Tonight, using my own @ReiNetwork0x UNI01 (a dedicated smart agent for on-chain project research), I asked:
Help me conduct an in-depth research on $alpha, the official Twitter is @netprotocolapp, the official website is: , take a look at the secondary market's chip distribution and investment opportunities.
UNI01 replied:
Net Protocol is building a decentralized, on-chain native token issuance and social empowerment platform, supporting users to participate in project incubation through an Upvote mechanism, forming a "community as VC" model. Currently in the Expansion Early stage, opportunities are limited but have narrative observation value. (For more analysis details and overall research structure it provided, see the screenshot)
The accuracy of UNI01's project summarization and data retrieval made me feel very impressed; it is on par with any on-chain research assistant with over 2 years of experience around me. Moreover, it can work 24/7 without rest 🤣
The only thing it didn't research was: "For every token launched on the net platform, 50% of the transaction fees and voting costs must be used to buy $alpha, creating a positive buying pressure." This indeed requires human effort to piece together information and details from several places.
After digesting the token project analysis comprehensive model V1 that I repeatedly fed to ChatGPT 5, Claude, Gemini, Manus, and Flowith, UNI01, after training, has shown the ability to collect information, structure data, organize analysis, observe, and provide action suggestions when initially processing the projects I provided. I am very satisfied with it as my assistant.
Continuing to optimize a few points:
1. Continuously train it to think associatively like a human;
2. Teach it to analyze dynamic data, that is, after structuring on-chain data from different time periods, analyze the investment insights brought by derived factors, providing the owner with more angles of thought to help substantiate more possibilities;
3. Feed it more knowledge bases and project analysis case libraries.
【 $REI ：一颗有记忆、能进化的链上大脑，新技术范式 vs 巨鲸结构的对赌】
要承认，@ReiNetwork0x 是一个典型的“底层范式重写 + 二级结构很脆弱”的对赌局。
1. $REI要做什么？
它不仅仅只是另一个 #AI 叙事包装，而是真的在尝试做：把“记忆 / 推理 / 模型协作”重新拼成一块“链上可进化的大脑”。
它想解决当下 LLM （Large Language Model，大型语言模型）的几个硬伤：
会忘记（无持续记忆）、会幻觉、不属于你（不可拥有/迁移）。
@ReiNetwork0x 给出的解决方式是：那让它边用边学、记得住、还能资产化。
2. 一句话版：
Rei 核心定位是一个对标 OpenAI 和Anthropic 的基础研究 AI 实验室，并非另一个 LLM，而是一个革命性的“合成大脑”架构，名为“Core”。
Core 通过三大核心组件和三大技术突破，从根本上解决当前AI在记忆、学习和可靠性方面的瓶颈。
Crypto 赋予它三件利器：链上日志=可验证、资产化=可交易复用、开放激励=拉开发者补专用能力。
所以，散户们平时完全没机会接触到早期基础研究实验室的股权，现在公开流动的 $REI 就是稀缺入口，当然也是一个风险敞口。
3. 代币/经济模型：
优点：高初始流通，后续解锁炸弹少；
缺点：但早期集中低成本筹码巨大；
最大缺口：没有对 $REI 价值捕获说清楚，是调用费？创建 Unit 抵押？费用回购？暂时都没明晰。
4. 链上 & 数据看点：
1. 前 250 地址 >81% 供应量，不自欺欺人，这是核心系统性风险；
2.月度运行：几十 TB 级数据写入、六十多万 Patterns、接近 99% Sync Rate，说明真在跑，不是 PPT。
3.市值区间：中腰部 AI 概念，技术叙事溢价 + 持仓结构折价。
5. 近似对标项目的差异：
Bittensor @opentensor：激励算力/模型贡献的开放网络，侧重外部贡献的激励；REI 更像 架构+记忆+推理的一体化大脑。
Autonolas @autonolas：偏多代理协作与去中心化服务编排；REI 更侧重“单脑持续学习 + 内部知识结构”。
LangChain @LangChainAI：开发者工具/编排框架，没有长期记忆或激励；REI 则绑定记忆-推理-激励。
6. 风险点（不粉饰）：
1. 持仓集中：巨鲸筹码成本低，任何风吹草动就是挤兑预演。
2. 匿名团队：现在是“代码=信誉”，一旦迭代节奏断层，信任马上反噬。
3. 技术兑现：推理时学习/幻觉降低需要第三方测评，不靠自述。
4. 生态验证：外部真实业务应用场景、第三方 Units 规模都要时间。
5. 代币价值捕获机制现在是“空框”阶段。
7. 当前重点关注指标：
1⃣筹码集中度：Top 250 占比能不能往 75% 以下走；巨鲸买入和卖出。
2⃣开发进展：Core 版本持续（月或双月频率），GitHub 周提交保持跑（≥30 级别）。
3⃣ 生态应用：外部 Units 数量持续上升；真实 API/SDK 调用。
4⃣经济模型刺激：费用/抵押/租赁一旦落地，看是否产生真实代币消耗或锁仓。
8. 我的进入和持仓策略：
配合nansen工具已完成：链上监控，重点盯头部地址，CEX 轨迹 & LP 流动性变动；0.19U时建仓25%
这两天base上AI+crypto项目整体回调时，在0.11U时已补仓到50%
后续我会继续盯：持仓前100集中度 / Units 真实生态 / 费用消耗&激励&锁仓模型 / 幻觉实测 / 巨鲸资金流入/流出。
核心看法：
这不是简单的再做一个 AI 应用，而是新基础范式和新尝试的实验室，同时也是有一部分看项目方格局的筹码结构定时炸弹。
若能跑通， 市场估值上限会远高于普通 AI x Crypto 项目；base上也开始陆续出现很多这类这类“底层重构 + “风浪越大鱼越贵”结构风险”AI+crypto项目，保持跟踪，DYOR！
关于 $REI 汇总的一些资料贴：
1.@blocmatesdotcom 《从零开始重新思考大语言模型》
2. @bes_______《Rei 论文》
3.@0xreitern的《@ReiNetwork0x 的基本资源/资料指南》
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