ProReview - Catch AI Before It Wrecks Production<br>Start reviewing
AI command review drillWould you run this AI command?<br>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.<br>Try the 60-second challenge<br>FreeNo loginAnonymous
proreview-cli<br>bash -c "set -euo pipefail<br>MOUNT=/var/lib/postgresql<br>DEV=$(python3 -c 'import os, subprocess; src=subprocess.check_output(["findmnt","-n","-o","SOURCE","/var/lib/postgresql"], text=True).strip(); print(os.path.realpath(src))')<br>sudo umount -l "$MOUNT" || true<br>sudo wipefs -a "$DEV" && sudo mkfs.ext4 -F "$DEV"<br>sudo mount "$DEV" "$MOUNT""Approve this command?<br>Y/N
50+ Challenges<br>5 Tracks<br>0 AI hints<br>min Per review
How it worksFrom prompt to production review<br>A focused drill designed around the AI-generated artifacts engineers ship every day.<br>01Pick your tracks<br>Choose from Kubernetes, cloud infrastructure, SQL, CI/CD, and backend security surfaces.
02Judge the output<br>Read the AI-generated artifact cold. Decide whether it is safe before seeing any explanation.
03Flag risky lines<br>Select the exact commands, config, SQL, or code segments that would break production.
04Learn the reveal<br>Compare your verdict with the expert answer key and learn the pattern for the next review.
FeaturesBuilt for engineers who ship<br>No fluff. Just the loop you need to sharpen AI code review instincts.
Realistic AI output<br>Review generated commands, diffs, configs, migrations, and API code shaped like work engineers actually ship.
Blind judgment<br>Commit to safe or unsafe before any hints appear. No LLM hand-holding, spoilers, or answer-first training.
Segment-level scoring<br>Select the risky lines, not just the verdict. Get credit for the exact production hazard you caught.
Expert reveals<br>Compare your call with a curated breakdown of the failure mode, blast radius, and safer review outcome.
Five danger-zone tracks<br>Practice Kubernetes ops, cloud infrastructure, data migrations, CI/CD, and security-sensitive backend code.
Shareable results<br>Finish a run with a score and summary you can share after catching what AI almost shipped.
TracksFive surfaces. Fifty ways to fail.<br>Each track covers the exact artifact types that break production.<br>TRACK 01Kubernetes Ops# maintenance.sh<br>kubectl get nodes<br>kubectl cordon worker-node-03<br>kubectl drain worker-node-03 \<br>--ignore-daemonsets \<br>--force \<br>--delete-local-data<br>kubectl delete node worker-node-03<br>12 challengesHARD<br>TRACK 02Database & SQL# billing-cleanup.sql<br>psql -h db.internal \<br>-d billing_prod \<br>-c 'BEGIN;'<br>DELETE FROM invoices<br>WHERE status = draft;<br>DELETE FROM invoice_items;<br>COMMIT;'<br>10 challengesMED<br>TRACK 03CI / CD# deploy.yml<br>- name: Update image<br>run: |<br>DEPLOY_CMD="kubectl set image<br>deployment/${{ github.event.inputs.service }}<br>..."<br>eval $DEPLOY_CMD<br>11 challengesHARD<br>TRACK 04Infrastructure# backend.tf<br>terraform {<br>backend "s3" {<br>bucket = "company-terraform-state"<br>key = "app/terraform.tfstate"<br>region = "us-east-1"<br>9 challengesMED<br>TRACK 05Python & APIs# exports.py<br>@router.get("/exports/download")<br>async def download_export(<br>filename: str,<br>admin=Depends(require_admin)<br>):<br>file_path = f"/var/exports/{filename}"<br>return FileResponse(file_path)<br>10 challengesMED
Ready to level up?Your next production incident is already written<br>Join engineers who review one challenge a day and catch the bugs before deploy time.<br>Start reviewingBrowse tracks