Genpulse

Cohort 3 Demo Day is here! Watch the recording to see what these 6 elite teams are building at the frontier of @Solana tech. 4:31 - @ZyntaFinance 11:12 - @GenpulseAI 17:59 - @seedplex_io 22:08 - SendAI (@yashhsm) 28:02 - @xitadel_fi 35:19 - @pyefinance

this man sends Send AI 🤝@GenpulseAI

What We Learned from Builders who won the @Aura_Sci Sciencethon II -- Insights from @GenpulseAI @Contribocomes and @_ideosphere on how they started building in DeSci Lessons from the field: The Top Steps to Apply and Start Building 1. Start with the Research Friction Point This step is about identifying real-world scientific bottlenecks whether in semantics, data access, or coordination before designing solutions. “The hard part isn’t tech. It’s semantics. People describe the same problem 10 different ways.” Rei – Ideosphere 2. Create a Verifiable Contributor System If you want contributors to show up, you need more than open access. Participation requires visible ownership paths, tracked work, and recognition systems. “One of the hardest lessons we’ve learned is that contributors don’t show up unless you show them a path to ownership or recognition.” Aleksa – Contribo 3. Match Scientific Logic with DAO Incentives Scientific rigor and token-based coordination need separate mechanisms. Split governance between peer review and community voting to preserve both quality and engagement. “We split decision-making between peer-reviewed panels and token-based community votes. It’s messy, but it works better than purely technical governance.” Aleksa – Contribo 4. Visualize the Data Journey Researchers need to see what happens to their data. Mapping every step collection, storage, processing, usage builds trust before tools are even built. “You don’t need a full product. Prototype the data journey first. Make it visible. Make it verifiable.” Wendy – Genpulse 5. Test Interoperability Early Your tools should integrate with the rest of the DeSci ecosystem. Avoid closed systems build with composability in mind from day one. “We use attestations from DAO members and verified researchers. Those act as weights in our proposal ranking system.” - Ideosphere 6. Use Embeddings to Solve Semantic Chaos AI is essential for mapping scientific overlap across messy language. Embeddings help cluster similar proposals, datasets, and intents even when phrased differently. “We had to train models to detect conceptual overlaps.” Mariana – Ideosphere