Why Take-Home Projects Are Broken (And What Replaces Them)

The Take-Home Consensus

Take-home projects gained popularity as a reaction to whiteboard interviews. The logic was sound: instead of watching someone implement a binary search tree under pressure, give them a realistic problem, a reasonable deadline, and judge the output. It was more humane, more realistic, and more predictive of on-the-job performance.

Companies like Stripe, Basecamp, and GitLab built their hiring processes around take-homes. Engineering blogs praised them. Candidates preferred them. For a while, they were the best option available.

Four Problems That Were Always There

The Core Issue: Output vs. Process

Every traditional assessment method shares the same fundamental flaw: they evaluate what was produced, not how it was produced.

This distinction did not matter much when humans wrote every line of code. The quality of the output was a reasonable proxy for the quality of the engineering. If someone produced clean, well-tested code, they probably thought carefully about the problem.

That assumption no longer holds. AI has decoupled output quality from process quality. Two engineers can produce identical code through completely different processes:

In a take-home, both engineers submit the same repository. Same code. Same tests. Same result. But Engineer A will handle a production incident at 2am. Engineer B will make it worse. The only way to tell them apart is to observe the process.

What a Process-Based Assessment Looks Like

If output-based evaluation is broken, the replacement needs to capture the engineering process itself. That means observing, in real time, how an engineer approaches a realistic problem.

The key signals are not about what code was written, but about the decisions that led to that code:

• Investigation depth. Did the engineer explore the codebase before asking AI for help? Did they read the relevant files, run the existing tests, understand the architecture? Or did they immediately start prompting?
• Context quality. When they did use AI, what context did they provide? A vague “fix this bug” prompt signals a different skill level than attaching the relevant config file, the failing test, and explaining the expected behavior.
• Verification calibration. Did they verify AI output proportionally to its risk? Accepting a simple rename without testing is efficient. Accepting a complex architectural change without testing is dangerous. The best engineers calibrate their verification to the stakes of each change.
• Recovery behavior. When the first approach failed, what happened? Did they recognize the dead end quickly and pivot? Or did they keep re-prompting the same failing approach? Recovery is one of the strongest signals of engineering experience.

These signals cannot be captured from a submitted zip file. They require continuous observation of the engineering process as it happens.

Making It Work in Practice

Process-based assessment creates a measurement challenge. You cannot ask a human reviewer to watch a 45-minute coding session in real time for every candidate. That does not scale.

The approach that works is telemetry-based scoring. The candidate works in a controlled environment where every meaningful action is captured: file reads, AI prompts, edits, test runs, terminal commands, context switches. This telemetry stream is then scored against structured dimensions with clear rubrics.

At DynaLab, we score across 7 dimensions in 3 tiers, weighted by what research shows predicts production reliability. The highest- weighted dimensions are calibrated trust (25%), context engineering (20%), and problem decomposition (20%) — the three skills that most clearly separate engineers who will thrive with AI from those who will struggle.

Four of the seven dimensions are fully deterministic, computed directly from telemetry signals with zero rater variance. The remaining three use bounded LLM analysis (capped at a 15-point adjustment) to add qualitative evidence. Every score links to specific timestamped events in the session. Nothing is a black box.

The Shift That Matters

The question is no longer “Can this person write good code?” AI has made that question nearly irrelevant. The questions that matter now are:

• Can they identify what needs to be done before asking AI to do it?
• Can they provide the right context to get useful AI output on the first attempt?
• Can they tell when AI is wrong?
• Can they recover when their approach fails?
• Do they verify proportionally to risk?

These are process questions. They cannot be answered by looking at a finished artifact. They require observing the work as it happens.