Movement price

In the cryptocurrency market, there are already hundreds of DEXs (decentralized exchanges). However, Momentum Finance (@MMTFinance), built on the Sui blockchain, is not just "another DEX." Today, we will explore why Momentum Finance, which is set to have a sale on @buidlpad, uses the Move language. After its beta launch on March 31, 2025, this project recorded a TVL of $558 million in just six months and reached third place among global DEXs with a daily trading volume of $1.1 billion. It was fundamentally built on a different technological philosophy. In most programming languages, variables can be copied or deleted. For example, if you copy the code balance += 1000; twice, it becomes 2,000. But real money doesn't work that way. If I take out 1,000 won from my wallet, that money should no longer be in the wallet. The Move language implements this simple yet important concept at the language level. A special data type called Resource cannot be copied and can only be explicitly moved. Just like physical currency, it can only move from one place to another and cannot be created or disappear out of thin air. So why is this important? Currently, the most widely used smart contract language, Solidity, does not have such constraints, so if a developer makes a mistake, they could mint tokens twice or permanently burn them. In fact, many hacks in DeFi history have exploited such logical flaws, resulting in hundreds of millions of dollars in losses. However, Move prevents such mistakes at the compilation stage, ensuring that hacks due to errors do not occur. Momentum's CLMM contract consists of over 10,000 lines of Move code. Yet, in the audit conducted by Movebit in October 2025, no critical vulnerabilities were found. This is not a coincidence. Move Prover has mathematically verified the following properties: 1. The issuance of LP tokens when adding liquidity exactly matches the mathematical formula. 2. Fees are never double-charged. 3. Position NFTs can only be withdrawn by the owner. These properties are not just "passed tests" but have been proven to always be True for all possible input combinations. Additionally, in October 2025, Momentum started a bug bounty program with HackenProof. The reward system is: - Critical (potential fund theft): $20,000 - $200,000 - High (logic errors, oracle manipulation): $2,000 - $20,000 - Medium (denial of service): $500 - $2,000 - Low (minor issues): $100 - $500 The maximum reward of $200,000 ranks among the top in DeFi protocols, demonstrating a serious investment in security. As of now, the number of reported critical vulnerabilities is, of course, zero. I believe the biggest threat to DEXs is exploits, and currently, Momentum Finance is working to bring this probability close to zero, and I think they are on the right track. This might be the key reason why Momentum Finance uses the Move language. To keep users' assets as safe as possible.

In the cryptocurrency market, there are already hundreds of DEXs (decentralized exchanges). However, Momentum Finance (@MMTFinance), built on the Sui blockchain, is not just "another DEX." After its beta launch on March 31, 2025, it recorded a TVL of $558 million in just six months and reached third place among global DEXs with a daily trading volume of $1.1 billion. This project is built on a fundamentally different technological philosophy. In most programming languages, a variable can be copied or deleted. For example, if you copy the code int balance = 1000 twice, it becomes 2,000. But real money doesn't work that way. If I take 1,000 won out of my wallet, that money should no longer be in the wallet. The Move language implements this simple yet important concept at the language level. A special data type called Resource cannot be copied and can only be explicitly moved. Just like physical currency, it can only move from one place to another and cannot be created or destroyed in the air. Why is this important? Traditional smart contract languages like Solidity do not have such constraints, allowing developers to accidentally mint tokens twice or permanently burn them. In fact, hundreds of millions of dollars in hacks in DeFi history have exploited these logical flaws. Move prevents such mistakes at the compilation stage. Momentum's CLMM contract consists of over 10,000 lines of Move code. An audit conducted by Movebit in October 2025 found no critical vulnerabilities. This is not a coincidence. Move Prover has mathematically verified the following properties: 1. The amount of LP tokens issued when adding liquidity exactly matches the mathematical formula. 2. Fees are never double-charged. 3. Position NFTs can only be withdrawn by the owner. These properties are not just "passed tests" but have been proven to always be True for all possible input combinations. Additionally, in October 2025, Momentum launched a bug bounty program with HackenProof. The reward structure is as follows: Critical (potential fund theft): $20,000 - $200,000 High (logic errors, oracle manipulation): $2,000 - $20,000 Medium (denial of service): $500 - $2,000 Low (minor issues): $100 - $500 The maximum reward of $200,000 ranks among the top in DeFi protocols, demonstrating a serious investment in security. So far, there have been zero reported critical vulnerabilities. I believe the biggest threat to DEXs is exploits, and I think Momentum Finance is working hard to make this probability close to zero and is on the right track.

The two directions of on-chain AI: Talus_Labs' computational parallelization and irys_xyz's data immutability The MoveVM architecture of Talus Network and Irys' Dual-Ledger structure show distinct philosophical differences in how they handle AI workloads. Talus maximizes parallelism at the execution layer through an object-centric parallel execution structure, while Irys secures scalability for data-intensive tasks through a dual ledger structure that separates storage and execution. Talus executes non-conflicting transactions in real-time parallel using the Block-STM parallel processing mechanism, making it suitable for concurrent decision-making and transaction execution by AI agents. In contrast, Irys achieves processing speeds of over 100,000 transactions per second and low latency by handling data validation and permanent storage in parallel through the separation of the submission ledger and the publication ledger. Talus' MoveVM inherits Sui's object-based model, automatically analyzing transaction dependencies at runtime and processing operations on independent objects in parallel. This structure is optimized for parallel inference and real-time collaboration among AI agents, with over 95% of simple transactions not going through the consensus process, allowing for the potential of millions of transactions per second. On the other hand, Irys implements parallelization centered around the data layer, where data that has undergone light validation in the submission ledger is promoted to the publication ledger, gaining immutability and permanence. This process eliminates contention between storage and execution, fundamentally resolving bottlenecks in block space. Talus' resource model is based on the linear type system of the Move language, managing all assets and states at the object level, preventing replication or implicit creation. This ensures that resource ownership transitions and access control are explicitly managed, maintaining safety even during parallel processing. In contrast, Irys secures the integrity and immutability of stored data through Merkle tree-based cryptographic proofs and a global data replication structure. Data must complete sufficient replication proofs before being promoted to the publication ledger, and the dual ledger structure satisfies both the permanence and access speed of the data. While Talus performs static verification at the compilation stage, Irys verifies integrity through cryptographic proofs after execution. This difference indicates that Talus prioritizes security and formal stability, while Irys emphasizes efficiency and flexibility in large-scale data environments. The former is optimized for high-frequency, low-volume operations such as real-time decision-making by autonomous agents, while the latter is optimized for low-frequency, high-volume operations such as storing model training data and sharing large-scale inference results. In terms of resource management, Talus adopts a fine-grained gas charging structure at the object level to keep parallel operation costs predictable, while Irys stabilizes long-term storage costs through a fixed pricing system based on physical storage units (GB, TB). The average cost per operation for Talus is around $0.001, while Irys' cost for permanent storage is $0.05 per GB, making it more than 20 times cheaper than competing protocols. The philosophy of the verification system is also different. Talus blocks the possibility of errors at the pre-deployment stage through formal verification using the Move Prover, while Irys secures continuous reliability of data through post-verification methods using Merkle roots and storage proofs. The former is summarized as a security-centric proactive model, while the latter is a post-verification model centered on large-scale data. As a result, Talus is suitable for environments with low tolerance for errors, such as autonomous trading agents or real-time DeFi orchestration, while Irys shows strengths in areas that require continuous verification of large datasets, such as dataset management, distributed learning, and DePIN networks. The development ecosystems are also differentiated. Talus focuses on development tools and formal verification frameworks based on the Move language, while Irys can utilize Solidity and existing EVM toolchains, lowering the entry barrier. Talus has an advantage in security but has a steep learning curve, while Irys is advantageous in development speed and accessibility. The former is on-chainifying AI workflows through integration with Sui and the Nexus framework, while the latter allows for direct data verification and inference calls from EVM contracts through the scalable storage operations of IrysVM. Overall, both Talus and Irys show performance improvements of over 100 to 1000 times compared to traditional sequential execution models, but their optimization directions differ. Talus is specialized in computation-centric parallel inference and real-time agent collaboration, while Irys focuses on data-centric permanent storage and accessibility of large-scale AI models. Therefore, Talus is suitable for environments where execution bottlenecks are the main constraint, while Irys is suitable for environments where data availability is the key constraint. When the two systems are combined complementarily, they can form an ideal combination that efficiently covers both the execution layer and data layer of the on-chain AI ecosystem.