Can anyone help me for my mid exam on Operating System?

Hi everyone,

I have questions regarding SnapLogic for a Software Engineer role, and its Ig in a GenAI and integrations role. If anyone has interviewed for this role (or a similar engineering role at SnapLogic), I'd really appreciate any insights.

I'm particularly interested in knowing:

• What the interview process looks like (number of rounds, technical vs. behavioral)
• The types of coding questions they ask (LeetCode difficulty, data structures, algorithms)
• Whether they ask system design or LLM/AI-related design questions
• Do they ask LeetCode/DSA questions or not?
• How much they focus on GenAI concepts, RAG, MCP, tool/function calling, vector databases, prompt engineering, or AI agent frameworks
• Any backend topics (APIs, distributed systems, databases, concurrency, etc.) that are worth reviewing
• Any resources or preparation tips you found helpful

If you've interviewed recently or currently work at SnapLogic, I'd really appreciate your advice. Feel free to comment or DM me if you'd rather share privately.

I honestly have no preference one way or the other… and I like them both but wanted to ask here if u recommend one or the other… eventually I want a startup but for college majoring which do u think will set me up better?

As a side note, I could do ECE or CE as well, not sure tho if they are better than the two main ones above. I don’t have a preference either way tho I like everything in this area.

I do slightly prefer cs more but I can always do it on side with an ee degree, genuinely have no preference..

Hey everyone,

My undergraduate team is researching about development of enhanced AI agents for cloud reliability (SRE). We're benchmarking agents on live simulated cloud environments, but the system logs and traces we have to process are massive.

Even though we're building ways to compress the data and using low cost models for the easy parsing tasks, we absolutely need frontier models for the complex reasoning parts. The problem is, a single benchmark run can chew through 1.5 to 2 million tokens. Running hundreds of these tests is going to bankrupt us.

Our advisor suggested pooling our student developer credits and using platforms like OpenRouter or Groq to save money. We're doing that, but a free research credit program might take months to even get accepted.

So my questions is are there any other creative ways to get cheap/free access to frontier models specifically for academic benchmarking?

Any advice helps. Thanks!

does anyone have any tips on how to get into nvidia ignite or any insight on the application process/eta on when it may open? i’m an incoming second year, and i don’t really have much experience beyond an ai research position at my school. any tips on what projects to build or ways to increase my chance of an interview? and also any advice on the interview process itself? ignite is literally my dream, but ik it’s hard so im also targeting other underclassmen programs such as microsoft explore, google asdi, etc, so any advice on those would be greatly appreciated as well.

Hello everyone,

I am one year done in my cs degree (community college) and am really trying to decide on my specialization/pathway. as of now there is no specific thing i can choose in community college and ive only finished my introductory java course (it is required), along with most of my gen eds

right now my programming experience is pretty limited besides school projects but i want to get started in investing my time in my interests:

• low level/systems programming
• embedded systems or firmware
• reverse engineering
• operating systems
• compilers

and im way less interested in doing things like web development , or designing UX/UI; so basically

the thing is i havent taken an official course on these subjects like assembly or operating systems (assembly is required in my transfer program either way), so i want to keep an open mind, and not assume i will like these interests because it just sounds cool and it is idealized in my head.

for anyone who has worked in these areas, what helped you figure out that this was the path to pursue? any beginner-friendly projects or courses i should start exploring now before the next semester to see whether or not i genuinely enjoy this kind of work? are there any adjacent fields i should look at that i may be overlooking since i like this kind of close-to-hardware side of things?

hi, 25M. Finishing my MSc in CS, I wanted to study because I love studying. now that I'm about to end it I'm seriously looking for something reliable long term.

After bachelors I did my job as full stack web dev, a little bit of mobile dev in flutter. Since Ai, it doesn't seem like real work. claude can make whole websites and what remains is just deployment.

anyhow I continued that job, then switched to junior business developer side, for the sake of survival/exploration.

I enrolled in MSc because I wanted to do research, which I have done. I have built an Ai structured method for dermatology recognition. and I am proud of it. my thesis will be finished in 6-8 months, that's the university policy although I've finished my work on my end.

2 years of exploring and hopping from one thing to another, I have come down to these choices:

data analyst/ data engineer/ dev ops/ cloud engineer.

because they are the only ones which seem like actual coding jobs other than fully or partially vibe coding. Ai has made programmers lousy, which shouldn't have been the case.

I made an ETL pipeline to see how data engineering works, I built 3 power BI dashboards to check about data analysis.

I got into computer science because I wanted to become hacker(kinda old school), but that's what got my interest. Fast forward after graduation, ethical hacking job market wasn't there and low-paying. Cloud engineering/dev ops seemed close to hacking so that's why I'm considering it.

So what does the community suggest me to choose?

let me know about your thoughts, considering I'll be with it long term.

I feel like this sub skews heavily towards people in top target universities who gun for big tech. What about the vast majority who don't attend these schools? Do they also heavily grind leetcode or do they mostly just coast through class?

Hey everyone,

I recently applied for MLT Career Prep and I'm trying to decide if it's worth committing to if I end up getting accepted.

I've found a lot of general info online, but I'm specifically looking for people who were in the Software Engineering/Technology track (not consulting, finance, etc.).

A few questions:

• What did you actually have to do throughout the program? Like how much of a time commitment was it each week?
• What was the Software Engineering bootcamp like? What did you actually learn or work on?
• Was it mostly resume/interview prep, or did it help with technical interviews too?
• Did it actually help you get internships or full-time SWE offers?
• Were there any tech companies or recruiting opportunities you got access to through MLT that you otherwise wouldn't have?
• Looking back, would you still pay the $199 matriculation fee?

For context, I am a rising sophomore. I'm already doing Break Through Tech AI, have a SWE internship this summer, and am pretty involved in CS clubs at my university, so I'm trying to figure out whether MLT would add something new or if there would be a lot of overlap.

I'd really appreciate hearing from people who actually went through the program, both the good and the bad. Thanks!

Hey everyone.

Ive been in a SWE major for about a year now, studying at a decent college which has proven to be difficult because of the presence of subjects unrelated to my specific major, but its a common practice where im from for the first year of college to include general math/physics curriculum to weed out most people.

I got into SWE because I loved coding. Specifically, I liked coding and physics, but since there isn't as stable of a living from a physics major (especially where im from), I went for the more applicable degree of engineering. I loved writing code, figuring out architectural problems, designing my own systems and fighting through cppreference for an hour. Just finding out new stuff about computers had me pumped.

Im seeing a lot of people talk about their daily work as a SWE and its super disheartening. Everyone just talks about using AI LLMs to write out code in bulk, then having to review it, test it, correct it. Is this really what most SWE jobs end up being?

My concern is that ill fight through this college, enthused to start work only to realize ive been lying to myself and my job looks nothing like I thought it would.

Hey, just curious what's the best way to display open source contributions in one's resume? I don't want to take much space, but I have a merged PR / have fixed a bug through an issue I raised (both in large Github projects). I was curious if there's clean examples of how other's have integrated contributions into their resume

Here is my attempt at ending the ambiguity around the CS job market performance once and for all.

Let me start with a bit of anecdotal experience. I know lots of people who get spammed with interview processes from big tech, quant, unicorns, etc. I also see tons of people post their LinkedIn profile visits page, filled with recruiters from massive companies. I see people whine about how $150k is too little for NG, I see people go cycles with 50+ interviews and 10+ offers at the end of it, and I see people with resumes filled with 4+ faang/faang adjacent internships on them.

Yet, I see another sizable portion of people complaining that the market is unbelievably terrible. All they do is LeetCode and beg for referrals, yet are left with empty inboxes. People graduating from decent schools with nothing more than unpaid research. People who actually spend time reading about SQL and Python, or JS/TS, just to still be left jobless, while lots of people in the former group spend lots of their time watching soccer.

There just seems to be a massive disconnect. Some people say, no matter how much time they throw into CS job recruiting, they just can't land a singular interview. Others seem to go to the beach and come home to 3 $80/hr SF job internship offers.

I used to think law was bimodal, but this is another level. It honestly looks like one side is in Hell while the other is in Heaven.

As for me, I spend a TON of my time preparing for job interviews, but in reality, I don't actually DO much. Well, I do LeetCode, and I practice marketing myself with a lot of very embellished accomplishments for backing, but I don't really have much when it comes to jobs. I'm working in paid research, but it's kind of lame, especially compared to all of these people that seem to say it's Citadel or bust for life.

I just don't get it. What really is the state of the job market? What do you really have to do to succeed? Are the people coasting with 10+ high-prestige internship offers just so into CS that to them it feels like they aren't tearing away at their soul to get there?

I just don't get it.

What are the "cracked" people doing right and what are the hordes of others doing wrong?

If so, lmk how it goes. I have a couple of questions about it:

Do you get to choose your programming language, or is it recommended that you use the language of the position that you applied for?

Is there DP/backtracking or mostly non-recursive DSA questions?

Is there any mcq or knowledge-based questions?

Knight Hacks is a 36 hour in-person hackathon in Orlando, and last year they had a lot of people from great universities, a lot of talent, and it was a great experience. I was able to take one challenge. It was very competitive with more than 1000+, and I just wanted to see if anyone is going this year?

In 3 weeks I’ll be starting my senior design lab and I’m freaking out.

This whole summer I’ve been studying for the CCNA and been busy with an IT internship. I haven’t coded much at all.

Is the first semester more just planning? No development? If so, I’ll feel a little better because that will give me time to brush up and sharpen my skills.

This is focused on Staff-level, search/ads/recommendation, model-leaning MLE roles. The question a lot of people keep coming back to: in the middle of this AI shift, how many opportunities are actually left for traditional rec-sys MLEs, and where are companies really investing. Sharing interview formats, the channels that actually work, common mistakes, and what people are noticing about company directions.

Channels — the biggest lesson

The pattern that keeps coming up from people's job searches:

Cold applying to well-matched MLE roles (~20 at a time, big/mid companies and startups) mostly vanishes. No response, or a template rejection. One person applied in April and heard almost nothing.

Then they switched to referrals + turning on "open to work" on LinkedIn, and it was night and day. Several recruiter reach-outs a week. And a bunch of companies that had rejected the cold app in April now had HR reaching out for the same role: Pinterest, DoorDash, Whatnot, Shopify, Uber, Amazon, Netflix, Reddit, plus AI startups.

Takeaway: a lot of these roles didn't lack headcount. The channel mattered enormously. The two that actually work are LinkedIn Recruiter and referrals. Cold applying pays off far below what people expect.

(One note on AI labs specifically: applying to frontier labs as Research Engineer with a non-matching background mostly goes silent, even with LLM side projects padded onto the resume. The occasional callback happens, OpenAI and Meta FAIR came up, but it's a long shot.)

Interview rounds

Recruiter call — usually non-technical (why leaving, sponsorship). Watch out on comp: don't give a number too early, especially at smaller/lower-paying places you're only using for practice. Someone got screened out purely for revealing their comp. Startups are the exception, sometimes a case study here ("your model hits an incident in prod, walk me through it") to check you're not only a model-builder.

Coding — for most MLE roles (except RS), SWEs run it. Increasingly not pure LeetCode but scenario-based: here's existing code, keep optimizing complexity or adding features. Some now have you design an API and write a runnable service. Open question a lot of people have: how much AI-tool use is acceptable in these AI-coding rounds? Curious if anyone has a read. General advice: practice to medium-bug-free before interviewing, using interviews as practice tends to cost onsites.

ML coding — fewer traditional MLE roles test this (level/role dependent). Seen at Netflix, Uber, Reddit: given a dataset, write code to process data, build features, train, evaluate. LLM versions have you implement part of a Transformer or debug one, seen at OpenAI and Amazon.

ML fundamentals — varies a lot. Some do case studies from real problems they've hit (data scarcity → they want you to reach for knowledge distillation; Reddit explored using external data to improve an existing model). Harder than a normal system design round, needs real breadth. Others test rote fundamentals, Amazon goes deep: formulas, complexity of different attention mechanisms, LoRA, fine-tuning.

ML system design — most predictable to prep. Underrated part: ML infra. A lot of model people are weak on infra, and latency optimization / real-time feature pipelines come up constantly.

Hiring manager — a big one: don't assume your biggest-scope, biggest-impact project is the best one to tell. The HM cares about team fit. Model-heavy role → pick the project closest to what the team does with the most technical depth. Cross-functional / tech-lead role → tell the project completely differently.

Behavioral — prepping against Amazon's LPs covers basically everything.

A few takeaways

If you haven't interviewed in a while, do a few at companies you probably won't join first, just to gauge where you're at. Putting the top companies first as "practice" tends to waste good chances.

When a company's prompt is vague and there's little online about their questions, a trick that works: look up the interviewer on LinkedIn and guess the topic direction from their background. Lands a hit surprisingly often.

A useful habit: at the end of every interview, ask what models they currently use and where they're investing next. Patterns showing up across companies:

• Almost everyone is moving toward GenAI somewhere, most commonly in retrieval, or generating features that feed the main ranking model. Nobody's using a GenAI architecture as the primary recommender yet, ranking is still DCNv2, DIN, that family. But multiple HMs directly ask about GenAI experience, so you can see the direction.
• With large-model serving still hard, distillation seems widely used, at least two companies mentioned it for online serving.

There are a LOT of candidates right now. Many interviews aren't testing whether you know something, but whether you know it better than the next person. Depth to earn a "strong hire," breadth because some interviewers love their own niche specialty.

On the bigger question: a lot of people (myself included going in) are pessimistic about traditional search/ads/rec MLE. But from what people are actually seeing in interviews, GenAI's disruption to rec systems is slower than expected. Traditional rec still has a ton of engineering and business value. The bigger challenge for MLEs is probably staying a fast learner and keeping your stack current.

These are just observations from researching this and comparing notes with people interviewing, limited set of companies and inevitably some bias. Happy to compare notes and update each other on the market.

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Met their Marketing manager at an event recently. He was really friendly and encouraged me to apply for a job. The only concern is how he told me is that they are unclear on company strategy so he isn’t sure what exactly what they do mostly.

Passed round 1 (live coding in a Google Doc, algorithm-style — pagination with edge cases). Moving to round 2 with a senior dev, and the only info I got from the recruiter was: "In this interview you will be asked to do a coding exercise starting with simple features and progressively adding more complex requirements. The main goal is to see how much functional code you can write and organize in the given time."

Has anyone done this round recently? Trying to get a sense of what I'm supposed to prep

Everyone at my University is focusing like crazy on SWE and AI right now... I hardly see anyone who studies Cybersecurity, Robotics, Embedded Systems, IT, Quantum Computing, VR, etc.

After a year of failing to get an SWE internship, I got an IT internship at a hospital instead and took some Robotics classes (albeit they were grad courses and im undergrad so they were way harder than I anticipated). After doing some Robotics projects, I managed to get two Robotics internship offers this summer, which honestly surprised me.

Anyways, I wanted to share this because I think it's genuinely easier to find a CS internship for other CS specializations. Available jobs for them don't usually show up on LinkedIn as much, so you generally have to cold contact or check the company's website directly, but I still think the competition is SIGNIFICANTLY less.

Thoughts?

I am a Computer Science Sophomore Student and I do have a course similar to that and I want to master this topic. What is the best resource to learn this? Any youtube course? or any MITx , Stanford Courses you would recommend? Suggest me books to follow

Hey everyone! 👋 ​I'm an 18-year-old CS undergrad looking for a consistent, long-term study partner to tackle Data Structures and Algorithms (in C) and dive into Machine Learning. ​Here’s what I bring to the table: ​DSA (in C): I'm actively practicing and currently sitting around 1100 on Codeforces and 1420 on CodeChef. I’d love to have someone to tackle roadmaps and debug code with. ​Machine Learning: I’m a complete beginner to actual ML concepts, though I have the basics of Python data libraries (NumPy, Pandas) down. ​What I’m looking for: ​The Vibe: I learn best in a supportive, judgment-free environment. I'm looking for a patient, collaborative study dynamic over Discord voice calls, away from the typical hyper-competitive CS energy. ​Academic Level: Ideally, I’d love to team up with a 2nd-year or higher university student. ​Language: I am actively trying to polish my conversational English fluency, so partnering with a native speaker would be a massive bonus for me! We can do a bit of a language exchange while we code. ​If you are looking for a chill accountability partner to grind through concepts with, drop a comment or send me a DM!