OpenServ v3 用 Jev 做推理图决策节点
SERV v3 的 Graph Sharding 把推理图拆成脚本、Jev 决策和受限 LLM 三类节点，每步可审计计费，团队称小模型加 SERV 以约九十分之一成本胜出。
🚀 #SERV – OpenServ: Graph Sharding Unveiled as the Core of SERV v3! 🌐 @openservai just introduced Graph Sharding, the foundation of SERV Reasoning v3, built to make enterprise AI agents predictable, auditable and cheaper to run. 🧠⚙️ 🔥 Latest Stats: 💰 Price: ~$0.0173 📊 Market Cap: ~$13.3M 🔄 Circulating Supply: ~770M SERV 🔒 Max Supply: 1B SERV 🌟 What's new in v3: ✅ Each reasoning graph is split into 3 node types: Script (plain code), Decision (Jev model) and Agent (constrained LLM calls) ✅ The LLM is used only where judgement is needed; rule steps run as code with no inference cost ✅ Every step has defined inputs/outputs, logged time and recorded cost, so decisions can be replayed and audited ✅ Shadow Agents verify the output and PromptGuard guards against injection and prompt leakage ✅ Team-reported benchmark: SERV + a small model beat a frontier model at ~90x lower cost Unique Value vs Competition: Instead of open-ended agent loops, SERV turns English prompts into verifiable pipelines with a cost you can calculate before a request runs. 📅 Catalysts: 🔹 Graph Sharding beta with a select group of early testers, "coming soon" per the team 🔹 Wider enterprise distribution, incl. AWS Marketplace 🛠️ Buy & Trade $SERV: https://www.lbank.com/trade/serv_usdt 🔗 Explore: 🌐 https://openserv.ai 💬 https://t.me/openservai I hold $SERV, NFA #AIAgents #AIcrypto #DeAI