Launching today

NAEOS

Launching today

Specify once. Build anywhere

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Specify once. Build anywhere

1 follower

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NAEOS is a declarative engineering platform that transforms specifications into high-quality software systems through a consistent, validated, and extensible pipeline.

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Launch tags:Software Engineering•Developer Tools•Artificial Intelligence

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Hi Product Hunt! bayu here, the maker of NAEOS. I'm genuinely excited (and a little nervous) to share this one. The problem I kept hitting: every project starts with a great spec document, and six months later the spec and the code are strangers. Nobody maintains the spec because the code "is the source of truth." But then the code base grows, the docs rot, and every new engineer or AI assistant has to reverse-engineer the whole system. The idea: flip it. Make the specification the source of truth, and make the tooling so good that keeping the spec accurate is the path of least resistance. That's NAEOS — you describe your system once, and it parses, validates, builds an internal engineering model (NEIR), and generates real code in Go, TypeScript, Python, Java, and Rust. Not a scaffold — a consistent, validated, auditable pipeline. What's in v3.0.0 (the version we're launching with): Pipeline profiling, memory analysis, and stage caching — rebuilds reuse cached results A NEIR-aware LSP server with autocomplete, diagnostics, and go-to-definition for .naeos.yaml Distributed builds with naeos build --distributed --workers N VS Code extension generation with naeos dx vscode-gen Official example plugins and a microservices Go starter template The AI angle: we ship a compiler that turns the engineering model into instruction sets for 6 AI tools (Copilot, Claude Code, Cursor, Gemini CLI, Codex, OpenCode), plus an MCP server — so AI assistants work from an accurate model of your system instead of guessing. Try it in 30 seconds: curl -fsSL https://naeos.dev/install.sh | sh naeos create my-app naeos run --input-file spec.yaml It's open source (Apache 2.0) — the whole roadmap is public. I'd love feedback on what's missing, what's over-engineered, and what spec-driven workflow you'd want us to tackle next. Ask me anything — I'll be here all day.

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3d ago

Forum Threads

p/naeos

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2d ago

Product Hunt Pre-Launch Thread 🚀

1/ We re getting ready to launch NAEOS on Product Hunt.

NAEOS is an AI Engineering Operating System for building production-ready software with AI coding agents.

Not another AI coding assistant.

We re building the engineering foundation around the agents.

p/naeos

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2d ago

AI angle

The AI part deserves its own comment, because it's the part people react to most.

NAEOS compiles the engineering model into instruction sets for 6 AI tools: Copilot, Claude Code, Cursor, Gemini CLI, Codex, and OpenCode. Plus an MCP server. The idea: your AI assistant works from an accurate model of your system, not from guessing your repo's conventions.

The spec stays the source of truth so when it changes, the generated context changes with it. No stale context files.

If you're building AI-assisted workflows, I'd love to hear what your setup looks like today.

p/naeos

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2d ago

Honest trade-offs

Since transparency builds trust: what NAEOS is not.

It's not a "no-code, describe and never code again" tool. For systems where the spec IS the artifact (APIs, services, schemas, governance), it shines. It's not magic for arbitrary bespoke UI.

The spec language has a learning curve ($ref, $include, $fn, $if). We document it thoroughly, but it's real syntax.

Generated code is a consistent starting point you own and modify it. The pipeline keeps the spec authoritative, and the diff engine shows drift.

I'd rather say this than have people discover it later. Questions welcome.

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