Generated with AI assistance (Claude Code) | Reviewed: May 2026 Period: May 15 – June 12, 2026
Prompt used:
"We're building Loopless — a freelancer-enterprise matching platform. Our North Star Metric is Weekly Active Matched Pairs. We're in the final 4 weeks of a 6-sprint course project. Generate 3 OKRs for the final sprint that balance technical completion, product quality, and measurable outcomes. Each KR should be binary or numeric. Justify each KR."
Tool: Claude Code (claude-sonnet-4-6)
Review: Team reviewed all generated OKRs. Accepted 3/3 with minor KR wording edits. Time saved: ~30 min vs writing from scratch.
Objective: Ship a fully integrated backend + frontend with no critical blockers
| # | Key Result | Measure | Status |
|---|---|---|---|
| KR1 | All 10 API endpoint groups return correct responses under authenticated test load | Binary: all pass | In progress |
| KR2 | Frontend → backend happy path works for: signup, onboarding, discover, message, standup | Binary: all 5 flows pass | In progress |
| KR3 | Zero CRITICAL/HIGH Trivy CVEs in Docker images pushed to GHCR | Binary: 0 CVEs | Done ✅ |
| KR4 | docker compose up starts all 16 services with 0 manual intervention |
Binary: passes | Done ✅ |
Why: Course rubric requires working end-to-end integration demo. Non-working flows = score deduction.
Objective: Prove Loopless meets production-grade engineering standards
| # | Key Result | Measure | Status |
|---|---|---|---|
| KR1 | Lighthouse performance score ≥ 90 on all 8 audited routes | Numeric | Done ✅ (avg 99) |
| KR2 | Lighthouse accessibility score ≥ 90 on all routes | Numeric | Done ✅ (avg 96) |
| KR3 | Integration test suite: ≥ 5 tests green with real Testcontainers DB | Numeric | Done ✅ (8 tests) |
| KR4 | API p95 latency < 200ms on /api/v1/matching (k6 test, 50 VUs) |
Numeric | In progress |
Why: Rubric grades on code quality + test coverage + performance evidence. Quantified KRs make grading objective.
Objective: Systematically document and improve AI-assisted engineering practices
| # | Key Result | Measure | Status |
|---|---|---|---|
| KR1 | ai-log.md has ≥ 12 entries across M1–M6 with Grade A/B distribution ≥ 70% | Numeric | In progress |
| KR2 | At least 1 AI debugging session documented with measurable time-saved | Binary | Done ✅ |
| KR3 | At least 1 AI PR review documented with specific issues found + fixed | Binary | Done ✅ |
| KR4 | Final AI retrospective: team identifies top 3 lessons + 1 process change for next project | Binary | Pending (Jun 5) |
Why: Course explicitly grades AI usage quality (not just quantity). Grade A entries require measurable outcomes. KR4 creates reusable process artifact for the team.
| Grade | Target | Meaning |
|---|---|---|
| A | ≥ 60% | Worked out of box |
| B | ≤ 30% | Minor edits needed |
| C/D | ≤ 10% | Significant rework |
Current distribution: A: 70%, B: 30%, C/D: 0% (M1–M4 entries)
- Mid-sprint check-in: May 22 — assess KR1.1, KR2.4, KR3.1
- Sprint close: June 8 — final OKR scoring before demo