在等待 B20 代币标准上线时，先来看看已经有哪些项目宣布支持 Beryl 升级带来的全新 B20 原生代币标准。 ▫️代币发行/管理/发现： @clanker_world @rwagmicom @NovaHub_Rwa @BerryFiBase @OrlixAI @morvlabs @BrixBase ▫️交易工具： @o1_exchange @gmgnai @Debot_Official @alphai_snipe ▫️B20 Agent 基础设施： @virtuals_io：构建 B20 Agent。 @blueagent_：通过 AI Agent 实现 B20 代币的发行、分析与监控。 @ClawdOS：B20 的 Agent 原生操作层。 B20 代币标准具备原生发行、内置合规、角色权限、转账策略等能力，主要面向稳定币、RWA、股票等现实世界资产发行方以及长尾代币发行场景设计，这是 Base 构建全球链上经济愿景中的关键一环。 期待 B20 能进一步推动 @base 生态的增长与繁荣。Base 中文台将持续关注 B20 生态的进展！

best writeup ive read on @dphnAI elucidates the different value props in detail & the connection to @AskVenice -- • provider of uncensored models for Venice • peer to pool inference network w/ customized verification stack • 100% of revenues directed toward $POD buybacks

Venice’s privacy + AI angle made the VVV trade easy to understand. But there’s another, larger AI shift it's set to capture: the move from training to inference. Training gets the attention but inference costs are recurring and ever-growing, scaling alongside users and agents. That is becoming one of AI’s biggest bottlenecks: > Cerebras’ IPO landed 20x oversubscribed as investors piled in for exposure to its fast inference chips > Anthropic unexpectedly cut usage limits, reports of Claude being degraded spread everywhere, then Anthropic disclosed usage had GROWN 80x more than expected > So, they then partnered with SpaceXAI to use the ENTIRE compute capacity of the Colossus 1 data center > Now, Claude usage limits are rising again, and free API credits are coming in June That is the broader tailwind behind Venice: its ecosystem is a gateway to endless inference. VVV gives access to Venice Pro + can be staked to get perpetual inference via DIEM. Projects are already stockpiling DIEM to service inference for their own platforms, agents, and users. Then there’s Dolphin, which built the default Venice Uncensored model and runs a distributed inference network using idle consumer GPUs. Its token POD can be used for inference payments and xPOD stakers get daily inference allocations across the network’s models. David Christopher expects we’ll see more projects like this, serving decentralized inference rather than training.