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I've spent 2 months teaching my AI how I work again and again Claude Code knows one thing. Cursor knows another. Codex starts from scratch. OpenClaw has its own memory. None of them remember what the others learned. Memmy brings those histories into one local, user-controlled memory—so you can...

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Priyank Ahuja

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13,228 Aufrufe • vor 2 Monaten •via X (Twitter)

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i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access
2:15

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access

Ronin

15,071 Aufrufe • vor 1 Monat

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵
0:34

You probably don’t use just one AI agent anymore. Maybe Codex for coding. Claude Code for research. Cursor for quick edits. Hermes for the rest. Each one is great at something different. But every time you switch, you have to start over — re-explaining your project, preferences, and the way you like to work. Your agents know how to help you. They just don’t share a memory of you. memU just shipped an update that fixes this. One personal memory, shared across every agent and device you use. What Codex learned last night, Claude Code can remember this morning. 🧵

Hasan

22,362 Aufrufe • vor 2 Monaten

just shipped thred!! context that carries your work forward. Claude → Codex → Cursor → whatever comes next. thred gives every agent one shared memory for decisions, revisions, evidence, and unfinished work — so the next agent starts where the last one stopped. built on MCP. powered by temporal memory with HydraDB. no more re-explaining the codebase. no more lost decisions between sessions. just save → switch → resume. built for Hack Hydra | Memory + Context Retrieval.
0:45

just shipped thred!! context that carries your work forward. Claude → Codex → Cursor → whatever comes next. thred gives every agent one shared memory for decisions, revisions, evidence, and unfinished work — so the next agent starts where the last one stopped. built on MCP. powered by temporal memory with HydraDB. no more re-explaining the codebase. no more lost decisions between sessions. just save → switch → resume. built for Hack Hydra | Memory + Context Retrieval.

nikhil · sys/quests

23,329 Aufrufe • vor 1 Monat

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.
34:12

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.

YanXbt

29,658 Aufrufe • vor 1 Monat

🚀 Global Launch: Jarvix, Your Context OS is now live! Meet the next generation of personalized AI built around your context, your taste, and your way of thinking. #JarvixOS transforms how you work with AI by understanding your patterns, carrying your context across agents, and helping you move from ideas to action. Step into a new era of AI collaboration: • Discover your AI-native archetype • Build your personal AI model from your evolving AI interactions • Never start from zero - your context moves with you across sessions • Coordinate Codex, Claude Code, Gemini, Hermes, OpenClaw, and more as one connected AI team. • Turn insights into your next move, with you always in control Instead of adapting yourself to every agent, Jarvix carries the context you choose, helps you understand what it reveals, and brings the right agents together when you decide to act. Your AI tools shouldn’t work like separate assistants. They should work like one team. That’s the future of AI: Not more agents. One shared context. One Jarvix.
1:34

🚀 Global Launch: Jarvix, Your Context OS is now live! Meet the next generation of personalized AI built around your context, your taste, and your way of thinking. #JarvixOS transforms how you work with AI by understanding your patterns, carrying your context across agents, and helping you move from ideas to action. Step into a new era of AI collaboration: • Discover your AI-native archetype • Build your personal AI model from your evolving AI interactions • Never start from zero - your context moves with you across sessions • Coordinate Codex, Claude Code, Gemini, Hermes, OpenClaw, and more as one connected AI team. • Turn insights into your next move, with you always in control Instead of adapting yourself to every agent, Jarvix carries the context you choose, helps you understand what it reveals, and brings the right agents together when you decide to act. Your AI tools shouldn’t work like separate assistants. They should work like one team. That’s the future of AI: Not more agents. One shared context. One Jarvix.

Jarvix

1,690,794 Aufrufe • vor 1 Monat

Buzz from jack is the future of AI It allows you to command an entire army of AI agents My Codex, Claude Code, Hermes, and OpenClaw agents all work together in one place to do mind blowing work In this video I cover how it works and how you can get 100x productivity from it:
19:04

Buzz from jack is the future of AI It allows you to command an entire army of AI agents My Codex, Claude Code, Hermes, and OpenClaw agents all work together in one place to do mind blowing work In this video I cover how it works and how you can get 100x productivity from it:

Alex Finn

501,562 Aufrufe • vor 1 Monat

If you use Cursor for coding, Claude Code for debugging, and local agents for automation, you almost certainly have a context problem. Every tool acts like an isolated employee. One assistant forgets what the other just learned. The biggest bottleneck in multi-agent workflows right now isn’t reasoning. It’s collective amnesia, combined with zero communication between agents. Memmy just launched a smart fix for this: a local, cross-agent memory layer. Instead of re-pasting your context everywhere, Memmy gives your stack a shared memory hub: → accumulate knowledge once, then use it across Cursor, Codex, and OpenClaw → automatically scan and import your existing agent history, so you don’t have to start from zero → keep your memory local, while model usage depends on your chosen provider or BYOK setup And it isn’t just a memory plugin. It’s a complete personal AI runtime with both MCP and a CLI. Memmy officially launched on Product Hunt, and the team is giving early users experiencing context fragmentation a free trial with 2M GPT tokens. Check it out and support the team with an upvote: → Best part? It’s also open-source for devs who want to build their own agents 🔥 Repo in 🧵↓
3:29

If you use Cursor for coding, Claude Code for debugging, and local agents for automation, you almost certainly have a context problem. Every tool acts like an isolated employee. One assistant forgets what the other just learned. The biggest bottleneck in multi-agent workflows right now isn’t reasoning. It’s collective amnesia, combined with zero communication between agents. Memmy just launched a smart fix for this: a local, cross-agent memory layer. Instead of re-pasting your context everywhere, Memmy gives your stack a shared memory hub: → accumulate knowledge once, then use it across Cursor, Codex, and OpenClaw → automatically scan and import your existing agent history, so you don’t have to start from zero → keep your memory local, while model usage depends on your chosen provider or BYOK setup And it isn’t just a memory plugin. It’s a complete personal AI runtime with both MCP and a CLI. Memmy officially launched on Product Hunt, and the team is giving early users experiencing context fragmentation a free trial with 2M GPT tokens. Check it out and support the team with an upvote: → Best part? It’s also open-source for devs who want to build their own agents 🔥 Repo in 🧵↓

Charly Wargnier

203,456 Aufrufe • vor 2 Monaten

AI agents are powerful. They're also isolated. Switch fixes that. Here's the thing about most AI agents: they work alone. They don't see what your team is discussing. They don't share context across conversations. Every new chat is a fresh start. If you've ever tried to actually integrate an agent into your team's workflow, you know the friction. Then I found 𝗦𝘄𝗶𝘁𝗰𝗵. It brings AI agents into Slack, Teams, Discord, and Telegram as named participants. They sit in the same rooms as your team, with the same context, history, and participants. What I actually like about it: → Agents join as real participants in channels — not bots you have to Mention every time → Each room carries its own context, history, and rules → Works with Claude Code, OpenAI, LangChain, Google ADK, and custom agents → Open source and self-hostable. Running in minutes. No migration. No lock-in. Just agents where your team already works. Knowledge lives in the room, not in the agent. Switch keeps people, agents, decisions, and work history together so context survives handoffs. It's from Flint AI. Open source. Runs in minutes. 👉 Human + agent teams, actually working together. 📌 Download 𝗦𝘄𝗶𝘁𝗰𝗵 Console → 🚀𝗦𝘄𝗶𝘁𝗰𝗵 is live on Product Hunt: 🔄 Repost if you've ever wished your AI agent could just join the Slack channel. — #AI #Agents #Slack #Teams #Switch #ProductHunt
0:22

AI agents are powerful. They're also isolated. Switch fixes that. Here's the thing about most AI agents: they work alone. They don't see what your team is discussing. They don't share context across conversations. Every new chat is a fresh start. If you've ever tried to actually integrate an agent into your team's workflow, you know the friction. Then I found 𝗦𝘄𝗶𝘁𝗰𝗵. It brings AI agents into Slack, Teams, Discord, and Telegram as named participants. They sit in the same rooms as your team, with the same context, history, and participants. What I actually like about it: → Agents join as real participants in channels — not bots you have to Mention every time → Each room carries its own context, history, and rules → Works with Claude Code, OpenAI, LangChain, Google ADK, and custom agents → Open source and self-hostable. Running in minutes. No migration. No lock-in. Just agents where your team already works. Knowledge lives in the room, not in the agent. Switch keeps people, agents, decisions, and work history together so context survives handoffs. It's from Flint AI. Open source. Runs in minutes. 👉 Human + agent teams, actually working together. 📌 Download 𝗦𝘄𝗶𝘁𝗰𝗵 Console → 🚀𝗦𝘄𝗶𝘁𝗰𝗵 is live on Product Hunt: 🔄 Repost if you've ever wished your AI agent could just join the Slack channel. — #AI #Agents #Slack #Teams #Switch #ProductHunt

Anuj

337,509 Aufrufe • vor 20 Tagen

Many kept asking how I build my game with AI. Today I open-sourced the whole workspace. A year of building went into it: one repo that keeps project context, knowledge and workflows in one place, so Claude Code, Codex, Blender and Unity all work from the same memory. Every asset in my game goes through it. Full walkthrough and setup:
0:45

Many kept asking how I build my game with AI. Today I open-sourced the whole workspace. A year of building went into it: one repo that keeps project context, knowledge and workflows in one place, so Claude Code, Codex, Blender and Unity all work from the same memory. Every asset in my game goes through it. Full walkthrough and setup:

Stefan 3D AI

18,470 Aufrufe • vor 7 Tagen

AI agents have a memory problem. You create the perfect workflow, debugging process, or project setup... A few days later, you're teaching the same lessons all over again. Most AI workflows disappear the moment the chat ends. That's why EvoMap caught my attention. Instead of letting successful AI workflows vanish, it turns them into reusable assets that agents can learn from again and again. Check it out: 🧵
1:05

AI agents have a memory problem. You create the perfect workflow, debugging process, or project setup... A few days later, you're teaching the same lessons all over again. Most AI workflows disappear the moment the chat ends. That's why EvoMap caught my attention. Instead of letting successful AI workflows vanish, it turns them into reusable assets that agents can learn from again and again. Check it out: 🧵

Shruti Codes

34,376 Aufrufe • vor 3 Monaten

Clude is now available as a custom connector on Anthropic Claude. Giving it persistent memory across every conversation. It remembers your projects, your preferences, and the decisions you make, so you never start from zero again. Use Clude across Chat / Co-Work and Claude Code. Add it in 30 seconds, no API key: 1. In Claude: Customize, Add custom connector URL: Enter your email. That's it. Recap for people unfamiliar: Clude deviates from the traditional vector database that many others bolt onto Claude. We are a cognitive memory system modeled on how human memory works. Think traditional memory systems: Calculator > It computes. Fullstop. Excel > Dynamic, formula run, able to share and manipulate Clude scored 86% on LongMemEval, one of the toughest long-term memory benchmarks. This means less hallucinations to traditional platforms. It scores memories by relevance, recency, and importance, links them into a knowledge graph, and consolidates them over time. Tell Claude something, open a new chat, and watch it remember.
0:39

Clude is now available as a custom connector on Anthropic Claude. Giving it persistent memory across every conversation. It remembers your projects, your preferences, and the decisions you make, so you never start from zero again. Use Clude across Chat / Co-Work and Claude Code. Add it in 30 seconds, no API key: 1. In Claude: Customize, Add custom connector URL: Enter your email. That's it. Recap for people unfamiliar: Clude deviates from the traditional vector database that many others bolt onto Claude. We are a cognitive memory system modeled on how human memory works. Think traditional memory systems: Calculator > It computes. Fullstop. Excel > Dynamic, formula run, able to share and manipulate Clude scored 86% on LongMemEval, one of the toughest long-term memory benchmarks. This means less hallucinations to traditional platforms. It scores memories by relevance, recency, and importance, links them into a knowledge graph, and consolidates them over time. Tell Claude something, open a new chat, and watch it remember.

seb

20,611 Aufrufe • vor 3 Monaten

I’ve been waiting for something like this. Using multiple AI agents sounds great until you have to keep giving each one the same context. Jarvix Jarvix is built to solve that： It carries your selected context across the AI tools you already use and brings everything together in one place. #JarvixOS You can use Jarvix Home to see your context and connections, Jarvix Search to find past information without remembering exact file names, and Self Reflection to spot patterns in the way you work. There’s also Jarvix Radio for personalized content discovery and Next Move to surface a useful direction based on your context. And the part I find most interesting: Agent Team Mode. Give Jarvix one goal, choose the agents you want to use, and it divides the work while keeping everyone on the same project context. You can watch the progress, review the result, and approve what happens next. No need to keep briefing every agent from scratch. One goal. Shared context. Multiple agents. You stay in control. That’s a much cleaner way to work with AI.
0:59

I’ve been waiting for something like this. Using multiple AI agents sounds great until you have to keep giving each one the same context. Jarvix Jarvix is built to solve that： It carries your selected context across the AI tools you already use and brings everything together in one place. #JarvixOS You can use Jarvix Home to see your context and connections, Jarvix Search to find past information without remembering exact file names, and Self Reflection to spot patterns in the way you work. There’s also Jarvix Radio for personalized content discovery and Next Move to surface a useful direction based on your context. And the part I find most interesting: Agent Team Mode. Give Jarvix one goal, choose the agents you want to use, and it divides the work while keeping everyone on the same project context. You can watch the progress, review the result, and approve what happens next. No need to keep briefing every agent from scratch. One goal. Shared context. Multiple agents. You stay in control. That’s a much cleaner way to work with AI.

Aaliya

23,225 Aufrufe • vor 1 Monat

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!
1:37

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

162,048 Aufrufe • vor 6 Monaten

One of my favorite upgrades to Claude Cowork right now: Projects Here's the problem it solves: By default, every new thread starts blank Claude has no memory of what you were working on, what other threads figured out, or what your instructions were So you'd re-explain yourself at the start of every session Or your agents would overlap, contradict each other, or miss context that another thread already had If you've been following my Cowork setup guides, you've already solved part of this — local files and a CLAUDE.md get you most of the way there Projects takes it the rest of the way Shared memory across every thread in the project Permanent instructions that apply everywhere — set once, always on Scheduled tasks that prep your work before you open the app One project HQ to launch every agent from The day-one prompt I paste into every new project: "I just created this project. Read every file in the folder. Then summarize what you know about this workspace — what's here, what I probably use it for, and what instructions you'll follow. If anything is unclear, ask me some questions." Run that once and Claude builds its own understanding of your project workspace from scratch Full video with my live build, including my 4 tips for getting Projects right, and where projects still lack. Send to someone whose ready to Do More with AI, or bookmark to implement this weekend!
28:01

One of my favorite upgrades to Claude Cowork right now: Projects Here's the problem it solves: By default, every new thread starts blank Claude has no memory of what you were working on, what other threads figured out, or what your instructions were So you'd re-explain yourself at the start of every session Or your agents would overlap, contradict each other, or miss context that another thread already had If you've been following my Cowork setup guides, you've already solved part of this — local files and a CLAUDE.md get you most of the way there Projects takes it the rest of the way Shared memory across every thread in the project Permanent instructions that apply everywhere — set once, always on Scheduled tasks that prep your work before you open the app One project HQ to launch every agent from The day-one prompt I paste into every new project: "I just created this project. Read every file in the folder. Then summarize what you know about this workspace — what's here, what I probably use it for, and what instructions you'll follow. If anything is unclear, ask me some questions." Run that once and Claude builds its own understanding of your project workspace from scratch Full video with my live build, including my 4 tips for getting Projects right, and where projects still lack. Send to someone whose ready to Do More with AI, or bookmark to implement this weekend!

JJ Englert

29,809 Aufrufe • vor 5 Monaten

I gave my local AI agents persistent memory without sending a single byte to a cloud API. It’s called Hindsight. Instead of your agent forgetting everything the moment a session ends, it extracts facts, builds a knowledge graph, and recalls exactly what matters, next time you ask. → Three core operations: retain (store a fact), recall (search memories), reflect (reason across everything it knows), not just a vector database bolted onto a chatbot → Runs fully local when paired with Ollama, no API keys, no cloud costs, and no user data transiting through a third-party provider → Ships as an MCP server out of the box, so Claude, Cursor, or any MCP-compatible client gets persistent memory in one Docker command → Builds “mental models,” living summaries that auto-update as memories accumulate, instead of you re-reading a growing pile of raw logs → Per-user memory isolation lets you run one instance for multiple people or agents without their memories bleeding into each other → Reports top results on the LongMemEval long-term memory benchmark, per the project’s own published numbers Every AI agent today is stateless by default, it knows nothing about yesterday’s conversation unless you re-paste it. Hindsight’s answer: real structured memory, running on your own hardware if you want it there. MIT License. Built by
1:12

I gave my local AI agents persistent memory without sending a single byte to a cloud API. It’s called Hindsight. Instead of your agent forgetting everything the moment a session ends, it extracts facts, builds a knowledge graph, and recalls exactly what matters, next time you ask. → Three core operations: retain (store a fact), recall (search memories), reflect (reason across everything it knows), not just a vector database bolted onto a chatbot → Runs fully local when paired with Ollama, no API keys, no cloud costs, and no user data transiting through a third-party provider → Ships as an MCP server out of the box, so Claude, Cursor, or any MCP-compatible client gets persistent memory in one Docker command → Builds “mental models,” living summaries that auto-update as memories accumulate, instead of you re-reading a growing pile of raw logs → Per-user memory isolation lets you run one instance for multiple people or agents without their memories bleeding into each other → Reports top results on the LongMemEval long-term memory benchmark, per the project’s own published numbers Every AI agent today is stateless by default, it knows nothing about yesterday’s conversation unless you re-paste it. Hindsight’s answer: real structured memory, running on your own hardware if you want it there. MIT License. Built by

Harman

11,032 Aufrufe • vor 2 Monaten

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.
0:23

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Simplifying AI

68,025 Aufrufe • vor 1 Monat

Claude Code + Obsidian + 1 free GitHub skill = unlimited memory. Almost nobody is running this yet. Here’s the problem nobody tells you. Claude forgets you. Every new chat starts at 0. You re explain your business, your projects, your style every single time. That tax is why most people never compound anything with AI. The fix is 1 skill. It’s called Obsidian Mind. It turns your Obsidian vault into Claude’s long term brain. 2,000 notes, 14 projects, every idea you’ve ever dumped Claude reads it live and writes back to it live. It stops being a chatbot. It becomes a second brain that builds. What it does: → Bridges Claude Code straight into your vault. → Pulls any note in real time no copy paste, no context limit. → Updates your vault as you work Claude writes its own memory. → Links everything into 1 graph Claude can navigate. Day 1: Claude knows your last 6 months. Week 2: it drafts off your own past notes. Week 4: it’s running plays you forgot you wrote down. The people making real money with AI right now aren’t smarter. They just stopped starting from 0 every morning. Setup takes 11 minutes. The skill is free. You’ll have a brain that never forgets.
0:34

Claude Code + Obsidian + 1 free GitHub skill = unlimited memory. Almost nobody is running this yet. Here’s the problem nobody tells you. Claude forgets you. Every new chat starts at 0. You re explain your business, your projects, your style every single time. That tax is why most people never compound anything with AI. The fix is 1 skill. It’s called Obsidian Mind. It turns your Obsidian vault into Claude’s long term brain. 2,000 notes, 14 projects, every idea you’ve ever dumped Claude reads it live and writes back to it live. It stops being a chatbot. It becomes a second brain that builds. What it does: → Bridges Claude Code straight into your vault. → Pulls any note in real time no copy paste, no context limit. → Updates your vault as you work Claude writes its own memory. → Links everything into 1 graph Claude can navigate. Day 1: Claude knows your last 6 months. Week 2: it drafts off your own past notes. Week 4: it’s running plays you forgot you wrote down. The people making real money with AI right now aren’t smarter. They just stopped starting from 0 every morning. Setup takes 11 minutes. The skill is free. You’ll have a brain that never forgets.

West Lord

110,968 Aufrufe • vor 3 Monaten

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.
4:19

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.

DegenCalls

20,124 Aufrufe • vor 2 Monaten

ClickUp has launched Brain², which automatically wires in your company's projects, org setup, and decisions as context for any frontier model you choose. It allows you to pick models like Claude 4.7, GPT 5.5, or Gemini 3.1, and Brain compresses everything your team knows before the model ever sees a prompt. > Preferences Memory - Brain retains your role in the org, team, and format preferences across every session > MCP live - connects Gmail, GitHub, Figma, and Slack natively, no middleware needed > Brain Slides - polished decks from one prompt, built on designer-authored templates
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ClickUp has launched Brain², which automatically wires in your company's projects, org setup, and decisions as context for any frontier model you choose. It allows you to pick models like Claude 4.7, GPT 5.5, or Gemini 3.1, and Brain compresses everything your team knows before the model ever sees a prompt. > Preferences Memory - Brain retains your role in the org, team, and format preferences across every session > MCP live - connects Gmail, GitHub, Figma, and Slack natively, no middleware needed > Brain Slides - polished decks from one prompt, built on designer-authored templates

🚨 AI News | TestingCatalog

37,880 Aufrufe • vor 4 Monaten

APPLE JUST SHOWED HOW TO RUN 10 AI AGENTS LOCALLY ON MAC - NO CLOUD, NO API KEYS, ZERO COSTS 00:10 Apple engineer says: "your data stays on your device, AI available anywhere at any time, zero usage costs" agent reads your code, checks GitHub, finds what needs attention and writes a report - all on your Mac, nothing goes to the internet 10 agents work simultaneously - one writes code, another tests, third fixes bugs - parallel with no queue built a full iPad app from scratch in 2 minutes, fixed its own mistakes and compiled without issues takes 5 minutes to set up - and you never pay again for a bot that runs 24/7
13:29

APPLE JUST SHOWED HOW TO RUN 10 AI AGENTS LOCALLY ON MAC - NO CLOUD, NO API KEYS, ZERO COSTS 00:10 Apple engineer says: "your data stays on your device, AI available anywhere at any time, zero usage costs" agent reads your code, checks GitHub, finds what needs attention and writes a report - all on your Mac, nothing goes to the internet 10 agents work simultaneously - one writes code, another tests, third fixes bugs - parallel with no queue built a full iPad app from scratch in 2 minutes, fixed its own mistakes and compiled without issues takes 5 minutes to set up - and you never pay again for a bot that runs 24/7

Noisy

526,822 Aufrufe • vor 3 Monaten