.

I have a technical interview coming up soon for an SRE role on High Availability AI clusters, and honestly I have zero clue what to expect or what kind of questions they might throw at me.

The position is completely remote, focusing on monitoring, troubleshooting, and keeping things running via SSH without any hands-on access to physical hardware.

Core stack is mostly standard Linux, Docker, Kubernetes, and networking.

If anyone here works with HA AI infrastructure or went through a similar interview process, what do they actually ask about?

Do they stick to standard Linux and networking troubleshooting, or do they push hard on AI-specific tech like GPU drivers, Infiniband, NCCL, or CUDA failures?

Any heads up or real-world examples would be awesome. Thanks in advance!

Hello community,

I am really stressed about my cka exam. I have bought it but have not prepared yet. My goal is to give exam in december. I have udemy monthly subscription and thrive subscription for this year. I am watching mumshad course on udemy.

I have 4 years of experience as a devops engineer. Done ccna training and comptia security plus. I have a very good command on linux(ubuntu) because my career started with it.

But the uncertain and pressure of work is giving me hard time. I am also works as a consultant so have to give time on that as well. I get exhausted after work. I consumed half of the course with labs on udemy and kodekloud but having no experience of production kubernetes. I understand the concepts but still thinks something is missing. The last topic of admission controllers gave me hard time to consume.

Do you guys know anything like tips and tricks to pass this exam. Anyone studying or prepping for the same can also collaborate. Because it is better to clear the concepts.

Dont ignore this post.

Regards

Worried and stressed person

Edit: thankyou everyone for the kind advices. Maybe I have many sources that is why I overthink it. Because I switch from udemy to thrive to multiple youtube channels. I will stick to 1 course and complete it.

​

Hey everyone! I’m exploring ways to extend an existing internal production health-check bot that monitors systems checks whether the important pages loads or not and sends status updates to Microsoft Teams.

I’m looking for ideas beyond basic health checks, such as proactive alerts, log analysis, incident detection, automated troubleshooting, or AI-powered features.

What useful automations have you built around monitoring bots? Which ones have made the biggest difference in real-world scenarios?

Would love to hear your suggestions and experiences!

Got this email from them this morning. My understanding is you can't fully audit GitHub access through the API, which is how accounts were connected, so it's best to assume the worst. Most connected accounts gave full read and write access, so if true, this constitutes serious IP theft concerns.

I personally don't feel like they have been fully transparent with the breach. This was essentially a footnote and buried under a misleading statement "Review provider activity and audit logs; especially AI keys". It should be flashing and unambiguous "GitHub code is possibly compromised".

Furthmore, a simple, nonchalant "we're sorry" and that's it!? Not "we're deeply sorry that we messed up and we take full responsibility, it won't happen again and we are doing x, y, z for our customers..."

I'm switching away permanently. I highly recommend Fly instead, took 30 mins to migrate. Not sure what to do about to do about the IP theft though? I would expect legal action to come of this, possibly a Class Action Lawsuit regarding IP theft.

With Black October happening and all.

How do you all perform full automated tests after deployment? Using postsync hooks in argocd or use Argo events? Do you use ephemeral or static environments?

Reading the php2shell research, the bit that stuck with me was how AI helped turn longstanding bug into working exploit. The bugs was already there, getting them to work reliably was the difficult part.

That make “hard to exploits” feel like a shaky reason to leave something in the backlog. For people maintain production system, has this changed your patching schedule at all? We are still on php 7.4 and the last upgrade attempt broke half of our cron jobs, so i’m curious how other team handle an urgent patch when the upgrade itself need debugging

source: Wiz’s PHP2Shell research, published october 21

I’ve spent a while working in product/application security, and one thing always bugged me: most supply-chain tools scan dependencies after npm install or pip install has already run — by which point the install scripts have executed. The decision happens too late.

So I built DepGate, an open-source dependency gateway. It’s a single proxy + policy engine that sits in front of npm, PyPI, Maven, Go modules, NuGet, Cargo, Composer and RubyGems. Your devs and CI point their package managers at DepGate instead of the public registry. It fetches the package, checks it against a malware feed and your policies, and either serves it or returns a 403 naming the exact policy and reason.

What it does:

• Known malware never downloads — every request is checked against a synced feed of 200k+ malicious packages before any policy runs.
• Readable policies — block by CVSS/EPSS, license, release age, release type, OpenSSF Scorecard, or package name. One rule per policy, stated in a sentence.
• Monitor mode — run a new policy against real traffic first and see exactly what it would break before you enforce it.
• Dependency-confusion protection — internal names are served only from internal registries, never a public look-alike.
• Decision Ledger — every block is recorded with the reason and who asked, down to the CI job.
• Self-hosted, one Docker image (Postgres + Redis), runs air-gapped, and can sit in front of or behind Artifactory/Nexus.

It’s basically meant to be the open-source answer to the commercial “repository firewall” products, with a lot fewer moving parts. TypeScript end to end, MIT licensed.

Repo (quick start + docs): https://github.com/secmods/depgate

Still early and I’m actively building — would genuinely appreciate feedback, criticism of the approach, or feature requests. Heuristic detection of never-before-seen malware is on the roadmap. Happy to answer anything in the comments.

Hi All,

It has been my experience at a couple of different organizations that almost all engineering teams own their own CI/CD pipelines. So my question is how do you enforce or ensure that these teams are performing SAST or DAST (Snyk, Trivy, IaC, etc) on their pipelines? I read that Github has some capability of enforcement, but that doesn't seem like a sound solution. I am curious to understand this and if there is any good litereature on how mature organizations handle this please point me in the right direction.

This is my own personal research and drive to understand being a cloud security engineer.

TIA

Our QA team has years of jmeters scripts for our core apps, but running our own load generation infra is turning into a huge operational and expensive headache.

Every time we do a high concurrency run or need traffic spread out across different geographic regions, half our sprint is spent provisioning EC2 boxes, setting up slave nodes and dealing with network timeouts.

My team lead wants us to explore managed cloud platforms to take that maintenance off our backs. But part of our team is hesitant about moving everything over. Has anyone moved from a self hosted JMeters to a cloud solution? Does the saving in infra justify the subscription cost? And most importantly, were you able to keep your existing .jmx test assets or you had to rewrite everything from scratch?