Recently, @SpaceandTimeDB launched on Binance, sparking widespread discussion in the market and bringing attention to the previously niche narrative of "ZK data infrastructure." As a bridge connecting smart contracts and off-chain data, $SXT is attempting to address a fundamental pain point in the blockchain world: trusted execution and verification of data. Here are my observations: 1) At its core, Space and Time is a decentralized Layer 1 blockchain (SXT Chain), but its primary value does not lie in building a general-purpose smart contract platform. Instead, it takes a unique approach by focusing on solving a specific problem: trusted data processing under zero-knowledge proofs. Its killer feature, Proof of SQL, makes tamper-proof data tables a reality, leveraging ZK technology to ensure both query verifiability and data integrity. From another perspective, this completely disrupts the traditional approach to data processing in the blockchain world. In the past, smart contracts either endured the exorbitant Gas costs of on-chain storage or were forced to trust centralized APIs and oracles. SXT offers a third path: building a dedicated decentralized data layer that combines on-chain cryptographic commitments with off-chain SQL execution, making data processing both secure and cost-efficient. 2) From a technical architecture standpoint, the SXT network consists of three key components: 1. Indexer Nodes: These act as data collectors, responsible for gathering real-time and historical data from major blockchains and converting it into SQL relational formats. 2. Prover Nodes: These serve as the computation engine, handling query requests, executing ZK-proven SQL queries on tamper-proof tables, and generating sub-second ZK proofs. 3. SXT Chain Validators: These function as data notaries, maintaining network integrity, processing data inserts, and collectively endorsing on-chain cryptographic commitments through BFT consensus. This architecture ensures that on-chain storage only retains cryptographic commitments (similar to data fingerprints) rather than complete datasets, significantly reducing on-chain storage costs. More importantly, these commitments are updatable/homomorphic, meaning that updating data does not require recalculating the entire dataset's fingerprint. Instead, changes can be layered onto the original fingerprint—a key innovation for overcoming performance bottlenecks in traditional ZK solutions when handling large-scale data. 3) SXT's Proof of SQL is not just a technical innovation; it addresses core pain points in current ZK proof systems when processing large-scale data: 1. Scalability: Traditional ZK proofs are inefficient for large datasets, whereas SXT claims to achieve millisecond-level ZK proof generation. If on-chain verification Gas consumption is as low as 150k, this represents a major breakthrough in the ZK Prove domain. 2. Developer Friendliness: It provides developers with familiar SQL interfaces rather than complex ZK circuit programming, significantly lowering the development barrier. 3. Versatility: It is not only applicable to SXT's decentralized database but can also be used with traditional databases (e.g., PostgreSQL, Snowflake), expanding the technology's applicability. From an abstract perspective, SXT essentially creates a "trusted data computation middleware" for the blockchain world, overcoming the inherent data blind spots of smart contracts and ensuring that on-chain applications are no longer data islands. It acts as a "query co-processor," addressing the inherent limitations of smart contracts in directly accessing historical on-chain data, cross-chain data, off-chain data, or complex aggregated data. 4) Beyond the technical narrative, SXT's commercial value is perhaps even more noteworthy. Its application scenarios cover almost all current Web3 hotspots: 1. ZK-Rollups/L2 Optimization: Serving as the data layer for L2, reducing Gas costs and enhancing scalability. 2. Cross-chain Security Bridging: Providing multi-chain data verification to enhance bridge security. 3. Decentralized DApp Backend: Replacing traditional centralized backends with verifiable data services. Additionally, it includes data-driven DeFi, RWA, GameFi, and SocialFi applications—all of which face on-chain storage bottlenecks. 5) Finally, let's look at SXT's tokenomics design, which I find reminiscent of traditional PoS combined with a data marketplace: 1. Validators: Stake SXT to participate in network security and earn network fees and token emission rewards. 2. Table Owners: Create and maintain tamper-proof data tables, profiting from insertion fees and query fees. 3. Users: Pay query fees to use network services. The brilliance of this model lies in splitting "query fees" between data providers and validators, forming a self-sustaining data marketplace ecosystem. The more valuable the data, the higher the query volume, benefiting all parties and attracting more high-quality data, thus completing a positive feedback loop. In summary, $SXT's greatest innovative value lies in creating a solution that combines SQL, a traditional database tool, with Web3's zero-trust architecture, enabling the blockchain ecosystem to handle more complex data logic. This not only addresses the "inherent data shortcomings" of smart contracts but also provides a viable path for enterprise-grade applications with stringent data quality and processing requirements to adopt blockchain technology. With the project's deep integration with leading ecosystems like zkSync, Avalanche, and Chainlink, along with Binance's endorsement, SXT has already secured its "ticket" to becoming mainstream infrastructure. Of course, challenges remain, such as overcoming the inherent trade-offs between decentralization and performance, as well as market education and developer adoption, which will take time.