Market enthusiasm is prone to fluctuations; what really endures are the infrastructures that clarify the "data—computing power—application" link. This year, Chainbase has shifted its narrative to Hyperdata Network: the goal is not to introduce a new concept but to process original signals scattered across different chains into data assets that can be "directly consumed by machines" according to unified standards, enabling applications and agents to be plug-and-play. This direction of "data layer integration" aligns well with the recent trends of Agentification and DataFi.

1. The three cornerstones of the Hyperdata Network

Manuscript: Abstracting the orchestration, processing, permissions, and pricing rules of data assets to solve the old problem of "inconsistent structural standards";

AVS layer: Decentralized execution and verification to reduce single-point trust; official materials mention that large-scale staking serves as a security backing, aiming to balance performance and credibility;

Data availability components: High-throughput data publishing and storage, serving real-time and near-real-time application scenarios.

These three elements overlap, attempting to shorten the transmission line from "on-chain data → smart applications" and reduce glue code.

2. From "can check" to "can use": Two-way connection between interfaces and data warehouses.

For development and analysis teams, the most practical experience lies in "being able to extract and implement":

Data is retrieved through a unified GraphQL/REST interface, combined with Webhooks for event triggering;

Data is sent in real-time to S3, Postgres, Snowflake, etc., through Sync; historical full data can be quickly restored, and incremental data can be continuously written at minute or even second intervals;

Forming a reusable processing chain (cleaning, aggregation, metrics) to directly support BI dashboards, risk control, and investment research pipelines.

These are all key parameters for reducing long-term operational costs.

3. Coverage of the "surface": Real progress of the multi-chain matrix

The official network support page shows that the EVM family (Ethereum, BSC, Arbitrum, Optimism, Base, Polygon, zkSync, etc.) has comprehensive coverage on "nodes/RPC, raw data, decoding, abstract API"; in the non-EVM direction, Bitcoin, TON, TRON, and Solana are also included in the support matrix (the depth of support and types of available interfaces vary across different chains and need to be verified according to scenarios). This provides a unified approach for multi-chain wallets, cross-chain data panels, risk control alerts, and clearing engines.

4. Performance perspective in engineering: SLA, backfill, and throughput

Enterprise users are more concerned about "what happens when things go wrong." Chainbase disclosed enterprise-level SLA, 99.9% availability, historical backfill speedup by 10 times, and throughput levels of "billions of data points per second" on the "How It Works" page, emphasizing the unification of historical and real-time data in the same pipeline. This triangle of "stability—throughput—cost" is crucial for risk control, market making, real-time economics in GameFi, and market monitoring.

5. Ecological collaboration: From TON to a broader partner network

On-chain data infrastructure relies on ecological collaboration. Beginning in 2023, cooperation with the TON Foundation to provide free data indexing and analysis access for Telegram ecosystem developers is a typical example; such collaborations enhance the extensibility of the "unified data layer." This is followed by more cross-chain hackathons, developer programs, and scenario implementations.

6. The most "down-to-earth" usage: Completing address profiling with a single API

Taking the distribution of ERC20 token holders as an example, directly calling Get token holders can obtain the list of holding addresses, and then overlay behaviors such as transfer history, DEX interactions, and cross-chain bridge usage to construct accurate user profiles. For the marketing team, this can be used to design a combination of "targeted airdrops + subsequent incentives"; for the risk control team, it can speed up the development of anti-witch and anti-money laundering rule sets.

7. Transaction end reality: Listing rhythm and current market conditions (as of 2025-08-27, UTC+8)

Listing and circulation: Binance announced that trading will open on 2025-07-18 at 14:00 (UTC), with an initial five trading pairs; initial circulation is 160 million (total supply of 1 billion), accompanied by a HODLer Airdrops mechanism. For users holding BNB, this is a gain from a "historical balance snapshot + retroactive rewards."

Market capitalization and transaction: CMC currently shows a market cap of approximately $31.18 million, with a 24h volume of about $70.49 million; historical highs and lows were on 2025-07-18 and 2025-07-14 respectively, indicating significant volatility and ongoing chip turnover.

Distribution across multiple exchanges: In addition to Binance, MEXC, Bithumb, Bitget, PancakeSwap, etc., also have trading pairs; the depth presented across markets helps optimize institutional and market-making strategies.

8. Project-side "quantitative clues": Developer and call scale

In the "About" module of CMC, the project party disclosed several operational-level metrics, including "500 billion+ data calls, 20,000+ developers, 8,000+ project integrations." These data reflect the platform's "breadth and stickiness"; although they belong to official metrics, they can serve as one of the mid-term observation dimensions in conjunction with its multi-chain support and enterprise SLA.

9. Why this narrative perfectly aligns with the current situation

Agents are moving towards production: From automated market making to clearing robots, the key for on-chain "automata" is not how smart the model is, but whether the data input is verifiable, traceable, and low-latency.

The assetization of DataFi: When the data processing chain turns into standardized components, the data itself possesses the attributes of "composability and valuability," providing a realistic foundation for revenue distribution and governance around data.

The reality of multi-chain: Users, assets, and liquidity flow across different chains; a unified data layer is a necessity rather than a luxury.

10. Potential uncertainties and countermeasures

Unlocking and scheduling: The rhythm of token releases, ecological incentives, and node rewards directly influences secondary market risk exposure; trading announcements and research reports should be continuously monitored.

Verifiability of metrics: The service metrics disclosed by vendors need to align with the monitoring of the users; it is recommended to establish SLO and alert lines at the initial integration stage.

Cost and benefits: High-frequency stream processing consumes significant resources; usage, replenishment scale, and concurrency of Sync and Studio need to undergo stress testing and cost estimation during the POC phase.

Ecological competition: There are many service providers in the same track, and teams need to compare based on their own tech stack, compliance requirements, and migration costs, rather than just looking at "who has more chains and who is faster."

11. An "observation checklist" for product and investment research

1) Interface availability: Success rate and latency jitter of key APIs;

2) Backfill efficiency: Whether the speed of historical data playback meets modeling/review needs;

3) Multi-chain consistency: Whether the metrics for the same address are strictly aligned across different chains;

4) Data warehouse linkage: The landing delay from synchronization to usable tables;

5) Ecological events: New links, partner launches, milestone metrics;

6) Secondary market side: Exchange capacity expansion, market making depth, evolution of holding address distribution (using holder interfaces + cross-validation with on-chain explorers).

The core of Chainbase is not about "telling a new story" but addressing an old problem in engineering to make it "reusable, verifiable, and scalable." When the flow path of data becomes clear, AI and Agents no longer get stuck at the input end, allowing product teams to focus more energy on strategy and experience. For the trading end, the value of C will increasingly depend on the genuine usage of this data network and its coupling with external ecosystems. In this regard, continuously observing its multi-chain coverage, SLA fulfillment, and the speed of Sync → data assetization implementation is more important than short-term price fluctuations.

@Chainbase Official #Chainbase $C