I design systems that convert scattered knowledge, research, technical experience, and creative ideas into structured intelligence, reusable assets, products, content, and automation.
I am building a personal and public Knowledge-to-Money Operating System that connects three worlds:
Knowledge Base OS → capture, normalize, connect, and retrieve knowledge
AI Delivery OS → turn ideas into requirements, architecture, backlog, tests, and releases
Knowledge-to-Money OS → convert research into GitHub assets, LinkedIn, YouTube, courses, templates, and products
My goal is to work like an AI-native IT delivery company operated by one person: structured, version-controlled, reusable, and always evolving.
- Data Engineering, ETL, SQL, Python, Redshift-style analytics, and API/data integration patterns
- AI-assisted delivery workflows for requirements, BRD, architecture, testing, release, and support
- Knowledge management using markdown, GitHub, Obsidian-style thinking, normalized data models, and graph relationships
- Agentic workflows using ChatGPT, Claude, Gemini, Grok, Copilot, Cursor, Hermes-style agents, and automation tools
- Research-to-content systems for LinkedIn, YouTube, GitHub, presentations, and future digital products
- Spiritual-tech and symbolic intelligence research, including karma, patterns, synchronicity, and AI-assisted reflection
| System | Purpose | Status |
|---|---|---|
| Knowledge Base OS | Capture and structure everything I research, build, learn, and observe | Building |
| AI Delivery OS | Replicate IT service-company delivery: idea → BRD → architecture → sprint → test → release | Building |
| Knowledge-to-Money OS | Convert private knowledge into public authority, products, courses, and consulting assets | Designing |
| GitHub Research Governance Agent | Discover high-value public repos, extract patterns, and convert them into reusable knowledge atoms | Designing |
Private source of truth:
- sourav-knowledge-to-money-os
Public / planned showcase repos:
- knowledge-atom-schema
- ai-native-data-engineer-playbook
- ai-native-delivery-os
- research-to-content-factory
- hermes-agent-integration-lab
Raw idea
↓
Structured requirement
↓
Architecture and data model
↓
Reusable templates and prompts
↓
Working prototype or knowledge asset
↓
Public explanation, GitHub repo, video, course, or product
Data: SQL, Python, ETL/ELT, Redshift-style warehousing, analytics workflows
AI: ChatGPT, Claude, Gemini, Grok, Copilot, Cursor, agentic workflows
Knowledge: GitHub, Markdown, Obsidian-style vaults, Notion, NotebookLM, Zotero
Content: Canva, Gamma, Napkin AI, CapCut, PowerPoint, LinkedIn, YouTube
Delivery: Agile, BRD, user stories, testing, release notes, support handover
Build a world-class, AI-native knowledge and delivery system that helps me capture what I learn, structure it like a data engineer, explain it like a teacher, ship it like an IT delivery lead, and monetize it like a product builder.
- Capture raw knowledge first; structure later.
- Convert every insight into a reusable atom.
- Keep private knowledge private; publish only sanitized, useful patterns.
- Treat GitHub as a living knowledge infrastructure, not just a code dump.
- Build in public where safe; build privately where depth is required.
- Make every repo useful to a beginner, credible to an expert, and reusable by an AI agent.