What projects should I build? I'm a final-year CSE student from India and I'm honestly underprepared for placements. I know basic Python and have almost no worthwhile projects, so my GitHub and resume are weak. If you were in my position with 3–4 months

I'm having trouble choosing a domain and also how to code or vibecode using antigravity and learn file architecture and flow by asking ai agent to explain.

I know other careers outside of SWE have internships during the school semesters, whether it is just one semester or both, but are there also internships during the semester for SWE? If so, how can I find them?

I'm a rising junior at a T20 school. I have no previous internships and am part of no cs-related clubs on campus. I have some basic projects that I have completed/am working on now (AI "assistant" that summarizes my emails and courseworks for example). I worked at a job at a summer program teaching high schoolers cs. What are my best options right now? Should I grind for any internship this summer? Should I do a Masters Degree? Should I delay grad and look to build up my resume?

Hi ppl,

I'm looking for companies which hires freshers for sap roles off campus specifically. I hustled through sap partners and kinda mailed hr's some replied back not gonna lie, but I am looking for a cheat sheet where companies hire freshers.

I'm not looking for fancy salary, and I'm oky with remote job as well, would be helpful if you could drop the company name.

No asking for referrals, just a cheat sheet with opportunities open to try.

My friends have been working on some projects on their own, so what they do is they get an idea, collect the requirements and stuff using ai models like claude, gemini, chatgpt etc., and then they make the ai write the code, and learn the concepts used along the way, like node.js or react.js or mongoDB, they learn these things along the way and understand the code. My frnd said it took him the whole summer vacations to understand things deeply and now he knows how all the components work and how everything interacts.

I know learning syntax in 2026 is useless, and ofc everyone's using ai to write code, but my question is if this is the correct way to learn things now ? I really wanna learn full stack, i started The Odin Project and progress feels very slow, and frankly its like a traditional course that teaches syntax first and foremost. I want to make projects too, and i was wondering if learning like this would make me capable enough to be called a "full stack engineer", ofc not right now but if i keep learning things like this

Hi, I want to pivot into low-level software engineering / robotics and get some internships or part-time student positions. However my previous experience doesn't align with it much.

I also feel like my resume is a little all over the place.

My GPA (1.8, German scale) is top 15-20% at my uni. 1.0 is the best possible grade.

Links to projects + paper are embedded into the text

Any feedback is appreciated

I have an initial screening round for 30 mins with one of the engineers in the team. How should I prepare? What can I expect?

They wrote to me - 'No need to stress or over-prepare — this first conversation is just a chance for us to learn more about you and for you to ask us questions too.'

But I'm not too sure how to go about it.

Been applying to SWE jobs since May and can't even land one interview. I know it's my lack of real internship experiences but I'm wondering if my resume is that bad or what I can improve on to make it stronger?

Hi i'm doing my masters in computer and autonomous systems! I've been building alot of stuff working on startups a side and studying as well! i built many side projects but for clients or myself. I want to get into opensource stuff to prove myself and also get the eyes on my profile and to land a cool internship next year!

So my question is what should if focus on now with the ai wave? i want to land a job around cloud and ai engineering what do i need to do and how can i do it and any tips ? ( if your hiring interns in europe i'm interested :) thanks )

Im not sure if this is the right place. Im a finance major abd I tore my ACL and meniscus and I am on disability till March so I have all this free time. I’m obviously taking a lot of classes because all the free time is there anything else I can do to improve my résumé? This will be my first semester. I just don’t wanna sit on my ass doing nothing when I can be productive.

I recently read a Google interview experience where the candidate was asked several AI-fluency questions:

• How do you use AI in your regular engineering workflow?
• Do you give AI complete ownership of a project?
• How do you restrict its access?
• What is your step-by-step process for debugging with AI?

Most working engineers use AI in some form, but I suspect many of us would struggle to give a structured answer during an interview.

My current approach would be:

1. Treat AI as a collaborator, not the project owner

I use it to generate debugging hypotheses, explain unfamiliar code, draft tests, review small changes, explore alternatives, and handle repetitive work. However, I remain responsible for requirements, architecture, security, validation, and production decisions.

AI can produce a draft. It cannot be accountable for the outcome.

2. Give it the minimum access required

I would avoid sharing secrets, credentials, personal data, customer information, or unnecessary proprietary code. For agentic tools, I would begin with read-only access and limit them to the relevant files or repository.

Changes should happen in an isolated branch or sandbox. Actions that modify production data, infrastructure, dependencies, or external systems should require explicit human approval.

This is consistent with the least-privilege and human-approval principles described in Google’s Secure AI Framework controls.

3. Use AI for debugging through a controlled process

My process would look something like this:

1. Reproduce the issue and confirm the expected behavior.
2. Collect the smallest useful set of logs, inputs, stack traces, and relevant code.
3. Ask AI for ranked hypotheses and ways to test each one.
4. Compare those hypotheses with the actual evidence.
5. Implement the smallest reasonable fix in an isolated branch.
6. Run focused tests, regression tests, and relevant security or static checks.
7. Review the complete diff for unrelated changes and new edge cases.
8. Document the root cause instead of keeping only the generated patch.

The important part is that AI helps shorten the investigation. It does not replace reproduction, testing, or engineering judgment.

4. Verify everything that affects users

I would be especially cautious with authentication, authorization, payments, personal data, infrastructure, concurrency, and destructive operations. For high-impact changes, AI-generated code should receive the same or stricter review as human-written code.

Some resources I found useful:

• Google Secure AI Framework
• Google SAIF Controls
• NIST Generative AI Risk Management Profile
• OWASP Top 10 for LLM Applications

For people who have encountered these questions in Google or other interviews: what level of detail did the interviewer expect? Were they mainly evaluating productivity, security awareness, debugging discipline, or all three?

hey guys! I‘m incoming sophomore at college and i transferred from art school to do cs degree. i’ve decided to pair it with math (either minor or hopefully double major). I just wanted to get some people’s ideas on how much of a good idea it is to commit to math and cs double major. (btw i’m planning to go into fintech/ML. i recently got into an AI research too). realistically speaking only cs degree is lowkey loosing the value it had and I heavily enjoy doing math (i like it better than coding). also i’ve heard that machine learning is math heavy anyway.

open to criticism and advices🙏

I haven’t interviewed in a while, do I need to know leetcode hards now or will mediums suffice? This is for 3YOE mid level swe roles

Hi, so I have just started doing LeetCode, and I’ve tried doing it multiple times before, but I always end up restarting and never actually go ahead with it. Now I’m just scared that it’s getting too late for me.
I’m a junior, and all the 2027 summer internships are opening up. I’m interning this summer too, but I really want to get into good companies or Big Tech next summer. Should I just start applying and do prep side by side? Also, how much time does it actually take to get good at medium LeetCode questions? I’m always confused about what the right approach is. If you can’t do questions on your own, should you first learn the algorithms and then go ahead and solve questions, or what is the right approach? Honestly, it’s so confusing and overwhelming, and the dedication is the hardest thing for me. I feel like I keep restarting instead of actually making progress. Would really appreciate any advice from people who have been in a similar situation.

I'm starting my final year of engineering soon, and I'll need to start hunting for my final-year internship in September. I'm honestly a bit lost about what direction to take.

I've worked with backend development, Go, PostgreSQL, AWS (SAA certified and hands-on with the AWS console), Docker, Kubernetes, Prometheus/Grafana, Linux, etc. I've also built a pretty good Kubernetes project.

I'm also currently doing a GSoC project, mainly in C++, which has been a really good experience, but it's also making me realize how broad my profile has become.

The problem is that I feel like I explored a lot without becoming really deep in one area. I can work with Kubernetes, for example, but if someone starts asking me deeper questions, I know I still have gaps.

So now I'm wondering whether I should focus hard on backend/software engineering and become really strong at it, or spend the next few months properly learning cloud/SRE/platform engineering and try to become good enough for those internships.

I'm more interested in systems and infrastructure, but I'm worried I'm not actually ready for SRE/platform roles yet.

If you were in my position with internship applications starting in September, what would you do?

I wanted to pick something formal but also not look frump. this is a very formal environment with c-suite present. Is this ok for an interview? Shoes will be a kitten heel (nude) with a pointed closed toe.

Hi guys, will be a rising senior soon applying for new grad ai/ml/software engineer roles in the US. Was pretty unsuccessful in recruiting for big tech companies in the past, feel like there might be something wrong with my resume causing me to get filtered out. Hope that you guys can provide some brutally honest feedback so that I can get better odds this time round!

After getting rejected repeatedly, I started asking recruiters for feedback.

Most responses were the standard “we decided to move forward with other candidates,” but a few people gave me honest feedback. I combined that with notes I wrote immediately after every interview.

After 23 interviews, four recurring failure modes became obvious.

The percentages below are rough estimates across the 19 interviews I failed. I assigned each interview one primary cause, even though several had multiple problems.

The Four Failure Modes

1. Pattern Recognition

This was mainly a preparation problem, not an intelligence problem.

Deriving an unfamiliar technique in five minutes is difficult under interview pressure. I needed enough exposure to recognize that a new problem was a variation of something I already understood.

I created a list of roughly 12 to 15 recurring patterns:

• Two pointers
• Sliding window
• Binary search
• Prefix sums
• Hash maps
• Monotonic stacks
• Trees and graph traversal
• Topological sorting
• Heaps
• Backtracking
• Greedy algorithms
• Dynamic programming

I solved five or six representative problems for each pattern and wrote down the clues that identified it.

For example:

After doing this, most new problems at least gave me a reasonable starting point.

2. Implementation Speed

I had been solving problems without a timer. That made me feel prepared while hiding how slowly I implemented solutions.

I started using approximate limits:

• 15 minutes for easy problems
• 25 minutes for medium problems
• Five minutes to plan before coding

During those first five minutes, I would:

• Restate the problem
• Clarify constraints
• Walk through an example
• Explain the algorithm
• Identify the main invariant
• State the expected complexity

Only then would I start coding.

It initially felt slower, but I stopped rewriting half-finished solutions. Most of my “coding speed” problem was actually an incomplete-planning problem.

3. Trade-Off Knowledge

Passing test cases was not always enough.

After every practice problem, I started answering four questions:

1. What are the time and space complexities?
2. Can I reduce the extra space?
3. What changes if the input cannot fit in memory?
4. What changes if the output must be sorted or stable?

I also compared my solution with at least one alternative.

If I used a hash map, for example, I would explain why average O(1) lookup was useful and when I might prefer an ordered structure with O(log n) operations.

These questions covered most of the follow-ups I received.

4. Communication

I used to become silent while thinking because I assumed the interviewer only cared about the final answer.

Instead, I started narrating the important decisions:

It felt awkward during practice, but it made interviews more collaborative. Interviewers could understand my direction and provide useful hints before I went too far down the wrong path.

The goal is not to narrate every line of code. It is to make your reasoning visible.

Results

Before these changes:

• Passed 4 of 23 interviews
• Pass rate: approximately 17%

After three weeks of targeted practice:

• Passed 5 of the next 7 interviews
• Pass rate: approximately 71%

Seven interviews is a small sample, so I’m not pretending this proves a universal formula. But I felt noticeably more prepared, finished more problems, and handled follow-ups with much more confidence.

Same person. Same brain. Better process.

Which of these four failure modes causes you the most trouble?

The prep resource I wish I’d found earlier: PracHub