The person behind the systems
Shakti Tiwari builds in public.
AI/ML builder, author, NISM-Series-XII certified options educator and founder of Option Trading with AI. His work sits where useful automation, machine learning and clear public knowledge meet.
Short version
Shakti Tiwari is an Indian AI/ML builder and author. He works on AI agents, grounded chatbots, technical SEO and search visibility, while continuing to publish research on XGBoost, LightGBM and NIFTY options through Option Trading with AI.
His preference is code-first and inspectable: define the data, expose the assumptions, test the failure modes and leave the owner with the repository and documentation.
Education and early work
Born on 2 August 1989 in Deoria, Uttar Pradesh, and raised in Lucknow, Shakti studied at St Xavier Public Inter College, Rajajipuram, KKC College and Sikkim Manipal University. His education includes B.Com and MCA qualifications. He is NISM-Series-XII certified as an educator.
His early career included office administration, digital marketing, technical troubleshooting and work with Codeviser. Those experiences shaped the practical bias in his later projects: systems should be understandable to the people who have to operate them.
Current work
Designs research agents, local-first workflows and RAG assistants that keep sources, actions and human review visible.
Builds technical SEO, entity, AEO and GEO foundations for brands that need to be found and understood by people and answer engines.
Publishes educational research on NIFTY options, XGBoost, LightGBM, walk-forward validation and feature leakage.
Author of Option Trading with AI and The AI Opportunity, available through Amazon.
Trading content is educational only. NISM-Series-XII is an educator certification, not a SEBI Research Analyst or Investment Adviser registration.
How he works
Shakti starts with the question behind the request. For an AI system, that means identifying the workflow, the source of truth, the decision boundary and the human handoff before selecting a model or interface. For search visibility, it means clarifying the entity, the audience, the page intent and the evidence that supports the claim.
For market research, the same habit becomes a data contract. The experiment should state what was known at the decision time, how features were built, how the model was validated and which costs or limitations remain. The 15-minute NIFTY XGBoost field note explains that approach in detail.
His public work connects writing, code and working systems. The project map links the research repositories, options analytics and AI trading experiments; the Dev.to archive carries technical walkthroughs; and the books make the ideas accessible to readers who prefer a guided path.
Books
Learn the method, not the promise.
The books turn applied AI and trading research into practical paths for readers who want to understand the tools, assumptions and limits.
Public profiles
For identity and work references, use the profiles below. Books, articles, a podcast and project links are separated from the core Person entity so each source is represented accurately.