Is this type of take-home assignment becoming the norm?
Question Details
I recently got contacted by a recruiter for a Founding Engineer role at an AI-for-real-estate company. They already have 4 engineers and 2 co-founders. Even before I got the chance to get an intro cha
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I recently got contacted by a recruiter for a Founding Engineer role at an AI-for-real-estate company. They already have 4 engineers and 2 co-founders. Even before I got the chance to get an intro chat with anyone on the team, they sent me this take-home assignment: >Information about a real estate property is often scattered, inconsistent, or incomplete, making it hard for buyers to see the comprehensive picture before purchasing a home. We want a feature that turns this landscape into a clear, reliable brief so people can make confident property decisions. Your task is to design and implement this feature end-to-end. >What to Deliver: - A GitHub repo link with your code and frequent, clear commits. - A short design note (markdown in the repo in README.md) explaining your approach, trade-offs, and what you’d do with more time. >You are welcome to use any tools you’d normally rely on IDEs like Cursor or Windsurf, AI-assisted coding, web search, API docs, or hosted AI services. We encourage you to use whatever stack or workflow helps you demonstrate your design and implementation skills best. >We’re less concerned about which exact APIs or frameworks you choose and more interested in how you structure the problem, make design decisions, and communicate trade-offs. What really struck me is that this assignment was supposed to be done in only 2 hours (checked by the GitHub commit timestamps). The combination of the short amount of time, the open-ended aspect of the problem definition, and the lack of possibility to ask questions to the interviewer caught me off-guard to be honest. I ended up writing a structured document with my analysis of the problem and each pros and cons for different parts of it, but I left it at that. Since they asked for a public GitHub link (which I didn't provide because my current employer doesn't need to know I'm interviewing), I was later able to find two other candidate's public GitHub repos for the same interview question. They both did a serious attempt at building an end-to-end web app, but both of them used simplified mock data instead of real API connections, and one of them didn't really address the "scattered, inconsistent, or incomplete" part of the problem. But the fact that they both delivered a decent app in 2 hours makes me wonder how much I should practice my "vibe-coding" skills if this type of interview question becomes the norm? I'd love to hear what you think!
About This Question
This is a reported interview question from a github interview for a swe role during the take home round reported in 2025.
It covers the following topics: Networking, Backtracking, Stack, Stack Queue .
Difficulty rating: Hard
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About Github Interview Reports
This question was reported by a candidate who interviewed at Github. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at Github are the higher-signal extractions to take from this report.
For broader preparation context, the Github interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Github reports consistently are the ones worth investing in; one-off niche problems are not.
During Your Github Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Github reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.