Most teams start projects the same way: a spec document that slowly drifts out of sync with the code. NAEOS inverts this. You describe your system once in a YAML/JSON specification, and NAEOS builds an internal engineering model (NEIR), validates it, schedules a DAG of tasks, and generates production-quality code across Go, TypeScript, Python, Java, and Rust — with full traceability from intent to implementation.
And the spec stays the source of truth for the entire lifecycle. Watch mode re-runs the pipeline on every change, the diff engine shows what drifted, the migration engine evolves schemas across versions, and the artifact store keeps every generated output auditable.
NAEOS is built for the AI era. Its compiler transforms the engineering model into instruction sets for GitHub Copilot, Claude Code, Cursor, Gemini CLI, Codex, and OpenCode — so any AI assistant works from an accurate model of your system, not a guess. A built-in MCP server exposes the model to AI agents, and context bundles give LLMs the project summary they need in a single file.
Spec Language v2 — variable interpolation (${var}), environment resolution ($env{VAR}), cross-references ($ref{path}), multi-file composition ($include{file}), custom functions ($fn{name(args)}), and conditional sections ($if / $endif)
Multi-language generation — Go, TypeScript, Python, Java, Rust from one specification
AI compiler — instruction sets for 6 AI tools (Copilot, Claude Code, Cursor, Gemini CLI, Codex, OpenCode), MCP server, LLM-optimized context bundles
67 CLI commands — run, validate, compile, context, test, docgen, mcp, marketplace, diff, watch, migrate, and more
NEIR-aware LSP server — autocomplete, real-time diagnostics, hover docs, and go-to-definition for .naeos.yaml files
Profile & plugin marketplaces — 5 built-in industry profiles (SaaS, AI Agent, FinTech, Healthcare, Government), WASM plugin SDK with official example plugins
Governance built-in — policy evaluator, artifact review, and full audit trail
Enterprise plumbing — PostgreSQL/MySQL/SQLite, WebSocket, event sourcing, distributed task execution, stage caching, pipeline profiling
Open source — Apache 2.0, Go, single static binary, Docker-ready
curl -fsSL https://naeos.dev/install.sh | sh naeos create # interactive wizard — enter your project name cd my-app naeos run --input-file spec.yaml
Docs: https://docs.naeos.dev — GitHub: https://github.com/NAEOS-foundation/naeos Whitepaper: https://naeos.dev/whitepaper
PH requires at least 3 gallery images; minimum size 750×750px, recommended 1600×900px (16:9) or square. Dark backgrounds match the brand (#05050a).
All five gallery images + the logo are already generated in assets/ (PNG, 1600×900, brand-accurate). Regenerate anytime with node launch/producthunt/assets/generate.js (requires sharp: npm i sharp).
#ImageFile1Hero cover — logo, tagline, real naeos run output in a terminal cardassets/01-hero-cover.png2Pipeline diagram — Input → Core Layer → Generation → Output with traceability barassets/02-pipeline-diagram.png3CLI screenshot — real naeos run pipeline output on a dark terminalassets/03-cli-pipeline.png4AI compiler output — naeos context bundle + 6 compiled AI instruction setsassets/04-ai-compiler.png5LSP / VS Code — .naeos.yaml with hover tooltip, diagnostics, and status barassets/05-lsp-editor.png—Logo (upload in the logo field, 240×240px)assets/logo-240.png
Do not put links in gallery images — PH strips them and it looks unprofessional.
Website: https://naeos.dev
Documentation: https://docs.naeos.dev
Whitepaper (EN): https://naeos.dev/whitepaper
Release notes (v3.1.0): https://github.com/NAEOS-foundation/naeos/releases
Community (Discord): https://discord.gg/WnUWmm7XMv