I Open-Sourced Nash

17,000 Models, Ridiculous Memory, and a Cleaner Way to Build AI Apps

Most AI apps have one move.

A chat box.

A model picker.

A little retrieval.

Maybe a tool call if you’re lucky.

That’s not enough.

So I open-sourced Nash — an AI assistant app built on Backboard.io with 17,000 models, Stripe subscriptions + overages, and what I’d argue is the smartest memory system in the game.

Not a toy.

Not a wrapper.

A real foundation for building serious AI products.

The problem

A lot of AI app demos look good for five minutes.

Then the cracks show:

• memory is shallow
• model support is narrow
• billing is bolted on
• orchestration gets messy
• the product feels like a prototype wearing a blazer

The result: lots of flash, not much flow.

If you want to build something users actually come back to, the app needs more than a prompt box. It needs context, continuity, flexibility, and a business model that doesn’t collapse the second people start using it.

What Nash does

Nash is an open-source AI assistant app that gives builders a serious starting point.

It includes:

• access to 17,000 models
• subscription billing with Stripe
• usage overages
• deep memory
• tool-friendly AI app architecture
• a polished product foundation built on Backboard.io

The focus is simple: give developers a system that sees the floor well, moves fast, and makes the whole offense better.

Why I open sourced it

Because too many AI starter apps are either:

• too shallow to matter
• too rigid to extend
• too demo-first to ship
• too disconnected from real product needs

I wanted to release something more complete — an OSS app that shows how to build an AI product with actual range:

• flexible model access
• persistent user context
• monetization
• product-ready UX
• architecture that can support real usage

Something that doesn’t dribble the air out of the ball every time you try to add a feature.

Why the memory matters

The biggest gap in most AI apps isn’t raw model quality.

It’s continuity.

People don’t just want an answer. They want software that remembers what matters, adapts over time, and gets better with use. That’s where Nash is strongest.

A good assistant shouldn’t reset every possession. It should carry context forward, make better decisions, and keep the interaction smooth.

That’s the difference between a neat demo and a product people actually trust.

Built on Backboard.io

Nash is one of three apps I’m open-sourcing, all built on Backboard.io.

The point isn’t just to release code.

It’s to show what a stronger foundation for AI apps can look like when you combine orchestration, memory, billing, and product design in one system.

Repo

If you want to fork it, study it, or build on top of it:

GitHub: https://github.com/Backboard-io/Nash

If you’re building AI apps and you’re tired of the usual iso-heavy demo stack, Nash might be a better starting lineup.