Personal research journal exploring agentic AI systems, multi-agent architectures, and human-AI interaction
Maintained by: Rohit Sharma | AI Systems Builder
This repository is my public research journal documenting:
- 📖 Paper reviews and summaries
- 🧪 Experiment logs and findings
- 💡 Technical learnings from production AI
- 🤔 Research questions and hypotheses
- 🔗 Connections between theory and practice
Why public?
Learning in public, sharing insights, and inviting feedback from the AI research community.
ai-research-notes/ ├── papers/ # Paper reviews and summaries ├── experiments/ # Experiment logs and results ├── learnings/ # Technical insights from production ├── questions/ # Open research questions ├── reading-list.md # Papers to read └── concepts/ # Deep dives on specific concepts
- Coming soon — Starting with agentic systems papers
- Coming soon — Multi-agent coordination patterns
- Coming soon — Production AI insights
Research Questions:
- How do we coordinate multiple AI agents effectively in production?
- What trust mechanisms enable AI-generated content for high-stakes decisions?
- How can we build agents that learn from human feedback efficiently?
Practical Context:
Building production AI systems at WebBrandify with real users and business constraints.
Production AI Experience:
- 2+ years building multi-agent systems
- OpenClaw Agent — Autonomous lead generation
- n8n orchestration with Claude API
- Real business operations (not demos)
Research Projects:
- AI Citation Analyzer — Trust layers for answer engines
- Exploring verification systems for AI-generated information
Academic Interest:
- Applying for Perplexity AI Research Residency
- Focus: Agentic systems + human-AI interaction
- Bringing production insights to research
What I document:
- ✅ Papers that challenge my assumptions
- ✅ Experiments (successes AND failures)
- ✅ Patterns from production systems
- ✅ Questions without answers yet
- ✅ Connections between different ideas
What I don't:
- ❌ Surface-level summaries
- ❌ Only successful experiments
- ❌ Isolated facts without context
Format:
- Clear, concise markdown
- Always link to original sources
- Code snippets when relevant
- Real production examples
For Researchers:
- See how production AI connects to theory
- Find practical implementation insights
- Discover papers worth reading
For Builders:
- Learn from production experiments
- Understand real-world challenges
- Get architectural patterns
For Recruiters/Collaborators:
- Understand my thinking process
- See continuous learning commitment
- Gauge research depth
Currently Reading:
- List will be populated soon
Queue:
- Papers on agentic systems
- Multi-agent coordination research
- Human-AI interaction studies
- Trust and verification in AI
Key Sources:
- arXiv (AI, ML, NLP sections)
- AI conference proceedings (NeurIPS, ICML, ICLR)
- Industry research blogs (Anthropic, OpenAI, DeepMind)
- Perplexity research blog
Open to:
- Paper discussions and debates
- Experiment suggestions
- Research collaboration
- Feedback on notes and analysis
Reach out:
- GitHub Issues for discussions
- Email: rohit@webbrandify.com
- LinkedIn: Rohit Sharma
Production Systems:
- WebBrandify AI Systems — Multi-agent production architecture
- OpenClaw Agent — Autonomous lead generation agent
- n8n AI Workflows — Reusable workflow templates
Research Projects:
- AI Citation Analyzer — Trust verification for AI answers
MIT License — Notes are public for learning and collaboration
Inspired by:
- Researchers who share their work openly
- The AI community's culture of open learning
- Production AI challenges that drive research questions
📚 Learning in Public | 🔬 Research & Practice | 🤝 Open Collaboration
"Bridging the gap between AI research and production systems"