Elementalist

For a long time, the on-chain world has been accustomed to simplifying everything into addresses and transaction records.
For protocols, as long as the rules are established, it doesn't seem to matter who is participating.
But as applications increase, people gradually realize that an ecosystem that cannot understand its users is difficult to sustain in the long run.
It is against this backdrop that @bluwhaleai emerged.
It uses AI to understand on-chain behavior, connecting scattered interactions into a more complete usage trajectory, allowing protocols to no longer just face cold data but to

There are many projects now working on perp DEX, but few are truly willing to take responsibility for the trading itself.
Most protocols are more concerned with traffic, incentives, and leaderboards, rather than whether trading execution remains stable under extreme market conditions.
@pacifica_fi gives me the opposite impression.
It doesn't start from a narrative, but from a very practical question: on-chain, can we really create a derivatives trading system that professional traders are willing to use long-term?
It has chosen a hybrid architecture, using off-chain matching to solve latency i

In the current era of the cryptocurrency industry moving towards multichainization, many people experience the same problem: assets are scattered across different networks, making lending, collateralization, and liquidity management tedious and inefficient.
@MultichainZ_ aims to change all that. From the beginning, its core vision has been cross-chain lending, hoping to allow users to smoothly operate assets on any familiar blockchain without worrying about the barriers between networks.
After the mainnet launch, users can now obtain loans by collateralizing stablecoins, NFTs, or even real-wor

Many of the intelligent applications we use daily, from recommendation systems to automation services, often have black-box AI logic behind them, making it difficult for ordinary users to determine whether the outputs are truly trustworthy.
The emergence of @inference_labs is precisely a solution to this practical problem.
This project leverages blockchain combined with zero-knowledge proofs to enable AI reasoning processes and outputs to be verified as operating as expected while maintaining privacy.
This not only enhances the compliance and transparency of AI technology within the industry b

Many people, upon first seeing @Firestarter_AI, might easily mistake it for an already established AI × Web3 project. The core message Firestarter conveys externally isn't complicated; it doesn't focus on making AI give you better suggestions, but rather on a more fundamental question:
Can AI be authorized to act on behalf of users to complete real business actions and execute transactions? This is a severely underestimated gap.
Today, most AI remains at the decision-support level, with final execution still carried out by humans. But once AI enters the execution layer, it inevitably involves

I increasingly agree with a judgment: whether blockchain games can scale up depends not on the speed of user growth, but on retention and re-engagement.
@River4fun's behind-the-scenes @RiverdotInc clearly recognizes this in product design.
River emphasizes clear rules, verifiable results, and genuine interactions between players, rather than simply using rewards to drive behavior.
On-chain settlement makes the gaming process transparent and also helps players form relatively stable expectations of their investments and returns.
Under this structure, players are more like participants in a long

Many so-called AI projects today are essentially about content generation or automation, but the core issue that truly troubles Web3 has never been a lack of content. Instead, it is the persistent absence of genuine understanding of users in the on-chain world.
Wallets are anonymous, behaviors are fragmented, and protocols can only see transactions but cannot understand people. @bluwhaleai chooses to address exactly this layer of problem.
It is not about creating simple data dashboards, but about using AI to aggregate and analyze on-chain and off-chain behaviors, building decentralized and com

Many reasons for the failure of AI × Crypto projects are not actually due to model capabilities, but rather the data itself.
Untrustworthy, non-reproducible, and unverified data sources ultimately lead to AI outputs that cannot be validated, let alone form long-term value on the chain.
@useTria's entry point is precisely here.
It is not about building models or application shells, but focusing on the most overlooked yet critical layer in AI training and inference: structured, verifiable data foundations.
Tria clearly records the source, contribution, and usage relationships of data through on-

Many creators, brands, and communities are pondering a question: what if they could easily convert trust and influence into real economic value?
@Firestarter_AI is precisely the platform that responds to this desire. It allows anyone to create their own digital tokens and stores within minutes, enabling supporters to participate in a fair and transparent manner.
This platform automates the market and trust scoring system, allowing issuers to enter the liquidity market directly without traditional issuance processes, while community members can buy, sell, and interact at any time.
Firestarter a

Many Web3 projects in their early growth stages rely on subsidies and narratives to quickly gather users. However, as the market becomes more rational, this approach becomes increasingly unsustainable.
Teams often know that users are coming, but do not truly understand their real on-chain behaviors and long-term needs.
The key to truly integrating AI into the Web3 ecosystem is not about how flashy the generated content is, but whether it understands the genuine behaviors of users on the chain.
@bluwhaleai focuses precisely on this layer of value, providing clearer and more actionable user prof

In the past, AI services relied more on centralized computing power and closed systems, where users could only trust the results but could not verify the process.
The value of @inference_labs lies in its attempt to change this default assumption, making inference itself a verifiable act rather than just an output.
This approach is influencing industry standards for AI infrastructure, shifting the focus from performance and cost to trustworthiness and composability.
Only when inference results can be securely reused across different systems can AI truly integrate into open networks.
From this p

When we discuss the value of Web3, a key proposition is how to enable ordinary users to easily participate in the development and use of decentralized applications.
@codexero_xyz provides a practical approach to this proposition. As an infrastructure known as the Decentralized Wall Street Engine, CodeXero is built on Sei Network, aiming to allow users to create on-chain applications and interact with financial assets with lower technical barriers.
This is not only an innovation at the tool level but also represents the direction of infrastructure development, transforming functions that were p

Recently reviewing the data from @pacifica_fi, I noticed a very interesting phenomenon: in the Solana perp DEX sector, it has actually reached the top in trading volume.
Although still in the closed testing phase, its daily, weekly, and monthly trading volumes have even surpassed Jupiter, making it one of the most active perpetual contract exchanges on Solana.
Several key factors drive this trading activity: expanding market depth, a variety of order types, and the weekly points reward mechanism that attracts many traders to participate.
The surge in trading volume was also accompanied by a si