In the Web2 era, knowledge graphs are the 'magic tool' for efficiently finding information — Baidu relies on it for precise answer matching, Feishu uses it to facilitate team collaboration, and CNKI relies on it to trace academic sources, transforming fragmented information into 'reusable and relational' assets, eliminating the need to 'search blindly' through information.

However, in Web3, this 'magic tool' malfunctions: on-chain data resembles a jigsaw puzzle; checking DeFi risks requires navigating Etherscan, Discord, and DApps, and information can even contradict; someone claims 'a certain NFT will rise' but cannot verify it; high-quality strategies sent out receive no feedback, and no one is willing to do it again. Web3 lacks information, but it's missing a closed loop of 'data → insights → knowledge.'

At this point, @Treehouse Official is crucial: it is not just a Web3 information platform, but it 'transplants' Web2 knowledge graph capabilities into Web3, establishing a preliminary on-chain knowledge graph with a three-layer architecture, allowing Web3 knowledge to be 'structured, comprehensible, and actionable.'

Treehouse three-layer architecture: from chaotic data to active knowledge.

Treehouse logic is straightforward, addressing one pain point at a time, as solid as building a house:

1. Structural Data Layer: Transforming tangled on-chain data into easily understandable visual modules. Actively capturing data, filtering out ineffective information, classifying by 'assets, positions, returns, risks', for example, using pie charts to view cross-chain asset proportions, radar charts to mark high-risk positions, making machine data into information that ordinary people can understand.

2. Content Expression Layer: Turning cold data into knowledgeable insights. Supporting users to post analysis, such as writing DeFi liquidation warnings from TVL declines, discussing NFT resilience from institutional holdings, with content strongly bound to underlying data, where clicking on data reveals real-time curves, and knowledge is verifiable and traceable.

3. Incentive Annotation Layer: Using TREE to enable high-quality knowledge to continue growing. When analysis is accurate and helps people avoid pitfalls, earn TREE; when content is good and sparks discussions, provide more rewards; long-term focus on a certain field to become an 'expert', with greater rewards and follower growth, forming a cycle of 'producing quality content → earning rewards → generating more quality content.'

Not just a platform: Treehouse creates the Web3 knowledge map.

If the architecture continues to optimize, Treehouse will become the 'most useful, most interactive, and most traceable' knowledge infrastructure in Web3: checking ecological opportunities will not require piecing together tools but directly obtaining a 'data + analysis + verification' package; it can supplement data and add insights, with knowledge becoming more accurate the more interactive it is; each piece of knowledge can check its source and creator, eliminating the fear of unfounded claims.

Participating in the Treehouse ecosystem is not just attending events; it’s helping Web3 draw an 'active knowledge map' — organizing fragmented knowledge into a searchable and modifiable manual. In the future, when Web3 users check opportunities or assess NFTs, they might first think of Treehouse, which is its value, and $TREE is the core fuel driving the construction of the knowledge system.

@Treehouse Official #Treehouse $TREE