Self-configuring package 5.1.0
MAIOS Project Kernel
MAIOS Project Kernel lets you build, within your project, a system for working with AI that develops knowledge and competences through use. The assistant draws on them to understand what is needed, develop solutions and evolve them along with the context.
It is a package installed in the project folder and used with your own AI coding assistant. It preserves knowledge, methods and the reasons behind decisions, so work can continue independently of the application, AI model or instance.
- Context
- Competences
- Continuity
The project evolves in context
Knowledge and awareness nourish each other.
Context gains meaning through evidence and provides the potential for insights and solutions suited to the moment when they will be needed.
The Kernel connects this information with the project’s sources and the assistant’s competences. The way forward takes shape from the situation: it may call for a direct answer, further exploration or the development of a capability that is still missing.
The assistant connects intent, sources and results as it works. New knowledge or a useful possibility can lead people and the assistant to reconsider both the solution and the result to pursue.
Learning within the project
Competences that develop through work.
Knowledge can shape the method as it acts.
A competence gives the assistant the knowledge and methods to work within an area of the project. The assistant can combine it with other competences, develop it and create new ones when needed.
Learning takes shape when new knowledge, a successful solution or a correction improves the knowledge and methods available for subsequent work. The project can thus develop capabilities that were not anticipated at the outset.
The knowledge and methods developed in this way remain in the project, readable and editable, and become available to the assistant in later work.
The meta movement also concerns the Kernel itself: it can improve the method through which it recognises, forms and updates competences. In this context, the autological core means that the system can also work on its own way of understanding and operating; experience thereby participates in its autopoietic evolution.
Across nodes, applications and instances
Continuity belongs to the system.
The Kernel can be used from different working nodes, with different applications, AI models and instances. The system expresses itself by preserving how, why and what events mean.
Decisions, results and open questions remain connected to the reasons behind them. Those taking part in the work can recover these reasons in the current context, recognize what remains valid and contribute to the system’s evolution.
With your AI assistant
Start in the project you want to work on.
Open your coding environment and send the installation prompt to the assistant. It will find the instructions in the official repository for using the package in the context already open.
You can start a new project or integrate the Kernel into existing work. The installer proposes changes and reports any conflicts with existing files.
Requirements and manual installation
Python 3.11 or later is required (qualified 3.11–3.14). Project Kernel 3.0.0 family. The package/ folder contains the installer and installation instructions, including a change preview, verification and recovery.
Version 5.1.0 on GitHub
Explore the Kernel and its sources.
The usage guide explores working with the Kernel. The Kernel description explains its principles, competences and learning. The repository also contains architecture, code and the history of changes.
You can also download the installable ZIP package and its SHA-256 checksum.
Evolving research · Working Paper
System Semantic Kernel (SSK)
The SSK Paper explores the logic through which knowledge, competences and action participate in a system’s evolution. It accompanies the Kernel’s development, making its relationships and their implications for other systems understandable.
It is a source for people who want to understand these foundations and for AI systems that need to navigate this knowledge. The text evolves through new understanding and revisions while retaining its connection to the sources.
You can consult the Paper’s sources, follow its revisions and find competences for working on the text with AI. The research state keeps information about the manuscript and its revision up to date.
Content generated by the system.