Brothers, to be honest, do you really feel at ease when calling AI APIs these days?

You throw your data to some data center somewhere, and it returns a result.
If it’s right, the credit goes to the AI;
If it’s wrong, you take the blame.
Not to mention all the headaches around data privacy and model lock-in.
It’s like eating at a restaurant where you’re not allowed to see the kitchen—you can’t help but feel a little uneasy.

Recently, I noticed a project called @nesaorg that’s trying to change this situation.
It’s not just empty talk—it’s a real execution layer that runs AI inference on-chain.

So what does this mean for us developers?

First, you get ready-to-use on-chain AI capabilities.
You don’t need to build a distributed AI architecture from scratch—Nesa already has a network ready.
Just call it directly; things like model deployment, task scheduling, and result verification are all handled on-chain for you.
In other words, the execution process of your AI application becomes open and auditable.

Second, it balances cost and performance.
Through distributed inference and task splitting, multiple nodes can work simultaneously, which brings down the expensive inference costs.
Complex logic that used to “grind to a halt once it’s on-chain” now has a practical solution.

Third—and this is what I’m most optimistic about—it provides a full toolchain.
From inference calls to model management, there are ready-to-use tools available.
This greatly lowers the development barrier, allowing us to focus more on application logic instead of wrestling with the underlying infrastructure.

Simply put, what @nesaorg wants to do is give developers like us a tool to break open the “black box” of centralized AI.
If you’re also building backend services that need trustworthy, transparent AI, maybe you should check out Nesa.
What it offers might just be the missing piece for the next generation of AI applications.