The discussion primarily revolves around Pavel's proposal for a web3/DeFi-native modelling project, which aims to leverage the potential of web3-native intelligence for decision support tools. The project is based on the premise that current intelligence tools do not fully exploit the nature of web3, and instead, translate approaches from web2/TradFi, which falls short of providing deep insight. Pavel suggests that a web3 protocol lives in two domains: onchain transactions and community interactions, and understanding this can lead to better modelling and prediction of its evolution.

The project is divided into two stages. The first stage involves building a custom DeFi-focused dataset for fine-tuning a large language model for multidimensional affect analysis. This dataset will be assembled from Twitter, Discord, and Discourse discussions and annotated with labels reflecting the mood of each post. The second stage involves deep learning experiments on a vault-level model of onchain transactions using a Spatio-Temporal Graph Network (STGN). This model can be used for prediction, classification, transaction pattern forecasting, fraudulent activity detection, and other applications. It can serve as a decision support tool for various stakeholders including DAOs, investors, community members, and users.

However, the proposal has received mixed responses. While Raho and Fig suggested that the project might be better suited as a grant proposal, they also noted that the proposal doesn't mention how it could benefit specific DAOs like Aave. In response, Pavel argued that the project should ideally be a cross-protocol initiative, serving the interests of everyone in DeFi. He suggested that major players like Aave, Compound, Maker, and Uniswap could chip in. He estimated a budget of around 200k for the first 3-4 months, mainly for one-time investments like building a DeFi-native mood analysis dataset or cloud computing expenses. The discussion is ongoing, and a formal proposal is yet to be submitted.