"Not every project should issue a coin now, but every protocol must have value to exist. We focus on low-key testing, because the real infrastructure always speaks out when it goes online"

📌 DataSoul $27,000 Airdrop is now live! We will be giving away $100 worth of DataSoul tokens to 270 lucky participants. Total Prize: $27,000 in tokens Winners: 270 users / $100 tokens each How to participate: 1 Follow DataSoul 2 Like and retweet this tweet 3 Comment on

Our staking section is available on the official website, but it is not yet officially open. You can choose to stake in the short, medium or long term to earn returns. The returns for each period are different.

"We are stress testing the federated learning architecture in a complex real-world environment: – Network fluctuations & device disconnections – Large data quality differences – Random signal loss There is only one goal: Ensure that AI can complete training without any

On-chain governance is more than just a voting mechanism; it's essentially "encoding the incentive structure." Which datasets deserve priority? Who has access to model retraining? How is access granted for protocol upgrades? We are testing a new governance mechanism: Token

Every protocol has its own bottlenecks. For DataSoul, the bottleneck isn't computing power, but rather how to efficiently verify a complete data trace without exposing the data's content. This is precisely why we're investing resources in optimizing the ZK proof system—privacy

Today, a user asked us: Can BTC prediction data be sold? My answer is: Under the DataSoul platform's design philosophy and protocol structure, BTC (Bitcoin) prediction content can, under certain conditions, be defined as a tradable data asset, provided the following criteria are

Some people may ask, "Can my medical paper or the new contract code I developed be listed on a trading market?" In fact, many types of data can be listed. We are currently developing a "data detector" for intelligent pre-analysis. We will provide: Chainability Score (0-100)

We verify reality, not store original data. 1/ DataSoul does not record original logs. 2/ All data is processed locally, ZK generates a state hash proof. 3/ What is uploaded to the chain is the proof, not the original content. 4/ It verifies that the result occurred, but does

We tested the system's response to "denying validator access request." The answer is: Nothing is read, There is no fallback path, and There is no so-called "default consent." User sovereignty isn't just a clause in the protocol; It must be truly enforced.

Most blockchain infrastructure is optimized for tokens. We optimize for information itself. This means: – Data flow ≠ token flow – Integrity ≠ ownership – Computation ≠ storage This is a completely different paradigm, designed for knowledge, not just price.

We have completed testing and verification of our multi-chain data bridge. The results confirm the following: State proofs can be seamlessly transferred between Ethereum and Arbitrum Metadata integrity is maintained after serialization Average verification latency is less than 2

Here DataSoul your data can be converted into assets, very interesting stuff

We are simulating malicious attacks in the data market: 1. Tampered datasets 2. Falsified provenance history 3. Fake data with varying payloads but repetitive structure Our detection system, leveraging the ZK fingerprinting mechanism, successfully identified over 94% of

The DataSoul protocol will soon support external model injection. This means: You can deploy custom AI models Combining them with compliant data from the data marketplace Verifiable inference results using zero-knowledge methods This isn't "plug and play," but rather

We tested fault tolerance mechanisms, including: – When federated nodes are offline – When ZK proof generation times out – When mismatches occur in partial state submissions Failure simulations are designed to prevent failures on the mainnet. Each node's "backup path" isn't

We're not testing speed; we're testing trustworthiness. Can a third party verify data behavior without trusting the source? Can model results be adopted without exposing the training set? In the world of data finance, these are the core questions worth answering.

"Show me your architecture diagram." A protocol's true value lies in how it handles real-world problems, not in how much hype it generates. We build quietly, but test loudly."

Auditability isn't an add-on feature; it's a fundamental user right. – If a dataset has a price, its origin must be traceable. – If a model is deployed, its training assumptions must be verifiable. We're building a "trustless, verifiable everywhere" protocol stack.

The value of the sandbox lies in its ability to simulate the risks of real-world deployments. We test for: Denial of service attacks under extremely low network bandwidth Training inputs with malicious characteristics Overload pressure from high-frequency API requests from