Rohan Paul

Rohan’s Bytes separates tutorials, paper explanations, machine-learning case studies and interview material from its daily AI newsletter. Paul’s public profile describes a previous investment-banking background at Deutsche. His professional output now combines explanations of underlying software mechanisms with commentary on new AI systems.[1][2]

Paul’s MCP tutorial explains a host, client and server arrangement for connecting an AI application to external capabilities. Its Python example exposes loan-eligibility and interest-calculation functions on a server, then starts a client session that discovers and calls them. The useful separation is between the model choosing a tool and the server implementing the operation, so each application does not need its own custom integration for every service.[3]

His comparison of vector and traditional databases distinguishes meaning-based retrieval from structured filtering. Vector search finds documents whose numerical representations are similar to a query, while a relational database can enforce exact conditions on stored fields. The article describes hybrid designs that use similarity ranking alongside metadata filters, or retain original documents in a separate store. That distinction helps a developer avoid treating semantic similarity as an exact database predicate.[4]