Jared @JaredCoding
Joined August 2019-
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No CGI. No VFX. Just AI 🌊 Made with Higgsfield AI #AIVideo #AITools #Higgsfield
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👍 之前公众号学习过一遍，X 上再学习一遍～ AI 时代的稀缺品 —— 活人感、品味、信任关系、讲故事的能力（背后代表好奇心和叙事能力）
🤔 从单位 token 的效率表现上，OpenCode 对比 Claude Code 孰优孰劣？
@karpathy @proggineer Therefore, we need a tool that can quickly import information and feed it into various llm
我发现 LLM 时代的国内 AI SaaS 跟过去相比好像没什么本质上的变化… - PLG 依然很难成立，无论 KA 还是 SMB 都希望手把手教学； - 标准化是个伪命题，开口就是既要又要，定制软件功能 -> 定制智能体； - 鸡毛蒜皮的费用都要砍半天，还得半卖半送； 也许唯一差别是，Agent 能替代人的故事，给足了管理层降本增效的理由…🤣
目前能想到既能保障时延合理，又能稍微提升可靠性的思路： 1. 主/子 agent 之间适当共享一部分 context 和限制条件，尽量减少隐式决策差异； 2. 主 agent 更换参数量更大的推理模型，稍微牺牲一点时延，增加一致性检查和冲突裁定任务； Devin 之前分享的 insights 深有体会： cognition.ai/blog/dont-buil…
最近在调试多智能体时遇到一个很棘手的问题：如何提升子 agent 输出 meta prompt 时的可靠性？😢 以售前智能客服为例，一般会拆分为这些 agent 角色： - 主 agent：负责最后整合信息输出回复 - 知识检索（RAG）agent - 用户画像解析 agent - 售前 SOP 决策 agent - 语气模仿 agent - …… 由于考虑到回复时延的问题，所有子 agent 都是并行架构，子 agent 之间都是 context 和 prompt 隔离的，因此互相不知道输出。 那么只要某一个子 agent 发生偏移，最后主 agent 整合信息的时候就会产生逻辑冲突… 类比真人客服在回复用户时，工作流（思维链）是串行的： -> 先看看过去聊了什么，用户记录了哪些画像信息 -> 判断用户处于哪个阶段 -> 看看有哪些资料适合发送 -> 想一想用什么语气 -> 打字回复 现在并行相当于有几个真人带着不互通的业务理解在工作： - A 觉得用户在阶段 1 - B 觉得应该发资料 2 - 最终的客服听从各个角色的意见来执行，但很有可能 A、B 是冲突的，最后导致客服“精分”……
@DerekNee 可能不太恰当的形容：人类处理信息的容器从 报纸 -> PC 时代的 web，再从 web -> app，而 devin or manus 形态的 agent 就好比 app 内嵌 web……
@DerekNee 表示赞同，而且当未来基座模型足够强大并成为新的计算系统之后，所有的“应用”都需要向它迁移，目前沙盒的形式只是效率低下的过渡态
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