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RT @indie_maker_fox: 🎉 RedReach的Redreach Outbound功能 之前分享过RedReach工具，主要功能是监听跟你的产品有关的Reddit帖子。现在RedReach出了一个新功能Outbound，通过浏览器插件帮你一键发送私信。有黑五…
🔥 The best AI SaaS boilerplate - https://t.co/VyNtTs0jSX 🚀 The best directory boilerplate with AI - https://t.co/wEvJ1Dd8aR 🎉 https://t.co/bh1RxeERuY & https://t.co/zubXJCoY92 & https://t.co/tfQf8T7gGF
I’m tagging @robinhanson to read my mole bro paper given his prior has updated to the point where I think it’s worth considering as an alternative to the ET hypothesis, which doesn’t sit well with anyone who believes UAPs have crashed https://t.co/0VVeaO2qFE
Opening portals to VR without headsets at @portalvr_io. Problems soluble, potential to improve invariant.
This is the way to go Instead of changing to shitty alternatives like paper straws, wooden cutlery, and cassava bags that disintegrate and are all inferior to plastic You gotta make something that's good for the environment AND as good or better than plastic (cheap and strong) Also bacteria that break down plastic seems to be promising That's why I hate the performative eco stuff, it makes our lives worse when instead we should be looking at making our lives better while caring for the environment And you do that by inventing new technology instead of limiting the AC and making us suck through paper straws
🇪🇺https://t.co/NdorAWrhrB @euacc 📸https://t.co/lAyoqmT9Hv $125K/m 🏡https://t.co/1oqUgfDEsx $40K/m 🛰https://t.co/ZHSvI2wRou $38K/m 🌍https://t.co/UXK5AFra0o $16K/m 👙https://t.co/RyXpqGvdBB $14K/m 💾https://t.co/M1hEUBB6da $6K/m
Damn bandits RPGd a family member. No corpse nothing all burnt to crisp while he was inside a makseshift mosque in his station in Kebbi. Been losing family since 2012 man
Founder | Author | Speaker Building @beltstripe. Healtech/EdTech/Agric I'm Not The Man Of Your Dreams. Your Imagination Wasn't This Great.
@akshay_pachaar 最后，感谢你花时间阅读了这篇推文！ 关注@Yangyixxxx ，分享AI信息，商业洞察与增长实战 如果你喜欢这篇内容，也请点赞并转发第一条推文，把有价值的内容分享给更多人~
Believing is seeing
RAG 并非终点，AI Agent 的记忆才是未来方向。 用最简单的方式拆解下这个进化过程： RAG (2020-2023)： - 单次检索信息，生成回复 - 没有决策，只是提取和回答 - 问题：经常提取到无关信息 Agentic RAG： - Agent 决定是否需要检索 - Agent 选择哪个数据源来查询 - Agent 验证结果是否有用 - 问题：仍然是只读，无法从交互中学习 AI 记忆： - 读取和写入外部知识 - 从过去的对话中学习 - 记住用户偏好、历史背景 - 实现真正的个性化 这个心智模型很简单： ↳ RAG：只读，一次性 ↳ Agentic RAG：通过工具调用实现只读 ↳ Agent Memory：通过工具调用实现读写 Agent 记忆的强大之处在于：Agent 现在可以“记住”事情了——用户偏好、过去的对话、重要日期，所有这些都可以存储起来，供未来交互使用。 这解锁了更大的可能性：持续学习。 Agent 不再被冻结在训练时，而是可以从每次交互中积累知识，随着时间的推移不断改进，无需重新训练。 记忆是静态模型和真正自适应 AI 系统之间的桥梁。 当然，并非一帆风顺。 记忆引入了 RAG 从未有过的挑战：记忆损坏、决定忘记什么，以及管理多种记忆类型（程序性、情景性和语义性）。 从头开始解决这些问题很难。 如果你想给你的 Agent 赋予类似人类的记忆，可以看看 Graphiti，一个用于构建实时知识图谱的开源框架。 链接在下一条推文中！
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