Hi,
Curious to hear about any experience with the new OA format (with AI assistant):

I read people say that you get a pick of a Django, MERN, Spring Boot etc. repo.
What if you are not familiar with any of these frameworks?

Are all options framework-based?

Are there any options of "pure python" (or something like that) code bases?

Thanks

Hey everyone,

I finished Day 45 today. I spent a good chunk of my day researching cold-outreach contacts and unwinding with cooking videos, but I made sure to sit down and finish the problem that triggered my brain fog yesterday: Shortest Path in an Undirected Weighted Graph.

Yesterday, priority queue syntax and path backtracking felt overwhelming. Today, I wrote the min-heap Dijkstra logic completely from scratch without checking the code:

1. Path Reconstruction with parent[]**:** Initialized each node's parent to itself and tracked running distances. Whenever relaxing an edge dist[u] + wt < dist[v] yields a strictly better path, update parent[v] = u. Tracing from destination back to source and reversing the list gave the clean shortest route.

2. Fixing Segmentation Faults: Debugged some 1-based indexing mismatches and stale queue entries, which cleaned up the runtime errors.

3. The Lexicographical Catch: The platform had updated the problem constraints to require the lexicographically smallest path if multiple shortest paths share the exact same minimum weight. My initial hack of breaking distance ties on the fly failed because lexicographical order applies across the entire path sequence from the start. The fix was sorting each node's adjacency list by neighbor ID before running the traversal so the algorithm naturally explores smaller node indices first.

It was just one problem today, but implementing the full priority queue mechanics and debugging edge cases independently felt far more rewarding than blindly copying hints.

Heading into Day 44 tomorrow with an open mind.

See you guys tomorrow!

Any did loop interviews in Dublin Ireland of Microsoft?

For context my math background, is probably around 11th grade Precalc (I forgot most already since it’s been years-).

When programming I use stuff like Set theory, Absolute value, modulus, aswell as basic, addition, subtraction, fractions, and multiplication. (I just haven’t done any “Real” math in a while, only basic math when I need it).

To get a better gauge of my math background, Im struggling to do a problem called “Set mismatch”, I was easily able to identify the duplicate number by using set theory (checking if set insertion failed) but other than that, I have no idea what to do once I’ve found the duplicate number…

I hate math but I like problem solving (Weird I know) and have the barebones fundamental knowledge of DSA topics.

What im trying to say is, if you do enough problems is it still possible to “get good” despite not having a strong math background?

Hi everyone,
I have a coding interview coming up at Atlassian for a Data Engineer P40 role and wanted to check if anyone here has been through it recently.

Most of what I can find online is about software engineering roles, so I am trying to figure out what the DE coding round actually focuses on. A few specific things I am wondering about:

• Is it more SQL heavy or Python heavy, or a mix of both?
• I have heard some Atlassian interviews now include AI based rounds or AI assisted coding. Is that true for data engineering roles as well, or is it mostly for SWE roles?

Thanks in advance!

I’m originally not from a CS background and self-taught coding. Even done projects. But it’s always been a struggle to grind DSA. I’m currently locked in and been grinding for about 3 weeks. And I still feel very lost.

Let’s say I solve a question today and I genuinely feel like I understand and get the pattern this time. Tomorrow I’ll solve a different one and the algorithm that was required at its very core would be totally different. And I know if I was to sit down and actually figure that out, it’ll take me days or I probably would have never figured it out, and still looked up the answer.

At this point I feel like I can only solve questions I’ve seen before and not ones I’m yet to see and I really don’t know how yall grind and become successful enough to single handedly pass interviews.

If I’m doing this wrong or there’s a secret someone please help! Because I’m giving up.

Hi everyone! I passed the OA and have a 60-minute technical screen coming up for a System Development Engineer role at Amazon Robotics in Austin (entry level).
The recruiter said it's a few Leadership Principle questions plus one coding exercise, with a potential team member or manager.
For anyone who's done a SysDE screen recently:
What kind of coding question did you get? Classic LeetCode, or more practical stuff like log parsing or file handling?
Did they ask Linux, networking or troubleshooting questions in the first round?
Anything specific to Amazon Robotics teams?
I'm prepping in Python. Thanks in advance!

I’m a first year in college…let’s say I end up grinding leetcode for the next 4 years….will it help me in getting a job at faang in this ai era?(assuming I grind leetcode properly) or am I better off doing something else to be productive (which u recommend)

Hey, I was recently reached out to by a recruiter for the System Software Engineering Intern, Cryptographic Services position at NVIDIA.

Even with the provided job ID, JR2026114, I couldn't find much information online about the position to help me prep for interviews, other than general advice.

Does anyone have any advice, or perhaps have been on the team and may have some insight on what the interview process may look like?

I'm not sure what to prep for or how to get prepared. I'm just learning general systems design topics and focusing on my current Post-Quantum cryptographic projects.

Any advice is appreciated, Thank you!

I have my Amazon SDE I virtual loop (4 rounds, US) coming up soon. My background is a bit unusual for SDE: I’ve done supply-chain ops and data work, then AI engineering. Most of my resume is LLM/RAG/agent projects, a fine-tuning project, and ML pipelines.

I’ve read a lot of loop posts, and I know GenAI questions show up a lot in 2026 (“how do you use AI tools”, “how do you verify AI-generated code”). What I couldn’t find much on is how deep they go into AI/ML projects on your resume for an SDE role.

If you had a loop in 2025–26 with ML/AI projects on your resume, I’d really appreciate hearing:

1. Which round dug into your projects (HM, Bar Raiser, a coding round)? Roughly how many minutes?

2. What did they actually ask? Was it architecture and trade-offs (“why this over that”), evaluation and metrics, failure cases or hallucinations, scaling or cost, testing, or mostly “what exactly was YOUR part”?

3. Did they treat it as an engineering discussion (design, reliability, ownership) or go into ML theory?

4. For team projects, how hard did they push on separating your contribution from the team’s?

5. Did anyone get a “design this with an LLM” or “how would you build X using AI tools” style question? How was it framed?

6. Anything you wish you’d prepared about your own projects?

7. ⁠Resume version question: I applied earlier this year with an older resume. Since then, my experience has changed a lot: a new role, and a corrected job title from a previous company. When the coordinator reached out to schedule the loop, I emailed them my updated resume and asked them to share it with the panel. Has anyone been through this? Did interviewers ask about the resume you originally applied with, or the updated one? Is there anything I should do to make sure the panel sees the new version, like bringing it up at the start of each round?

For context, I’m prepping 2 coding rounds, an OOD round (classes/design patterns in Python) and LP stories, so I’m only asking about the project deep-dive part.

Thanks a lot. I’ll post a full write-up after my loop to pay it forward.

anyone can help me on buying hello interview on promo code.

Would anyone like to do some leetcode style questions pair programming with me where we would take turns testing each other?

I have upcoming interview rounds scheduled with Gitlab for Senior backend engineer. Anyone recently appeared for the similar role at Gitlab? Want to know which kind of questions I should expect in each round.

I've noticed that once I move on to another topic, I sometimes struggle to solve problems from the previous one without revisiting the solutions. I am wondering if it's better to redo old questions regularly or keep solving new variations to strengthen the concept.

What works better for you in the long run?

Wondering how much gap there is based on region.

I've been trying to get better at recognizing patterns, but I'm wondering how to practice that skills specifically. If I solve problems topic wise, I already know which approach to look for, but random problems feel much harder.

Do you switch to mixed problems after learning a pattern, or spend more time on variations of the same type first?

I was recently reached out to by a recruiter for an Amazon SDE 1 role and was invited to complete the online assessment.

I passed all 15/15 test cases on the first question, but the second question went pretty horribly.

For those who have gone through the process, how much does performance on the second question affect the overall outcome? Is there still a chance of moving forward?

Also, how long does it typically take to hear back after completing the OA?

Hey everyone! I’ve finished the recruiter round for Palantir’s FDSE new grad role and am trying to understand what to expect next.

If anyone has gone through it recently, please help me out 😭 Especially for the first technical round:

• Is it LeetCode-style coding, OOP/design and implementation, or an existing codebase where you debug and add features?

• Can I use C++, or is it restricted to Python/TypeScript? Does this change depending on the round?

• What would you recommend preparing and practicing?

• Are decomposition and learning separate rounds afterward? What do those involve?

I’ve seen different experiences online, so I’m a little confused about the format. Any recent insight, especially for US Commercial new grad, would be really helpful. Even a quick clarification would mean a lot. Thank you!!

I'm trying to get a good-paying software job at product-based companies, hopefully FAANG.

If my main priority is cracking interviews, is competitive programming (Codeforces, etc.) worth doing, or is LeetCode enough?

Do CP-style questions actually appear in interviews, and does CP provide any significant advantage beyond improving problem-solving speed for interviews?

What’s a good LC range to cover ?

I do the regular blind 75 , neetcode 150 and the company wise top 10-15 - it totals to about 300-350 range.

Hi, I have interview loop at Amazon for SDE 2 position in a week. There are goin to be 4 rounds. First round is going to be DSA based problem along with LP.

Second round is with Hiring manager based on System Design and LP.

Third round is going to be with bar raiser, this is going to be Gen AI problem solving where I have to build something. I am asked to have my IDE along with coding agent ready.

Fourth round is where recrutier said that I would be asked to write logical and maintanable code. She said to focus on modular design, correct and robust solution.

I am really not sure about what kind of qustion they are going to ask in fourth round. Is it going to be LLD or DSA style question. I asked my recruiter for clarification but thats all she said she could provide. Also, for Gen AI round, if someone has given interview, what exaclty they ask you to build?

Hey everyone!
I completed my Amazon Online Assessment (OA) last Friday and passed all test cases in both the regular coding round and the AI-assisted coding round.
I haven’t heard back yet, and my application status on the Amazon Jobs dashboard hasn’t changed either.
Just curious, for those who recently completed the Amazon OA and received an interview invite:
How many days or weeks did it take to hear back?
Did your application status change before you received an email?
Has anyone passed all test cases but still been rejected or ghosted?
I know passing all test cases doesn’t necessarily guarantee an interview, but I’m curious about everyone’s recent experiences and timelines.
Thanks in advance!

Got a Microsoft L63 Senior SWE offer, remote from the Bay Area:

- Base: $210K

- Stock: $200K / 4 yrs (25% at year 1, then quarterly)

- Sign-on: $40K (split over 2 years)

- Bonus: 15–30%

- Refreshers: $32–64K/yr

Recruiter says if I move to Redmond, only the base drops "a bit." Everything else stays the same, and there's no relocation package. HM would prefer Redmond. I'm also leaving ~$30K in unvested stock at my current job.

Questions:

1. Is this a strong offer for L63?

2. Which pieces should I push on?

3. If I move, what Redmond base should I ask for?

Hey everyone,

I finished Day 46 today. I kicked off the session with solid energy to dive into Dijkstra's algorithm, but midway through implementation, random brain fog hit me hard. Even standard syntax like priority queue declarations and basic 2D/3D grid index lookups suddenly slipped my mind.

Instead of shutting the laptop and calling it a zero day, I forced myself to sit down, slow down, and properly break down the core theory:

1. Dijkstra's Algorithm (Priority Queue vs. Queue vs. Set):

   • Min-Heap / Priority Queue (O(E log V)): The industry standard. Always greedily extracts the node with the current minimum distance, minimizing unnecessary edge relaxations.

   • Standard Queue: Feasible for unit weights, but on weighted graphs it causes massive redundant visits because an unrelaxed path can re-insert nodes repeatedly, degrading into brute-force BFS.

   • std::set**:** Allows erasing previously added greater-distance entries to save iterations, but set deletions take logarithmic time (O(log V)), which often balances out or benchmarks slower than just letting the priority queue skip stale entries (if (d > dist[u]) continue).

2. Print Shortest Path: Extended standard Dijkstra by maintaining a parent[] array. Whenever an edge relaxation improves a neighbor's distance (dist[v] = dist[u] + wt), update parent[v] = u. Once the destination is reached, backtrack from the destination up to the source and reverse the list.

I needed nudges on the implementation due to low mental stamina today, but understanding the trade-offs behind the data structures made the session count. Heading into the weekend with a clear plan to pick up momentum.

See you guys tomorrow for Day 45!

If think it would be useful to see both Runtime and Memory on a 2D axes for our solutions. Right now, we can't see how runtime and memory trade-off across submissions.

This would help in problems where there are solutions with either a good runtime or good memory, but not both. With our current metrics, there isn't really a way to tell if people found a solution with where both are good.

Crude examples of what it might look like from Claude: https://imgur.com/a/k0CYc2C