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.
🧵👇
2/ AI agents can already write code.
But production software needs more than code generation.
It needs:
→ Architecture
→ Standards
→ Governance
→ Security
→ Testing
→ Quality gates
→ Engineering knowledge
The missing layer is engineering context.
3/ Our thesis:
AI agents shouldn’t just generate code.
They should operate within an engineering system.
NAEOS provides the rules, context, workflows, and quality mechanisms that help agents work consistently.
4/ The architecture is built around several layers:
Constitution → Policy → Knowledge → Workflow → Quality Gates → AI Agents
The goal is to turn organizational engineering knowledge into something AI agents can actually use.
5/ We’re building NAEOS in public.
That means the community is part of the process.
We want to hear from:
👨💻 Developers
🏗️ Architects
🤖 AI engineers
🔬 Researchers
🚀 Indie builders
🌎 Open-source contributors
6/ Before the Product Hunt launch, we’re opening the NAEOS Discord.
This will be our home for:
• Architecture discussions
• AI engineering experiments
• Roadmap discussions
• Open-source development
• Community feedback
• Building with AI agents
7/ 🚀 Pre-launch: August 18, 2026
If this problem resonates with you, join us early.
👉 Join NAEOS Discord https://discord.gg/WnUWmm7XMv
We’re not just launching another developer tool.
We’re building the foundation for AI-native engineering.
#ProductHunt #NAEOS #AIEngineering #AIAgents #OpenSource #BuildInPublic
The “engineering context” thesis is interesting, but it feels like the hardest part will be proving agents actually behave better with the system than without it. Curious what concrete failure or quality metric you’re using to validate that.
That's exactly the right challenge. If NAEOS can't demonstrate measurable improvement in agent behavior, then "engineering context" is just another layer of documentation.
I'm thinking about validation at three levels:
1. Rule adherence
How often does an agent violate explicit engineering rules without the context layer vs. with it?
2. Rework / failure rate
How many generated changes fail tests, quality gates, reviews, or require human correction?
3. Context reconstruction
How often does an agent make a decision that conflicts with an existing architectural decision because the reasoning behind that decision wasn't available?
The interesting metric for me is not simply "code generated faster." It's how much human correction is required to get the agent's output production-ready.
I'd like to run the same engineering tasks against the same agents, with and without the NAEOS context layer, then compare violation rate, rework, test failures, and decision consistency.
If the numbers don't move meaningfully, that would be evidence that the thesis needs to change.
I’ve already sent you the scoped engagement by email. Please check your inbox when you get a chance — I’m waiting on your go-ahead to proceed.
Thanks for the follow-up, Aryan. I’ll check the email and review the scoped engagement. I appreciate you putting the scope together, and I’ll get back to you once I’ve gone through it.