Hiring managers: What do you hate about take-home assignments?
Interview Experience
Everyone has opinions about take-home projects -- some applicants hate them, some prefer them to live coding exercises. I personally don't mind them. But with the sheer number of online testing soluti
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Everyone has opinions about take-home projects -- some applicants hate them, some prefer them to live coding exercises. I personally don't mind them. But with the sheer number of online testing solutions available today, it seems like take-home assignments are still relatively low-tech: * I've seen many take-home assessments sitting in public GitHub repos. The candidate solutions are also posted to public repos, meaning that an unscrupulous candidate could simply copy another candidate's solution. * Even if you keep the repos private, an engineer will need to create new repos for each candidate. Not to mention the time spent cloning candidate solutions and evaluating them. * Nearly every application uses a database. But the attempts I've seen at replicating a database in take-homes aren't optimal, and spinning up (and tearing down) a cloud database for technical assessments is time consuming to say the least. * Not to mention all of the back and forth emails between recruiters, hiring managers and candidates. I once had a recruiter overlook my email after I had completed an assessment. Sometimes, devs are busy and delay evaluating candidate solutions, or neglect them altogether. There are alternative to take-homes, depending on the candidate, but assuming that they're relevant to the skills used on the job and aren't too time consuming, they're probably the least worst option in many cases. What problems do you encounter when administering and evaluating take-home assessments to candidates? How could the process be better or easier?
About This Question
This is a candidate experience report from a github interview for a swe role during the oa round reported in 2025.
It covers the following topics: Sql .
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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.