AI 智能体1 month ago
MemoAIr - Realtime memory for voice AI agents
为语音AI代理提供实时记忆和上下文处理，延迟低至10ms。
@SouravDaaa · X
- End to end personalised memory retrieval in 10ms. Would love some feedback here
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为语音AI代理提供实时记忆和上下文处理，延迟低至10ms。
@SouravDaaa · X
- End to end personalised memory retrieval in 10ms. Would love some feedback here
为人类和AI代理记录和检索决策，追踪完整来源。
@burn2delete · X
一次存储共享记忆，在AI系统和团队中复用。
Repeater22746 · HN
ContextVault – Shared memory layer for your AI and your team
为AI代理提供托管服务，支持持久工作空间、记忆和自动备份。
@hijak01 · X
check out the demo
个人AI助手，有自己的电话、收件箱和计算机，记住你并遵循你的规则。
rohanrecommends · Product Hunt
Zinley Your Personal AI Representative for calls, email, and tasks
Telegram私有AI助手，使用API密钥记住对话
@latent_node · X
Future Memory AI 学习你的周习惯，在忘记前及时提醒。
@Prajwalr04_ · X
Check out what I just built with Lovable!
与AI助手分享加密代码和文本，它会记住对话上下文。
u/clausyn · Reddit
I built Pastepile because I was tired of re-explaining the same context to Claude and Cursor I originally built Pastepile as a fast way to share code, logs, configs, and text without creating an account. While using AI coding tools, I kept running into another problem: every new session meant explaining the same architecture, decisions, debugging history, and project context all over again. So I added an MCP server that lets Claude, Cursor, Windsurf, VS Code, and other MCP clients save us
帮助企业员工保存和获取机构知识与决策的AI助手。
@romanbodnarchuk · X
Check out what I just built with Lovable!
Persistent memory API for AI agents. Hybrid scoring (semantic + recency + importance) that works with LangGraph, AutoGen, and CrewAI. Free to start.
@sunvic567 · X
AI agents are becoming company infrastructure. Their memory is still personal. OrgComputers builds the shared layer underneath them.
@KshitijGer40354 · X
在平台上管理和同步你的AI Agent技能配置，避免供应商锁定。
@matebenyovszky · X
I use multiple providers Claude Code, Cursor, Codex, Hermes, Antigravity etc. and multiple machines. Over time, each one ended up with its own slightly different skills and tools 😰 So I started where a playbook is a portable bundle containing my MCP/OpenAPI servers, skills, prompts, personas and memory, which I can take with me into whichever environment I’m using. I’m still trying to figure out whether this is useful beyond my own workflow. Ideas? 🤔 🍎 Source / open source on Github: matebenyovszky/agentplaybooks I also built an npm CLI that audits a project and syncs the same skills and MCP definitions across harnesses.