NC
Express services and agent workflows with plain-English syntax and a compact language runtime.
Explore the language →The NC product family
From plain English
to working software.
Build application logic with NC, create interfaces with NC UI, and run models locally with NC AI. Explore three complementary tools in one place.
One family. Three building blocks.
Choose what you want to build.
NC UI
Describe layouts and behaviour, then compile interfaces into static HTML, CSS and JavaScript.
Explore interface development →NC AI
Bring model execution into your own environment with local, CPU-based inference.
Explore local inference →For developers
Explore the engineering.
Review the execution model and technical scope of each component.
NC compilation pipeline
Core architecture
// Agentic AI in NC service "research-api" @tool to search with query: gather results from "https://api.search.example/q={{query}}" respond with results agent researcher: purpose: "Research topics thoroughly" tools: [search] max_steps: 5 api: POST /research runs research topic
Problem: agent frameworks bolt tool-calling onto general-purpose languages with heavy dependency trees. Setting up a working agent means gluing together an HTTP client, a prompt template system, and an orchestration library.
Approach: plain-English syntax with agent and @tool as first-class language keywords, compiled to a single dependency-free binary.
Architecture: tokenizer → AST → bytecode compiler → stack-based VM, with a dedicated AI opcode for model invocation. See the IP page for the patent documentation covering this design.
Limitations: the standard library contains 80+ functions; assess required libraries and integrations against the language reference.
NC UI static interface generation
A plain-English frontend language that compiles to static HTML, CSS and JavaScript. Problem: AI-native applications need a UI layer that doesn't require a Node build pipeline to ship. Approach: describe layout, theme and behavior in plain English; NC UI compiles it to static assets deployable anywhere, including GitHub Pages. Limitations: component ecosystem is small; best suited to marketing sites, dashboards and internal tools today, not complex SPA state management.
NC AI local inference
Problem: most AI runtimes assume a cloud API and a credit card. Approach: local model execution on CPU — code generation, agentic tool-calling and the CORTEX runtime — with no API keys and no network dependency. Architecture: a compact model family (500K–20B parameters across 7 sizes) designed to run on commodity hardware. Limitations: local CPU inference trades raw capability for cost, latency and privacy — it is not positioned as a frontier-model replacement for every task. Full comparison on the Benchmarks page.
Bring your next idea to NC.
Explore the NC website, try a source example, or discuss how the tools fit your workflow.