Brothers, I'm back to write about the project again 😂 although my writing isn't great, just take a look, after all, it's hand-typed and I stayed up late writing it.

Project Positioning - Making ZK infrastructure affordable and usable for developers

Lagrange's goal is straightforward: to make 'off-chain recomputation, on-chain verifiable' a common capability rather than just a concept found in white papers. It breaks the system into three parts: ZK Prover Network (proof network), ZK Coprocessor (verifiable computing co-processor), and DeepProve (zkML). The first two parts focus on blockchain data and contracts, while DeepProve integrates AI inference into the verifiable domain. This is not a single-point function but a general stack capable of covering multiple scenarios.

What Can Be Done - Several High-Frequency, Concrete Scenarios

Complex On-Chain Data Computation: Tasks like cross-protocol yield settlement, historical event aggregation, risk control indicators, etc., are handled by the Coprocessor running off-chain, bringing proofs back on-chain. Developers can initiate queries in a SQL-like manner, avoiding the need to build fragile indexing services.

Cross-Chain State Verification: Making multi-chain states into verifiable queries, with the contract side only verifying, not recomputing.

AI Result Verifiability (zkML): DeepProve provides 'proof' for AI inference, claiming significant improvements in generation and verification speed compared to mainstream zkML, suitable for real business implementation.

Core Components - Each Plays Its Role

ZK Prover Network: A modular 'network of proof networks' that emphasizes horizontal scalability, integrating workloads from rollups, DApps, co-processors, etc.; running on EigenLayer and providing auction-based resource matching based on DARA to improve cost-quality balance. The official documentation mentions that over 85 institutional-level operators are involved.

ZK Coprocessor: Preprocesses on-chain storage into a 'verifiable database', then uses zkMapReduce to execute queries and calculations, while the contract side only verifies. For developers, the threshold feels more like writing queries + verifying proofs.

DeepProve (zkML): A library/system aimed at AI inference verification, focusing on performance and practicality.

Developer Usage Process - Step by Step to On-Chain

Steps:

1. Select data and tasks → 2) Coprocessor side does preprocessing and indexing → 3) Initiate computation/query → 4) Prover Network generates proofs → 5) Contract verifies and consumes results.

The benefits of this link are clear: computing power moves off-chain, while on-chain only verifies, making costs and delays controllable and the complexity more friendly to dApp sides.

Token Economics - 'Proof Demand = Token Demand' direct connection design (this is what the token is for)

Fees and Settlements: Clients can use ETH/USDC/LA to pay when submitting proofs; verifiers (provers) ultimately receive rewards in LA. If paid with ETH/USDC, the protocol will buy back LA and redistribute it to provers, directly converting business traffic into LA demand.

Inflation Subsidy: The network sets an annual issuance of 4% of LA to subsidize prover costs, making client-side expenses more controllable. (Actually, I was surprised by this too 😯)

Staking and Delegation: Holders can stake/delegate LA to designated provers, guiding subsidy flows and forming a positive incentive of 'who can efficiently provide proofs, who receives more subsidies'.

Supply and Unlocking: Total amount 1 billion, TGE unlocking ratio 19.3%; 'Passive holding does not share profits', encouraging value acquisition through usage and contribution.

Ecosystem and Progress - Running the network is more important than discussing concepts

The official documentation reveals: The proof network has been integrated into various application scenarios, with over 85 institutional operators (such as exchanges/infrastructure companies) participating, based on EigenLayer, combined with DARA for resource bidding and service quality assurance. For infrastructure projects, these 'operational metrics' are more indicative of usability than just looking at TVL.

At the same time, LA has been included in Binance HODLer Airdrops, providing channels for early diffusion and user education, accelerating developer and community connections.

My Practical Judgement on LA

For developers: If your application cannot do without large-scale data computation, cross-chain state verification, or needs to 'realize' AI inference, Lagrange's toolchain can significantly reduce integration difficulty. Prioritize assessing whether the Coprocessor's data access and verification latency meet your business criteria.

For long-term observers: This economic model connects the 'proof demand' on the business side with buyback and subsidy mechanisms back to LA, essentially betting on the 'popularity of verifiable computing'. Focus on two points: developer retention/reuse rate and supply elasticity on the prover side.

I think Lagrange's value lies in 'productizing verifiable computing and closing the loop'. When you no longer want to struggle with self-built indices and trusted intermediaries, it provides a more 'engineered' path, while LA ties the usage and value capture of this path together. Maybe that's the case 🤔

@Lagrange Official #lagrange $LA