Todo incluido, cero boilerplate
Cada funcionalidad ya está integrada en el framework. Configura los metadatos, el runtime hace el resto.
Metadata-Driven
Define tablas y columnas como metadatos: el framework genera CRUD, filtros, paginación y validación automáticamente.
Diseñador visual
Dashboards drag-and-drop con datasource, repeater, chart, map, scheduler y plantillas dinámicas.
Motor de workflow
Diseñador y runner para procesos operativos: grafos condicionales, pasos, acciones personalizadas y triggers automáticos.
Generador de informes
Generación de informes con diseñador integrado y viewer runtime, exportación PDF/Excel.
RAG Chatbot
Asistente IA integrado que consulta el código en lenguaje natural con retrieval híbrido + LLM.
Multi-SGBD
SQL Server y MySQL soportados mediante providers drop-in. Cambia de base sin reescribir código.
Linux nativo
Despliegue en Linux con SQL Server o MySQL. Stack .NET 10 multiplataforma, tarball autónomo, sin dependencias de runtime Windows.
Míralo en acción
Interfaces reales generadas por el framework. Cada componente es configurable vía metadatos.
List Grid
Designer
Kanban
Chart
Map
Edit Form
Operativo en 3 pasos
Descarga el ZIP, configura la base de datos, arranca. Sin instalación compleja.
Descarga y extrae
Descarga el ZIP desde Descargas y extráeloArranca el backend
dotnet runArranca el frontend
cd wwwroot && npm install && npm run serve:npmDel blog técnico
Análisis profundos del framework — metadatos, RAG, designer, layout mobile, motor de workflow.
The graph is the source of truth: shipping an embeddable workflow engine
How we built a workflow engine into a metadata-driven Angular framework — why the visual graph stays the single source of truth, and how assisted authoring keeps non-experts productive.
Teaching a local coding agent from its own mistakes: DPO on a 30B model
Our VS Code assistant was passing every test on its curriculum — which meant the curriculum had stopped measuring anything. Here's how we built honest eval sets, found two silent contaminations in our test bench, and used Direct Preference Optimization on the assistant's own redirect pairs to teach a 30B model to pick the right tool on the first try. Five OutOfMemory crashes, one counterintuitive fix, a clean 4-hour training run — and a pre-registered eval gate whose verdict we report as measured, including the part that failed.
Running the WUIC assistant on a local LLM: Ollama, an MCP server, and a free agentic VS Code
We moved the generative half of our RAG chatbot off a paid cloud API and onto a local model served by Ollama on a GPU box — and exposed the same WUIC knowledge to Cline and Continue in VS Code through a tiny MCP server. This is the architecture, the one hard problem (tool-calling), the measured results, and an honest accounting of what a local LLM costs you in quality and latency to save you in money and privacy.