Calculus and much of all mathematics works with big problems that are solved with multiple simple problems, which together form a solution. Functional programming does something very close, with isolated functions that form the solution of major problems.
The evolution works through modularization, where parts of the body work in a modular way suffering different forms of evolutionary pressure, changing over thousands of years almost independently, together these modules form feedback loops and that are influenced by several factors and form a complex organism, each cell functions in a functional way, being small solutions of a whole emerging from interconnected modules.
Most of these characteristics are common to complex systems that manage to organize themselves in the midst of entropy and form emergent structures, all thanks to the interaction between different parts. In DFM, functions are the parts and modules are the interaction between them.
DFM (Distributed Function Modulation) uses this paradigm to approach huge problems in a relatively simple way in their parts and highly complex in the whole. After years of research, we have seen that it is a good approach to structure complex systems that meets the needs of Drayker main projects well.
The BSDK framework uses DFM as its semantics and base architecture, coordinating how functions and modules work at the low level and connect to form the local OSDK framework.
Based on the local structure, the Dknetwork forms a distributed computing network, where each function has its proof of cryptographic knowledge that guarantees its transmission and reconstruction over the network, together with the cryptographic authenticators of Living Cryptography and the evolutionary consensus of the Dk, guarantee an infinitely scalable and secure computing network.
DFM based structures make the network's functions auditable and verifiable by Dk to ensure that the system can evolve safely. The inductive structure of DFM makes it possible for the specialist algorithms to feed back and form new metaprogramming structures, forming ontologies for Dknowledge that is based on functional and modularly aligned data structures to create a knowledge base integrated with the structure. Auditable and relationally connected data, which facilitate use by Dk and which enable the meta generation of data and ontologies to form an evolutionary and metaprogramming structure friendly to human understanding while preserving low-level self-auditing complexity and stochastic and exponential evolution.
The distributed kernel algorithms can self-coordinate forming local microstructures and global superstructures of evolutionary intelligence, thanks to the ecosystem, the metaprogramming of intelligent algorithms can solve problems cooperatively with the project and application platform, eliminate points of failure with Living Cryptography and Dknetwork, accumulating knowledge and intelligence with Dknowledge, the intelligence and evolution modules become better and more efficient the more computational power and usage the entire network has.
The more use and integration of the ecosystem, the more collective intelligence and cooperation at scale, the more discoveries, the more inventions, the more problem solving, the more sustainability and value. The network effect is one of the bases of the system, we made a vast study of graphs and fault tolerance to form these bases and we are still open to improvements, much still needs to be resolved in this field.
In the construction steps, we can use MetaDFMP to create structures based on simulation and artificial intelligence, building complex DFM-based algorithms that can be verified and integrated into the network structure and taking advantage of the latest machine learning models and techniques to form highly algorithms specialized and efficient, able to evolve thanks to the DFM structure.
Thus, we focus on models, training, simulations and tests to create architectures based on intelligent metaprogramming, taking advantage of network optimization for this type of structure. This gives us the ability to solve pending issues and needs by creating algorithms in an optimal, efficient and verifiable way. In the future Dk will create, optimize and update its own algorithms on its own according to the use of the platform and adaptation to different conditions and needs of the ecosystem.
The DFM paradigm allows us to have cooperation at scale before the PAP platform is complete, through it we create models for the construction and resolution of various types of problems.
With it we can divide big problems into small independent functions that can be worked together by people from all parts of the world, while everyone works on the modules that will connect these functions. That way we can work locally with independent functions and still have a global view with the modules.
This allows thousands of people to cooperate together for a single project, everything in a verifiable and redundant way, because if a function has not been delivered or has a problem, it can be rebuilt quickly by agents working on the same modules. A scheme can be adopted double construction and competition between functions, to increase optimization and redundancy.
With DFMPProject, we can build project proposals to help build the system and support members, so any person or group of people in the world can create a project for the ecosystem and receive support from the community, with well-validated projects that can be integrated into the main projects, if the type of project has this function and there is consensus on.
Transition projects can be planned and incorporated into the models, in order to support the community and members and optimize the construction of the main projects, such as the DAF (Decentralized Autonomous Federation).
With the DFMP we were able to create deductions, theories and specifications to resolve issues of the main and secondary projects, validate independent functions and reuse them, structure proposals and approve them in several modular steps. With this protocol we can structure proposals with formal specifications made by humans, different from MetaDFMP.
This also makes it possible to reuse existing algorithms and architectures and adapt them to DFM or create new ones, with papers that preserve the characteristics of DFM and can go through a validation and review process to gain legitimacy, reputation and support, using the DFM of architecture phase to engineering.
If an idea, project or any other type of initiative aimed at Drayker wants to build legitimacy without permission or protocol, the Emergence initiative is a way to create your projects with your own organization system, and anything else outside the DFM method and yet to be able to create teams and DAOs, create improvements and proposals. With EIP, proposals can be created with a free structure, the only limit here is imagination and ethics, legitimacy will depend on how much the initiative is aligned with Drayker and how strong it is with the community.
With this family of protocols, we hope to optimally organize the construction of something as big as Drayker, which cannot be built by just one group but in a globally distributed and cooperative way, we need to guarantee support and organization of the collective intelligence of thousands of people in the world to build that future in the best way.
