Review AI-generated bash, kubectl, and shell one-liners before they touch production. Make the call, flag the risky segment, and see what you missed.
bash -c "set -euo pipefail MOUNT=/var/lib/postgresql DEV=$(python3 -c 'import os, subprocess; src=subprocess.check_output(["findmnt","-n","-o","SOURCE","/var/lib/postgresql"], text=True).strip(); print(os.path.realpath(src))') sudo umount -l "$MOUNT" || true sudo wipefs -a "$DEV" && sudo mkfs.ext4 -F "$DEV" sudo mount "$DEV" "$MOUNT""
A focused drill designed around the AI-generated artifacts engineers ship every day.
Choose from Kubernetes, cloud infrastructure, SQL, CI/CD, and backend security surfaces.
Read the AI-generated artifact cold. Decide whether it is safe before seeing any explanation.
Select the exact commands, config, SQL, or code segments that would break production.
Compare your verdict with the expert answer key and learn the pattern for the next review.
No fluff. Just the loop you need to sharpen AI code review instincts.
Review generated commands, diffs, configs, migrations, and API code shaped like work engineers actually ship.
Commit to safe or unsafe before any hints appear. No LLM hand-holding, spoilers, or answer-first training.
Select the risky lines, not just the verdict. Get credit for the exact production hazard you caught.
Compare your call with a curated breakdown of the failure mode, blast radius, and safer review outcome.
Practice Kubernetes ops, cloud infrastructure, data migrations, CI/CD, and security-sensitive backend code.
Finish a run with a score and summary you can share after catching what AI almost shipped.
Each track covers the exact artifact types that break production.
# maintenance.sh kubectl get nodes kubectl cordon worker-node-03 kubectl drain worker-node-03 \ --ignore-daemonsets \ --force \ --delete-local-data kubectl delete node worker-node-03
# billing-cleanup.sql psql -h db.internal \ -d billing_prod \ -c 'BEGIN;' DELETE FROM invoices WHERE status = draft; DELETE FROM invoice_items; COMMIT;'
# deploy.yml - name: Update image run: | DEPLOY_CMD="kubectl set image deployment/${{ github.event.inputs.service }} ..." eval $DEPLOY_CMD
# backend.tf terraform { backend "s3" { bucket = "company-terraform-state" key = "app/terraform.tfstate" region = "us-east-1" } }
# exports.py @router.get("/exports/download") async def download_export( filename: str, admin=Depends(require_admin) ): file_path = f"/var/exports/{filename}" return FileResponse(file_path)
Join engineers who review one challenge a day and catch the bugs before deploy time.