Numbers
Scale of the ecosystem: Jarvis 34K LOC, 9 microservices, 15 pipelines, 7h vs 16h performance audit, 2 years CDAT.
- AI_DEVS 4 Builders - missions + secrets - 1st term
- 25/25 + 30/30
- 25 of 25 main missions + 30 of 30 secret missions unlocked, first term completion 2026. Certificate by BRAVE (Gospodarczyk, Mrugalski, Chrobok).
- CDAT Pattern battle-tested
- 18mo
- 9 production systems using CDAT over 2 years. 3000+ tests across the portfolio, zero maintenance nightmare.
- Lines of code in Jarvis
- 34K
- Private multi-agent QA platform - 9 microservices, event-driven, real-time UI.
- Microservices in Jarvis
- 9
- jarvis-core, jarvis-dispatcher, jarvis-orchestrator, jarvis-figma, audit-api, n8n, mcp-server, ui, cli.
- Hours logged (March 2026)
- 279h
- Norm is 160h. 279h documents actual work scale - B2B consulting plus AI tooling builds plus content creation.
- Performance audit - AI vs billable
- 7h vs 16h
- 7h with AI multi-agent pipeline vs 16h billable vs team-week classical. 5 agents parallel - bundle, runtime, API, SSR, assets.
- Production pipelines
- 15
- QA Full Cycle, Batch QA, Figma CSS/Visual Diff, WCAG 7-agent, Performance 5-agent, Git Audit, Timesheet Sync, View Pipeline, CI/CD.
- Production projects shipped
- 9
- MAF, DEMO BI, Claude VSCode Controller, K6 Dashboard, CDAT Pattern, Confluence Headers Manager, MAF E2E + API tests, Portfolio v1.
- QA tasks processed in 2-3 days
- 100-200
- Batch QA workflow w Jarvis. Context before LLM - deterministic Figma + Jira + staging pipeline, human-in-the-loop approval queue.