Mr. Stunt 🚗 🚘

AI infrastructure is also undergoing a shift, and @dgrid_ai is positioning itself within this transition. By introducing a decentralized compute layer, dGrid distributes AI workloads across a network, improving scalability, resilience, and cost efficiency. Key areas of focus: • Decentralized GPU and compute sharing • Efficient resource allocation for AI workloads • Reduced reliance on centralized providers As AI demand accelerates, compute access is becoming a core constraint. dGrid addresses not just performance, but accessibility and efficiency at scale. It’s still early, but turning underutilized compute into a shared global resource could unlock meaningful opportunities for builders and researchers. A strong start to a promising day. @permacastapp highlights a recurring issue in DAOs: knowledge isn’t lost through deletion, but through fragmentation. Proposals may exist in governance forums, while the reasoning behind them is dispersed across posts, calls, and documents. Critical context often ends up buried in threads or screenshots. As information becomes fragmented, reconstructing decisions grows difficult. New contributors face steep learning curves, active members lose continuity, and the connections between ideas gradually weaken. The data remains, but its clarity, context, and long term value diminish over time.