Recently I've been brainwashed by a concept:

'Web3 Native AI'

It sounds flashy, but here comes the problem:

What do these AIs actually consume? What do they rely on to grow?

Don't talk to me about LLMs, those like OpenAI don't belong to Web3.

I'm talking about on-chain native AI Agents——

For example, it can help you monitor the market, take profits, complete tasks, and allocate assets.

These Agents need to consume data to grow.

And it's not just any data, but:

✅ Multi-chain

✅ Structured

✅ Verified

✅ Traceable

✅ Usable for on-chain calls

Look, on-chain data is either scattered, fragmented, or untrustworthy.

Lagrange is the first project to 'unify the nurturing' of this stuff.

🐄 What exactly does Lagrange do?

In a nutshell:

On-chain nurturer of large AI models + ZK certified nutritionists

In other words, all AI applications that want to run on-chain,

Where does the data come from? Who ensures it's clean? Who provides credit endorsement?

Lagrange directly encompasses these three tasks.

📌 It has these few 'killer features':

• ZK Coprocessor:

Package, validate, and compress on-chain data into callable state proofs.

Simply put, it means making sure that all the data your AI calls is 'trustworthy, compliant, and standardized.'

• On-chain SQL queries (DSL):

Lagrange brings SQL thinking into Web3.

You don't need to write GraphQL or Subgraph; you can query any data on-chain with SQL and even across chains!

• Data aggregation layer (Data Lake):

No matter if you're using Ethereum, Solana, ZKsync, or Aptos,

It stews everything together, unifying data from all chains into one structure. Feeding AI is effortless!

🧬 Why is its combination with AI a necessity?

Because:

• Without data nurturing, AI Agents are 'blind.'

• Without a verification mechanism, what you feed in is poison, not nutrition.

• Without an aggregation structure, calling AI is like building blocks until you vomit

Lagrange addresses the underlying structural issues.

And it's a prerequisite for AI Agent applications!

🤯 Here’s a hypothetical blockbuster AI application scenario:

In the future, when you use an Agent to manage wallets, it can:

• Automatically alert you when a token suddenly surges

• Automatically stake your most idle funds

• Automatically cross-chain arbitrage

• Automatically track what VC wallets have purchased

All these operations require data tuning.

Tuning data relies on Lagrange's DSL + ZK Coprocessor.

Otherwise, all these Agents are useless.

🎯 So, what does it mean to focus on Lagrange now?

You are an early participant in the infrastructure for future on-chain AI.

And this participant has a valuable label.

In the future, whoever 'nurtures' AI on-chain and builds data channels for AI will have a voice.

And to be honest, Lagrange's DSL and ZK middleware might very well become the HTTP protocol of Web3.

✅ Summary

• AI is a trend, but no data equals waste

• Lagrange is the central hub for data sourcing in Web3 AI.

• The earlier you participate, the earlier you stand at the upstream of the data value chain.

• When AI becomes the mainstream user interface, Lagrange will also become the behind-the-scenes nurturer of AI.

So, if you don’t want to miss out on this bottom-level opportunity of AI + Web3,

Lagrange is a project you cannot ignore and must plan for.

🏷 #Lagrange @Lagrange Official $LA