Resources
Inputs, intermediate materials, code, process definitions, and published results.
Open-source framework
Build production pipelines from resources, tasks, precedents, and participants—while keeping data, execution, review, and system evolution under control.
The production problem
Digital production transforms earlier artifacts, user input, intermediate materials, and evolving requirements into new resources.
Prego! connects every result to its inputs, participants, procedure, events, and downstream consumers. It is designed for parallel production where resources keep changing while multiple workflows depend on them.
The universal building block
A precedent is a task that receives data, coordinates a transformation, and publishes a result. It has an initiator and an acceptor; it may also have a performer, human or automated.
Resolve requirements, participants, current inputs, tools, and permissions before execution begins.
A compact production language
Large workflows are composed from a small set of equally understood elements. Some tasks perform work; others group and organize related precedents.
Inputs, intermediate materials, code, process definitions, and published results.
Units that execute work or organize related activities into a navigable structure.
Repeatable transformations with inputs, participants, events, and accepted outputs.
Initiators, performers, acceptors, managers, services, applications, and agents.
Contextual relationships connecting people, capabilities, resources, and work.
A traceable account of progress, decisions, changes, availability, and publication.
Logical identity, flexible placement
Prego! separates resource identity and production history from physical storage. Metadata and heavy production data can use different backends and representations.
Erlang persistence, PostgreSQL, file servers, object storage, or specialized adapters.
Task-scoped copies prepared for desktop tools and kept aligned with current resources.
User output moves through commit, submission, review, approval, and publication.
Artifacts can migrate or run near render, inference, and remote production capacity.
Self-hosted development
Precedent code, libraries, and workflow definitions are first-class resources. They can be created, reviewed, accepted, and published through the same traceable processes they help execute.
This model welcomes source control, tests, DCC applications, external services, and AI agents as participants in the development chain.
From idea to working system
The Prego! idea describes information organization and production mechanics. Its current implementation demonstrates one practical realization without making the model dependent on that stack.
Technology-independent concepts and mechanisms for resource transformation.
ModelErlang, Elixir, Phoenix, abstract metadata and content adapters.
ImplementedProtocols for specialized applications, user workspaces, and composed data contexts.
In developmentPython access and paths toward DCC tools, services, and autonomous executors.
ExpandingIdentity and application context
The reference implementation presents each participant with one or more relevant parts of the common task hierarchy. Applications extend that hierarchy with their own resources, processes, and user workspaces.
Today, a user can self-register with email and password. The registration procedure is working and is planned to evolve into a more formalized system flow.
Each user receives one or more entry points into the task tree: personal workspaces, assigned work, project areas, or application-specific structures.
Participants can have different levels of administrative responsibility. Broader roles can oversee wider task hierarchies; the administrative model is being consolidated within the system.
An application is created through a unified process, then developed with domain-specific precedents, interfaces, libraries, code, settings, and production resources.
Assigning a user to an application establishes an application context and credentials, then provides the personal and shared task entry points required for that work.
A compact content-production application built on Prego! to exercise creative work, application integration, review, and controlled extension of the system itself.
A concrete demonstration
Vision is being developed around short-form scenario work. It will demonstrate how Prego! prepares a user context, coordinates production, receives results, carries them through review, and makes accepted artifacts available to later work.
Transparent project status
The project distinguishes working capabilities from active development and longer-term architectural directions.
Preparing for open source
Prego! is being prepared for public release with documentation, examples, integration interfaces, and its first complete application case study.
OpenAI support would help accelerate testing, documentation, maintainer automation, security review, adapter development, and experiments with agents as accountable participants in production workflows.