For a few years, the take-home assignment looked like it was dying. It's slow, candidates hate the unpaid hours, and companies worried about solutions leaking online. The live coding round won out — faster, cheaper to run, easier to standardize.
Then AI showed up in the room. A candidate with a code-completion tool open can pass most 45-minute live rounds without the interviewer learning much beyond "can operate a code-completion tool under time pressure." That's not nothing, but it's not what most engineering roles actually need. So the round that's hardest to shortcut with AI — the one graded slowly, on a real artifact, over days instead of minutes — is coming back. Several 2026 loops are re-adding a take-home or project round specifically because the live round stopped producing signal.
What's actually being graded
The mistake most candidates make is treating a take-home like a bigger LeetCode problem — get it working, submit, move on. That's not what earns the pass. A take-home is graded almost entirely on the decisions visible around the working code, not on the fact that it runs.
Notice that "does it work" isn't on that list. Working code is the floor, not the differentiator — almost every submission that gets reviewed already works. The pass/fail line sits in the decisions around it.
Three ways candidates lose the round
Given a 4-6 hour spec, some candidates spend 15 hours adding auth, a CI pipeline, and a settings page nobody asked for. It reads as either poor time judgment or an inability to say no to scope creep — both are exactly what a reviewer is trying to screen out before handing someone a real production codebase.
No README, no comments on why a particular approach was chosen, just a working repo. The reviewer is left to reverse-engineer your reasoning from the diff, and most won't bother — they'll assume there wasn't any. A few sentences on what you'd do differently with more time often matters more than the extra hours it would take to actually do it.
The demo works when you run it the intended way. It breaks the moment a reviewer passes an empty list, a malformed input, or hits it twice in a row. Reviewers try exactly these things on purpose — a take-home with zero tests, or tests that only cover the success case, signals the same gap a live interviewer is probing for when they ask "what if this input is empty?"
A worked example
Say the spec is: "Build a small API that lets users upload a CSV and returns aggregate stats on one column."
Weak submission: A single main.py that parses the CSV, computes a mean, and returns it. It works on the sample file. There's no README. There's no handling for a missing column, a non-numeric column, or an empty file — it just throws an unhandled exception.
Strong submission: The same core logic, but split into a parsing layer and an aggregation layer with a couple of unit tests covering a missing column and a non-numeric value. A three-paragraph README explains the approach, states explicitly that authentication and multi-file upload were left out as out of scope for a 4-hour spec, and notes what the author would add first with more time (streaming for large files). Nothing here took dramatically longer to build — it took different priorities.
The strong version isn't more code. It's the same code with the decisions made visible instead of left implicit.
How to prepare
You can't cram for a take-home the way you drill algorithm patterns, but you can rehearse the muscle that actually gets graded: stopping to scope and explain before you start building.
Before opening an editor on a practice take-home, spend five minutes writing down what you will and won't build, out loud or on paper — the same discipline the strong submission above used. Then, when you're done, write the tradeoffs section first, before you're tired and tempted to skip it. Treat the README as part of the deliverable, not an afterthought bolted on at the end.
The skill a take-home tests — narrating scope, tradeoffs, and what you deliberately left out — is also exactly what live technical interviews grade you on when they ask you to walk through your solution. Rubduck's spoken-first mock interviews put you in front of an AI interviewer that pushes back on your reasoning in real time, so the habit of justifying decisions out loud gets built before it's graded for real. Try a free session →
The candidates who pass a take-home round aren't the ones who worked the most hours. They're the ones who treated the scoping decision as the actual interview, and the code as just the evidence.
— Team Rubduck