Tutto incluso, zero boilerplate
Ogni funzionalità è già integrata nel framework. Configura i metadata, il runtime fa il resto.
Metadata-Driven
Definisci tabelle e colonne nei metadata, il framework genera CRUD, filtri, paginazione e validazione automaticamente.
Designer Visuale
Dashboard drag-and-drop con datasource, repeater, chart, map, scheduler e template dinamici.
Workflow Engine
Designer e runner per processi operativi: grafi condizionali, step, azioni custom e trigger automatici.
Report Builder
Generazione report con designer integrato e viewer runtime, esportazione PDF/Excel.
RAG Chatbot
Assistente AI integrato che interroga il codebase in linguaggio naturale con retrieval ibrido + LLM.
Multi-DBMS
SQL Server e MySQL supportati con provider drop-in. Cambia database senza riscrivere codice.
Linux nativo
Deploy su Linux con SQL Server o MySQL. Stack .NET 10 cross-platform, tarball self-contained, nessuna dipendenza Windows runtime.
Guarda in azione
Interfacce reali generate dal framework. Ogni componente è configurabile via metadata.
List Grid
Designer
Kanban
Chart
Map
Edit Form
Operativo in 3 passi
Scarica lo ZIP, configura il database, avvia. Nessuna installazione complessa.
Scarica ed estrai
Scarica lo ZIP da Downloads ed estrailoAvvia il backend
dotnet runAvvia il frontend
cd wwwroot && npm install && npm run serve:npmDal blog tecnico
Deep dive sul framework — metadata, RAG, designer, mobile auto-layout, workflow engine.
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 to teach a 30B model to pick the right tool on the first try instead of getting bounced by a guard and correcting afterwards. Five OutOfMemory crashes, one counterintuitive fix, and a clean 4-hour training run included.
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.