Technology

OKZOO collects real-world data—such as air quality, temperature, humidity, and noise—via its P-mini environmental sensing device, then uploads the data to a decentralized network for verification and recording. A full data upload process typically involves six stages: environmental data collection, device preprocessing, network verification, on-chain recording, AI data integration, and reward distribution. Unlike traditional IoT networks, OKZOO integrates data contribution, on-chain incentives, and AI use cases, enabling real-world data to continuously become verifiable digital assets and AI resources.

The key distinction between SQD and The Graph lies in their data processing architecture: The Graph primarily relies on Subgraph to create application-specific data indexes, whereas SQD employs a distributed Data Lake and Worker network architecture, enabling more flexible historical data queries and multi-chain data analysis. From a developer standpoint, The Graph is better suited for building standardized query interfaces around specific protocols, while SQD focuses on large-scale on-chain data access, complex analytical tasks, and real-time data processing. Both solutions are essential components of Web3 data infrastructure, but their design objectives and technical approaches differ significantly.

Subsquid (SQD) is a decentralized blockchain data layer built for Web3 applications, leveraging a distributed data lake, a network of Worker nodes, and a Portal query layer to deliver high-performance, low-cost, and scalable on-chain data access for developers. Unlike traditional RPC nodes that read blockchain data directly, SQD preemptively handles data collection, indexing, and storage, allowing applications to rapidly retrieve both complex historical and real-time data.

Unlike traditional RPC nodes that scan the blockchain in real time, SQD dramatically boosts the efficiency of complex queries by pre-processing and indexing data. As new blocks and transactions are generated on the blockchain, the SQD Network continuously ingests raw data and stores it in a distributed data lake. Worker nodes handle indexing and data processing, while the Portal layer manages developer requests and orchestrates network resources, ultimately delivering structured results back to the application.

SQD Worker nodes are the core infrastructure responsible for data processing and query execution within the SQD Network. They retrieve raw blockchain data, then index, verify, and optimize storage, while responding to query requests from the Portal layer. Through distributed collaboration, multiple Worker nodes collectively form a decentralized data service network.

Orochi Network is a Web3 data network specializing in Verifiable Data Infrastructure (VDI), with the core objective of building a trusted data interaction framework between blockchain and off-chain environments. By leveraging Zero-Knowledge Proofs (ZKP), zkDatabase, Verifiable Data Pipeline, Fully Homomorphic Encryption (FHE), and Trusted Execution Environment (TEE), Orochi aims to make every stage of data—from creation, storage, and computation to final output—verifiable. This enables users to independently validate data authenticity and computational accuracy without relying on the credibility of centralized entities.

Bluwhale AI (BLUAI) is an intelligent data infrastructure built for the Web3 ecosystem. Using identity embedding, on-chain behavior analysis, and privacy computing, it converts user data scattered across blockchain networks into intelligent profiles that AI agents, decentralized applications, and enterprise systems can access. Bluwhale AI aims to establish Web3's Intelligence Layer—protecting user data ownership and privacy—while enabling AI to comprehend user behavior, preferences, and on-chain identities. This supports personalized recommendations, intelligent decision-making, automated services, and novel digital economy applications.

Identity Embedding is the core technology Bluwhale AI uses to construct on-chain user intelligence profiles. By applying machine learning models, it analyzes users' behavior patterns, asset allocation, protocol interactions, and identity traits across blockchain networks, transforming these data points into a unified vectorized identity representation. Unlike conventional wallet addresses that merely log transaction data, Identity Embedding allows AI systems to grasp users' behavioral preferences, risk characteristics, and participation habits, resulting in a more complete digital identity model.

Bluwhale AI and Fetch.ai are both key infrastructure projects at the intersection of AI and blockchain, yet their core positioning is fundamentally different. Bluwhale AI focuses on building a Web3 Intelligence Layer that leverages identity embedding and user profiling to help AI understand on-chain users. Fetch.ai, on the other hand, is dedicated to creating an autonomous AI agent network, enabling automated collaboration and task execution through intelligent agents.

Glamsterdam is a critical upgrade phase in the Ethereum roadmap, with one of its core goals being to transition Ethereum from traditional sequential execution toward parallel execution. To accomplish this, Ethereum is advancing Block Access Lists (BAL), optimizing state access, and adjusting block execution architecture — all aimed at improving Layer 1 throughput and resource utilization while preserving decentralization and security.

Ethereum ePBS (Enshrined Proposer Builder Separation) is one of the most closely watched protocol-level mechanisms in the Ethereum Glamsterdam upgrade. Its core objective is to embed block construction directly into the protocol layer while preserving network decentralization and security—thereby optimizing the MEV (Maximal Extractable Value) market structure, reducing dependence on third-party relays, and enhancing transparency and fairness in the block production process.

Ethereum Glamsterdam represents the next-generation protocol upgrade within the Ethereum roadmap. Its core objectives are to improve Layer 1 Operar throughput, optimize block construction mechanisms, and further enhance network scalability and user experience, all while preserving decentralization and security. Key components of this upgrade include Enshrined Proposer Builder Separation (ePBS), Block Access Lists (BAL), and parallel execution capabilities. This upgrade is regarded as a critical milestone in scaling the Ethereum main chain.

Both GEODNET and traditional CORS networks deliver RTK high-precision positioning services, yet GEODNET operates on a Decentralized Physical Infrastructure Network (DePIN) model, whereas traditional CORS networks are predominantly centrally built and maintained by government agencies, surveying authorities, or commercial operators. While both depend on GNSS reference stations to produce positioning correction data, they differ markedly in network expansion approaches, cost structures, and participation models.

RTK (Real-Time Kinematic, 实时动态定位) is a high-precision positioning technology built on Global Navigation Satellite Systems (GNSS). By calculating satellite signal errors in real time at a reference station and transmitting correction data to user devices, it elevates ordinary GPS accuracy from meter-level to centimeter-level precision. GEODNET integrates RTK with a decentralized physical infrastructure network (DePIN), leveraging globally distributed GNSS reference stations to continuously generate and share correction data. This delivers high-precision positioning services with broader coverage and lower cost. Unlike traditional RTK networks, GEODNET employs token-based incentives to fund infrastructure growth, enabling community-driven global expansion.

GEODNET and Helium are both part of the DePIN (Decentralized Physical Infrastructure Network) sector, but the infrastructure they build serves different purposes. At their core, GEODNET provides location data infrastructure, solving how devices pinpoint their position with accuracy, while Helium offers connectivity infrastructure, addressing how devices connect to the network. Both leverage token incentives to advance real-world infrastructure, yet they differ significantly in their target users, data types, business models, and industry applications.