Hiring a developer in Brazil no longer tells a tech lead much about how that person will perform inside a small engineering team. Over the last two years, coding agents have made polished take-home submissions easier to produce, while a weak review process still lets defects through. My position is that the old “prove you can build it” interview should become a “prove you can inspect and change it” exercise.
Polished take-homes have become weaker hiring evidence
A take-home project used to offer a rough signal about implementation skill: the candidate had to turn a brief into working code, often with limited assistance. That inference is less reliable now because tools such as GitHub Copilot, Cursor and Claude Code can draft substantial changes from a prompt. GitHub announced its Copilot coding agent in May 2025, a vendor-published milestone that makes “the candidate submitted a coherent pull request” a weaker distinction than it was in 2024.
This does not make AI use a hiring problem. Your team may already use those tools. The problem is attributing the result: a clean TypeScript component or passing Vitest suite does not show whether the candidate identified a missing requirement, rejected a plausible but wrong suggestion, or understood the trade-off they shipped. In a 15–30-person engineering organisation, those decisions matter because a new hire will soon review and modify code that colleagues depend on.
I would not ban AI assistance in the assessment, because a ban is difficult to verify and would test an artificial workflow. I would ask candidates to disclose what they used, then assess the decisions visible in the final diff. A useful prompt is: “Which generated suggestion did you decline, and what would have gone wrong if you had accepted it?” A candidate who used no assistant can answer the same question about an approach they considered and rejected.
Use Hire Software Developers in Brazil: Nearshoring Guide to settle location and coordination questions before the technical assessment. Do not let a favourable time-zone match stand in for evidence of engineering judgment. São Paulo remains on UTC−3 year-round, while New York changes between UTC−4 and UTC−5; the overlap shifts with the US clock, and neither offset predicts the quality of a code review.
The outdated advice is to rank candidates by the completeness of an unsupervised take-home. Completeness still matters, but it deserves less weight because the tool-assisted path to a complete-looking answer has changed. Ask instead whether the candidate can explain the diff, name what remains uncertain, and identify a test that would change their mind.
A flawed pull request tests judgment better than a blank repository
For a tech lead with a small review budget, I would make the core exercise a seeded pull request in a representative, non-production repository. Give the candidate a short product request, a passing CI run and a diff containing two plausible choices that deserve challenge. For example, a pagination change might pass Vitest while silently dropping the final page when the result count is an exact multiple of the page size. A Playwright test may also assert only that the first page renders. Neither failure requires a trick question; both reveal whether the reviewer looks beyond green checks.
The candidate should first review the pull request in writing. Ask them to distinguish a blocking defect from a preference, suggest the smallest useful test, and state what they would ask the author before approving. Then give them permission to amend one part of the diff. This sequence matters because editing first lets a strong coder bypass the harder task of explaining risk to another engineer.
Set a 75-minute limit as a trial setting, not a universal measure of ability: enough time for review and one focused change, but short enough to avoid rewarding an elaborate rewrite. Tell candidates that partial completion is acceptable if their review clearly identifies the next step. Keep the same seeded defect and rubric for everyone in a hiring round, since changing the hidden problem between candidates makes the scores difficult to compare.
There are two defensible options. A blank-repository build wins when the role genuinely requires turning ambiguous requirements into a new service or interface; it costs candidates more unpaid time and gives you a noisier attribution problem. A seeded pull-request review wins when the hire will join an existing codebase and spend much of the week changing others’ work; it costs your team time to maintain a realistic fixture and score responses consistently. For most hires into a 15–30-person organisation, I would choose the review, because integration judgment becomes useful sooner than greenfield speed.
Do not score candidates on whether they recite your preferred architecture. Score the observable response: did they locate the defect, explain its consequence, propose a proportionate fix, and communicate uncertainty? A reviewer who asks for a targeted regression test may offer more value than one who rewrites the module, because the smaller change is easier for the team to verify.
Reproducibility belongs in the assessment, not in a promise on the CV
A candidate’s submission should run from a clean checkout with the repository’s lockfile. That is a narrower and more useful requirement than “knows our stack,” because it exposes missing setup steps without making familiarity with your exact internal tooling a prerequisite. For a Node.js exercise, state the runtime and package manager explicitly; do not leave candidates to guess whether your local installation matches CI. Node.js 24, TypeScript, npm and GitHub Actions are concrete choices, but the assessment should use the versions your fixture actually runs.
The following Bash script is suitable for a small npm-based exercise with a committed package-lock.json and a working test script. Run it from a clean checkout; the optional build must either leave tracked files unchanged or write to ignored output.
#!/usr/bin/env bash set -euo pipefail start=$(date +%s) npm ci npm test npm run build --if-present elapsed=$(($(date +%s) - start)) printf 'Reproduction took %s seconds\n' "$elapsed" test -z "$(git status --porcelain)"
The printed time is a local measurement, not a benchmark for candidate speed. The useful result is whether the checkout reproduces cleanly and whether a build unexpectedly modifies tracked files. If the script fails, fix the fixture before sending it to another candidate; otherwise you risk measuring their tolerance for your setup errors.
Keep the evaluation close to the job. If engineers maintain browser behaviour, a small Playwright regression can be appropriate. If they primarily review API changes, a Vitest or Jest assertion around a boundary condition may be enough. Do not add Kubernetes, Terraform and a cloud deployment merely to make the exercise look substantial; each extra system introduces environmental failures that obscure the review decision you meant to observe.
Use Hire Top Developers in Brazil for Your Next Software Project as a sourcing reference, then make the repository exercise carry the technical decision. A shortlist describes whom to meet. It cannot tell you whether a candidate will notice that a passing test misses the final page of results.
The interview should verify ownership of the diff, not demand a performance
Follow the written exercise with a conversation about decisions already visible in the submission. Ask the candidate to open one review comment, describe the evidence behind it, and say what reply from the author would change their position. This is more revealing than asking for a live rewrite, because engineering review often requires revising an opinion after new information arrives.
Use a scorecard agreed before interviews begin. One practical allocation to test is 40% for identifying consequential issues, 30% for the proposed verification, and 30% for the clarity of the review conversation. Those weights are values to tune after a hiring round, not claims that every team’s work divides that way. Record short evidence beside each score—such as the test the candidate requested—so two interviewers can discuss a disagreement rather than average impressions.
Keep AI disclosure separate from the quality score. “Used Claude Code to generate the initial test, then corrected its boundary condition” is evidence of a review decision; “used Claude Code” alone is not. Likewise, an unaided solution is not automatically stronger if its author cannot explain why the test would fail before the fix. The changed tool environment makes that distinction necessary.
Finally, arrange the conversation at an hour the candidate could reasonably sustain after joining. A single convenient interview slot can hide a poor recurring overlap, especially when US daylight saving time changes. Ask the candidate to mark the hours they can normally collaborate, then compare that answer with the team’s actual review and pairing schedule. This tests a working constraint without turning nationality or location into a proxy for reliability.
Replace one assessment before replacing your whole hiring process
Take the next vacant role and convert its current take-home into one seeded pull-request review. Write the scoring rubric before sending the exercise, run the clean-checkout script yourself, and have two engineers independently score a trial response. If they disagree, repair the prompt or rubric first. That small test will tell you more about your hiring signal than another polished assignment will.