Building intelligent systems with LLMs, MCP, and agents—rigorous, production-minded, AI-first
17 Research entries organized in 3 categories:
🟢 Practical Applications - Databricks Testing, AutoTriage Assessment, Healthcare AI Agents, CI/CD Optimization, RAG Testing, MCP Testing
🔵 Academic Research - Hydro-Swarm MoE, I, QA Workforce Transformation, AutoTriage Research, Multi-Agent Orchestration, Monte Carlo Testing, Model Evaluation, LLM Testing
🟡 Frameworks & Tools - Agentic Testing, Automated Patterns, AI Safety
graph TB
Research[AI Testing Research Portfolio]
Research --> Practical[Practical Applications]
Research --> Academic[Academic Research]
Research --> Tools[Frameworks & Tools]
Practical --> Databricks[Databricks Testing<br/>64% Time Reduction]
Practical --> AutoTriage[AutoTriage Assessment<br/>3.2x ROI]
Practical --> Healthcare[Healthcare AI Agents<br/>487% ROI]
Practical --> CICD[CI/CD Optimization<br/>40% Time Saved]
Practical --> RAG[RAG Testing<br/>Applications]
Practical --> MCP[MCP in Testing<br/>Context-Aware]
Academic --> HydroSwarm[Hydro-Swarm MoE<br/>Adaptive AI Testing]
Academic --> IQA[I QA Transformation<br/>Workforce Forecasting]
Academic --> AutoTriageResearch[AutoTriage Research<br/>85% Accuracy]
Academic --> MultiAgent[Multi-Agent Orchestration<br/>80.2% Detection]
Academic --> MonteCarlo[Monte Carlo Testing<br/>POFOD Estimation]
Academic --> Evaluation[Model Evaluation<br/>GPT-4 vs Claude]
Academic --> LLMTest[LLM Testing<br/>Methodologies]
Tools --> Agentic[Agentic Testing<br/>Integration]
Tools --> Patterns[Automated Testing<br/>Patterns]
Tools --> Safety[AI Safety<br/>Metrics]
style Databricks fill:#51cf66,stroke:#2f9e44,color:#000
style AutoTriage fill:#51cf66,stroke:#2f9e44,color:#000
style Healthcare fill:#51cf66,stroke:#2f9e44,color:#000
style CICD fill:#51cf66,stroke:#2f9e44,color:#000
style HydroSwarm fill:#74c0fc,stroke:#1971c2,color:#000
style IQA fill:#74c0fc,stroke:#1971c2,color:#000
style AutoTriageResearch fill:#74c0fc,stroke:#1971c2,color:#000
style MultiAgent fill:#51cf66,stroke:#2f9e44,color:#000
style MonteCarlo fill:#74c0fc,stroke:#1971c2,color:#000
style Evaluation fill:#74c0fc,stroke:#1971c2,color:#000
- 40-Prompt Production Gate — Practical LLM safety release gate for QA leaders.
- Hydro-Swarm MoE — Adaptive AI testing architecture (PARL + IDA-MoE).
- I, QA: Workforce Transformation — QA role and automation forecast (2025-2028).
- Databricks Testing Framework — Unified testing platform with measurable efficiency gains.
- Healthcare AI Agents — Practical case study with ROI and coverage outcomes.
- CI/CD Test Optimization — Monte Carlo approach to faster, risk-aware test selection.
- Multi-Agent Orchestration Framework — architecture trade-offs and statistical validation.
- Monte Carlo Testing Framework — reliability estimation for risk-based testing.
- AI Model Evaluation Framework — GPT/Claude/Gemini comparative evaluation.
View All Research → | Complete Research Index | Notebook Downloads
QA-to-AI Transformation Roadmap — 32-week phased strategy for QA Directors and Engineering Leaders
Impact: 487% ROI • 40-70% efficiency gains • 85%+ automation coverage
For: Teams ready to lead the AI transformation
leadershipstrategychange-managementROI
Preview Framework → | Request Full Access → (Premium)
| Project | Impact | Tech | Links |
|---|---|---|---|
| LLMGuardian | 23% accuracy ↑ • 60% faster • 3 safety violations prevented | JavaScript, AI APIs, RAG, MCP | Demo • Docs |
| Legacy-AI Bridge | 40% faster • 60% fraud ↓ • Zero downtime | Python, Legacy Integration | Framework • Assessment |
| Job Search Automation | 60% time ↓ • 85% match accuracy | Python, Playwright, React | Quick Start • Dashboard |
| AI IDE Collection | 100+ hrs testing • S-Tier rankings | 10 IDEs compared | Comparison • Source |
| Algorithmic Trading | +127% return • 1.67 Sharpe • 64% win rate | Python, pandas, Risk Mgmt | Strategy • Implementation |
LLMGuardian — Production AI Testing Framework
Advanced validation for Large Language Models with RAG, MCP, and safety testing
Impact: 23% accuracy improvement • 60% faster testing • 3 critical safety violations prevented
Tech: JavaScript/Node.js, AI APIs, RAG, MCP
LLM-testing AI-safety RAG MCP production-AI
Live Demo | Documentation | Case Studies
Legacy-AI Bridge — Enterprise Integration
Gradual AI integration for enterprise systems without disruption
Impact: 40% faster processing • 60% fraud reduction • Zero downtime migration
Tech: Python, Legacy System Integration, AI/ML Pipeline
Framework Details | Assessment Tool
Job Search Automation — Career Management
Ethical AI-powered automation for career management
Impact: 60% time reduction • 85% job matching accuracy • Improved application quality
Tech: Python, Playwright, AI/ML, React/TypeScript
Quick Start | Try Dashboard | Documentation
AI IDE Collection — Gotta Code 'Em All
Interactive comparison of 10 AI-powered development environments
Analysis: 100+ hours testing • S-Tier through B-Tier rankings • Real-world performance insights
IDEs: Cursor, Windsurf, Void, Continue.dev, GitHub Copilot, Zed, Replit AI, CodeWhisperer, Tabnine
developer-tools AI-assistants IDE-comparison code-editors
View Comparison | Source Code
Algorithmic Trading — Quantitative Strategy
Systematic quantitative trading with risk management
Performance: +127% total return • 1.67 Sharpe ratio • 64% win rate
Tech: Python, pandas, Statistical Analysis, Risk Management
Strategy Details | Implementation
Production-ready Playwright automation validating this portfolio
8 reliable tests | 4x faster execution | Core Web Vitals monitoring | CI/CD integrated
Stack: Playwright • JavaScript • GitHub Actions
Coverage: Functional • Performance Testing
View Test Coverage & Metrics
| Category | Tests | Key Features |
|---|---|---|
| Functional | 5 | Homepage smoke test, social links, navigation, project links |
| Performance | 3 | Core Web Vitals (LCP, FCP, CLS, TTFB), page load, resource analysis |
Optimizations:
- Page Object Model architecture
- Parallel execution (4 workers locally, 2 in CI)
- Smart retry logic (1 local, 2 in CI)
- Test tagging (@smoke, @performance, @fast, @critical)
- Custom fixtures for reusability
CI/CD:
- Automated GitHub Actions workflow
- HTML report artifacts (30-day retention)
- Video & trace capture on failure
- Badge status in README
Quick Start:
npm install && npx playwright install --with-deps
npm run test:smoke # Fast smoke tests
npm run test:ui # Interactive modeDocumentation: Test Plan • Setup Guide • Test Suite Docs • Quick Reference
Test your skills at distinguishing AI-generated code from human-written code
Can you spot the difference between code written by AI and code written by humans? This interactive game presents real code snippets and challenges you to identify their origin. Learn the subtle patterns that distinguish AI coding style from human creativity and problem-solving approaches.
Features:
- 6 diverse code examples from simple functions to complex implementations
- Real-time scoring and accuracy tracking
- Educational explanations for each code snippet
- Mobile-responsive futuristic design
- No registration required - jump right in!
Challenge yourself: Can you achieve 80%+ accuracy and earn the "AI Code Detective" title?
- Projects Deployed: 5 production systems (including Portfolio Testing Suite)
- Performance Improvement: 23-60% across projects
- Testing Coverage: 85%+ automated validation
- Test Automation: 8 E2E tests, 4x faster execution, Core Web Vitals monitoring
- AI Frameworks: RAG, MCP, LLM testing, safety validation
- Hydro-Swarm MoE - Fluid architecture for adaptive AI testing - PARL + IDA-MoE, AQUA uncertainty-aware routing, bridges agent swarms and Mixture of Experts
- QA Agentic Workflows Guide - Build your own specialized AI agents for daily QA work - Free solutions, Monday-Friday workflows, chat agents
- LLM Safety & Red-Teaming — community hub - Dedicated series for your LinkedIn group - New article ~every 21 days via workflow; maintainer notes
- Ethical AI Frameworks — community hub - Governance and practical ethics series for your second LinkedIn group - New article ~every 25 days via workflow; maintainer notes
- The 40-Prompt Production Gate - LLM safety & red-teaming for QA leaders - First sprint: 40 prompts, eight families, scoring rubric, production gate (practical research)
- State of AI Testing - Living overview of AI testing trends for AI builders - Updated monthly by the Research & Literary Agent
- AI Advancements Q4 2025 - Major AI breakthroughs and their impact on testing and AI systems - GPT-5.2, Gemini 3.0, Agentic AI, Multimodal AI analysis
- QA-to-AI Transformation Roadmap - 🎯 Transform your QA team to AI-first in 6-12 months (487% ROI teaser available, 🔒 Full roadmap - Premium)
- Prompt Engineering Guide - Master effective AI prompting techniques
- AI Workflow Integration - Integrate AI into daily development workflows
- AI-First Principles - Core philosophy and development approach
- AI Adoption Roadmap - Step-by-step guide for teams adopting AI
New to AI-First development? Start here: START HERE Guide
Want to customize this template? See: Customization Guide
Unified autonomous agent system working on this portfolio 24/7
Last Updated: 2026-05-04 11:04:35 UTC
| Component | Status | Last Run | Details |
|---|---|---|---|
| UAA Workflow | 🟢 Success | 2026-05-04 11:04:35 UTC | View Runs |
| CI-Fix Capability | ⚪ Unknown | N/A | View Status |
| Link-Health Capability | 🟢 Success | 2026-05-04 11:04:16 UTC | View Status |
| Security Capability | 🟢 Success | 2026-05-04 11:04:17 UTC | View Status |
- 2026-05-04T11:04:17Z: Security completed successfully
- 2026-05-04T11:04:16Z: Link-Health completed successfully
- 2026-04-27T11:02:54Z: Security completed successfully
- 2026-04-27T11:02:52Z: Link-Health completed successfully
- 2026-04-26T22:36:33Z: Link-Health completed successfully
Dashboard auto-updated by UAA after each run
Status: [ACTIVE] Active | Architecture: Modular, Single Workflow, Multiple Capabilities
| Capability | Status | Purpose | Key Features | Links |
|---|---|---|---|---|
| CI-Fix | [ACTIVE] Active | Auto-fix CI/CD failures | Fixes npm sync, missing deps, creates issues for complex errors | Guide |
| Link-Health | [ACTIVE] Active | Prevent broken links | Weekly link scans, creates PRs with fix reports, alerts on critical links | Guide |
| Security | [ACTIVE] Active | Security monitoring | npm audit, secret detection, auto-fixes moderate issues, critical alerts | Guide |
Unified Workflow: .github/workflows/unified-autonomous-agent.yml
Architecture: Unified Agent Architecture | Agent README
Why Unified Architecture? Single point of maintenance • Shared utilities • Modular design • Easy to extend • Consistent logging
| Agent | Status | Purpose | Links |
|---|---|---|---|
| SEO-MA SEO Monitor Agent |
🔜 Planned | Monitor SEO health | Roadmap |
| PMA Performance Monitor Agent |
🔜 Planned | Track performance | Roadmap |
| DUA Dependency Update Agent |
🔜 Planned | Keep dependencies current | Roadmap |
| CUA Content Update Agent |
🔜 Planned | Maintain content freshness | Roadmap |
| AA Analytics Agent |
🔜 Planned | Generate insights | Roadmap |
Why Autonomous Agents? 24/7 operation • Instant response • Consistent quality • Demonstrates practical AI agentic workflows
| Agent | Status | Purpose | Links |
|---|---|---|---|
| RLA Research & Literary Agent |
✅ Active | Monthly research digest & publish | Guide • State of AI Testing • Workflow |
Runs on the 1st of each month (and manually). Curates llm-discovery/*.md, inserts a new section into State of AI Testing, and commits—so you don’t have to initiate research or publish by hand.
Learn to build your own: QA Agentic Workflows Guide | Full Roadmap
Key paths
├── index.html / analytics.html # Main portfolio UI
├── research/ # Research hub + notebooks
├── docs/ # Guides, papers, architecture docs
├── community/ # Community series hubs + generated articles
├── llm-discovery/ # Weekly discovery data and pages
├── agents/ + scripts/ # Automation logic and helpers
├── .github/workflows/ # CI/CD and publishing pipelines
├── tests/ + playwright.config.js # E2E and performance tests
├── images/ + screenshots/ # Static assets
└── README.md / PROJECTS.md / CONTRIBUTING.md
- Core experience:
index.html,research/,community/,analytics.html - Content & knowledge:
docs/,research/notebooks/,llm-discovery/ - Automation:
.github/workflows/,agents/,scripts/ - Quality:
tests/,TEST_PLAN.md,PLAYWRIGHT_SETUP_GUIDE.md - Project modules:
llm-guardian/,legacy-ai-bridge/,job-search-automation/,ai-ide-comparison/,algorithmic-trading/,qa-prompts/
This portfolio demonstrates AI-First development practices using advanced AI systems:
- Rapid Prototyping: Complete portfolio architecture designed and implemented in 1-2 days instead of 2-3 weeks
- AI-Assisted Development: Leveraged multiple AI systems for code generation, optimization, and rapid iteration
- Human-AI Collaboration: Strategic decisions, domain expertise, and quality control maintained by human developer
- Efficiency Gains: ~10x faster development cycle through intelligent automation and AI pair programming
- Technical Partnership: Advanced AI systems as development accelerators and code generation partners
This project was built using AI-First development practices with:
- Cursor AI Agentic Mode - Advanced code generation and pair programming
- Void IDE - AI-powered development environment and workflow automation
- Claude 4 Sonnet - Architecture planning, documentation, and complex reasoning
- DeepSeek AI - Rapid iteration and optimization support
- DeepSeek Coder - Specialized code generation and technical implementation
Every technique in our guides was used to build this portfolio:
- Complete HTML/CSS generation with AI assistance for rapid iteration
- Advanced AI frameworks (RAG, MCP, LLM testing) implemented with AI assistance
- Production-ready CI/CD pipeline configured with AI guidance
Perfect for: Developers and AI engineers who want to ship faster with AI-first workflows, and teams adopting LLM- and agent-based tooling.
MIT License - feel free to use this template for your own portfolio!
@portfolio{elamcb2025,
address = {USA},
author = {Elena Mereanu},
title = {{AI-First AI Engineering Portfolio}},
url = {https://elamcb.github.io},
linkedin = {https://linkedin.com/in/elenamereanu},
github = {https://github.com/ElaMCB},
year = {2025}
}