I have 23+ years of experience in researching and developing wireless communication networks and computer science and information technology solutions. I have experience in developing and implementing every part of a communication system, from system architecture to radio layer, protocols, applications and services. Recently this also include using Machine Learning and Artificial Intelligence to build "smart" communication systems. I have designed and implemented a number of network and data processing solutions, including GrapeVine and TelHoc Borrego. I know the details of most networking protocols, especially TCP/IP protocols, and implemented many of them in C/C++ and Java, including a complete TCP/IP stack. GrapeVine is a delay tolerant network service using fountain coding that can be used as to transport data over very lossy links close to the Shannon capacity, where traditional protocols like TCP completely fails. I designed the protocol, service and implemented it in C and Java. It operate as a virtual network device, or as a service accessible via APIs.I also developed TelHoc Borrego into a released product. It is a data distribution service built on top of an Information Centric Network (ICN). Uniquely, everything is encrypted, no data can found or accessed without appropriate keys or authorization, achieved via novel homomorphic encryption methods. As a NoSQL database service, with SQL compliant APIs, it can be used as a cloud based DBaaS, and provide Content Distribution Network (CDN) using its internal ICN (NDN) network. It can combine multiple heterogeneous relational databases (SQL) and NoSQL databases into a single system with a single common interface. See https://telhoc.github.io/borregodb/.TelHoc Borrego can also be used as an platform for massive Machine Learning and Multi-Agent Artificial Intelligence solutions. Being able to build ML/AI solutions for encrypted data is a novel and innovative area. My primary focus in this context is creating networked Multi-Agent AI solutions. This is nsignificantly neglated area of AI development, with far greater potential than the hyped up (deservedly so though) field of generative AI. Children are not ML/AI model, they don't learn by looking at billions of images, or eading the entire corpus of Wikipedia or the whole Google database. One or two pictures of a cat and dog, and your kid can tell which is which. Can your AI model be trained on one or two images only, rather one or two million/billion? I don't think so. I think so.