Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

herdr 0.7.4 is out! many fixes, many additions, and structural work for multi-client 👀 but this one ships one of the two most wanted features: customizable sidebar. both agent and workspace rows: plugins, custom scripts, agent hooks, or just settings can put anything on them. claude's live task title,... show more

herdr

26,462 subscribers

93,988 Aufrufe • vor 2 Monaten •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

38 Kommentare

Profilbild von herdr

herdrvor 2 Monaten

and the second most wanted: floating panes (we named them popup panes in the architecture, so both names are correct :P). bind any script or command to a pane that floats over your layout lazygit, a scratch shell, or an agent hud that drills into whatever pane you're on. no splits harmed.

Profilbild von herdr

herdrvor 2 Monaten

also: sidebar rows no longer have a default gap between entries, so everything packs tighter and feels better. if you miss the old spacing, row_gap = 1 brings it back. row docs: full notes:

Profilbild von zebra

zebravor 2 Monaten

It's amazing! Btw, your Pi status line is so cool. Would you mind sharing what plugin or configuration you're using for it?

Profilbild von herdr

herdrvor 2 Monaten

of course!

Profilbild von zebra

zebravor 2 Monaten

Thank you!

Profilbild von Makaroni

Makaronivor 2 Monaten

pac-man eating pi's context window is genuinely the most unhinged feature description ive ever read shipped it tho

Profilbild von Elijah Wilt

Elijah Wiltvor 2 Monaten

Herdr is the best

Profilbild von Felipe Alves

Felipe Alvesvor 2 Monaten

Herdr is the best

Profilbild von Ugur Tarlig

Ugur Tarligvor 2 Monaten

Made Cmd+G in herdr open a “recent agents” picker: It jumps across Codex + Claude sessions, sorted by the latest real transcript event—not flaky file mtimes. Searchable, theme-aware, and Cmd+Shift+G keeps herdr’s native navigator. Code + tests:

Profilbild von Pres Mihaylov

Pres Mihaylovvor 2 Monaten

Excellent ship, was doing some workarounds in my fork to make this work, great to see it native

Profilbild von Pho

Phovor 2 Monaten

Windows version when? I really love your windows builds

Profilbild von Crokily

Crokilyvor 2 Monaten

Just made a lazygit plugin for herdr: it starts as a compact sidebar for the Git stuff I use most, then expands into full lazygit with U when I need it. C opens an AI commit pane, and ; opens settings. been pretty handy for me so far Repo:

Profilbild von Vranac Srdjan

Vranac Srdjanvor 2 Monaten

that is awesome, thank you

Profilbild von Konrad Krasowski

Konrad Krasowskivor 2 Monaten

Fire 🔥. Thank you for the floating pane 🙏

Profilbild von Satyaa Goyal

Satyaa Goyalvor 2 Monaten

Bring that web server 😔

Profilbild von Tomas Varga

Tomas Vargavor 2 Monaten

Yes, thank you! i was just working on new plugin that can benefit from sidebar customizations.

Profilbild von Daniel

Danielvor 2 Monaten

w herdr 🙏

Profilbild von Fede

Fedevor 2 Monaten

Herdr is the GOAT.

Profilbild von Daniel White

Daniel Whitevor 2 Monaten

Goated 🐐🐐

Profilbild von Ugur Tarlig

Ugur Tarligvor 2 Monaten

Thank you very much! With this release, are we able to have custom sorting in the sidebar? Recently active agents on top in the sidebar?

Profilbild von Synthetic Sea

Synthetic Seavor 2 Monaten

Herdr is so good, well done

Profilbild von Zinger Bee

Zinger Beevor 2 Monaten

really cool

Profilbild von Eric Jeker

Eric Jekervor 2 Monaten

Adopted!

Profilbild von Ray Todd

Ray Toddvor 2 Monaten

What about the copy text issue which causes extra lines to be captured

Profilbild von Tosu of Black Wind 🌪️

Tosu of Black Wind 🌪️vor 1 Monat

we like the sheep 🐑

Profilbild von Monty

Montyvor 2 Monaten

keep up the good work sir. thank u.

Profilbild von jintong.liu

jintong.liuvor 2 Monaten

pretty cool!

Profilbild von BlockedPath

BlockedPathvor 2 Monaten

Yes this is amazing!

Profilbild von ljudina83

ljudina83vor 2 Monaten

is there a way to launch apps when starting herdr?

Profilbild von Raj Josyula

Raj Josyulavor 2 Monaten

Can we have @herdrdev integrated with @orca_build ?

Profilbild von Salem Sayed 🔻

Salem Sayed 🔻vor 2 Monaten

i need that pac-man

Profilbild von Nik

Nikvor 2 Monaten

really enjoying herdr thanks for all the hard work!

Profilbild von Bernhard Keprt

Bernhard Keprtvor 2 Monaten

Nice. Gonna check it out 👍

Profilbild von Spencer

Spencervor 2 Monaten

How is the PI configured in the Agents panel of the sidebar? I like it very much!

Profilbild von Waldo Milanes

Waldo Milanesvor 2 Monaten

pac-man eating the context window started as a bit but that's genuinely the gauge i want most

Profilbild von Tài Ba Pi-Si

Tài Ba Pi-Sivor 2 Monaten

Tôi không hiểu gì về Code hay về kỹ thuật. Những gì Bạn đang làm là đang đóng góp gì đó cho Pi Network đúng không?

Profilbild von pol

polvor 2 Monaten

Awesome! I’ve been using herdr inplace of tmux and it’s great. Although i have been having trouble with getting agents state on mac os running docker agent sandbox.

Profilbild von Sebastian Buzdugan

Sebastian Buzduganvor 2 Monaten

custom sidebar is useful, but agent hooks need strict permission scopes before multi-client ships

Ähnliche Videos

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Nikos

19,498 Aufrufe • vor 28 Tagen

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,180 Aufrufe • vor 3 Monaten

Support for Agent Plugins is live in Cursor (and I made a plugin for searching X!) Agent Plugins combine Skills and MCP servers into a single package Think about them like an Ikea furniture kit: your table comes with instructions, parts, and tools. You use the instructions to understand how things work (which tools on which parts), then use the tools to assemble your furniture Agent Plugins are bundles of tools & resources that allow agents to accomplish a goal: - MCP servers: fixed context and tools - Skills: dynamic instructions, loaded progressively Plugins bundle tools and instructions so your agent is able to use tokens efficiently Many of us already use MCP servers and skills together - Agent Plugins formalize this pattern for more powerful workflows I'm excited to see where the standard takes us!

Support for Agent Plugins is live in Cursor (and I made a plugin for searching X!) Agent Plugins combine Skills and MCP servers into a single package Think about them like an Ikea furniture kit: your table comes with instructions, parts, and tools. You use the instructions to understand how things work (which tools on which parts), then use the tools to assemble your furniture Agent Plugins are bundles of tools & resources that allow agents to accomplish a goal: - MCP servers: fixed context and tools - Skills: dynamic instructions, loaded progressively Plugins bundle tools and instructions so your agent is able to use tokens efficiently Many of us already use MCP servers and skills together - Agent Plugins formalize this pattern for more powerful workflows I'm excited to see where the standard takes us!

matt palmer

40,657 Aufrufe • vor 1 Monat

Last, but not least, don't sleep on this one: Le Chat now has Work mode (Preview) — a powerful agent for complex long-horizon tasks like research, analysis, and actions across your connected tools. Connectors are on by default so the agent pulls context from docs, email, and calendars as it works, and sessions persist across many turns until the task is complete.

Last, but not least, don't sleep on this one: Le Chat now has Work mode (Preview) — a powerful agent for complex long-horizon tasks like research, analysis, and actions across your connected tools. Connectors are on by default so the agent pulls context from docs, email, and calendars as it works, and sessions persist across many turns until the task is complete.

Vibe

34,302 Aufrufe • vor 4 Monaten

On Day 3 of 12 Days of Agent Two: The Agent has eyes. Agent Two reads and understands ANYTHING you upload into the chat. Videos, images, scripts, PDFs, YouTube links, Drive files, brand books, rough cuts, whatever you give it. And it doesn't just read your files. It checks them against your project's context and understands what the uploaded video means and where it would be relevant and useful. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Invideo

3,233,044 Aufrufe • vor 1 Monat

Introducing Nebula Live: The fastest way to work with your agents. You can now talk, text or show them anything. They work in real time, understand what you're saying and autonomously kick off work. Invite your teammates into a live session and everyone can collaborate together. Experience hands free building while just talking and the best part is all your context is saved in the workspace forever. Background mode mutes your agent but they still listen to you, kick off work and let you know when things are done. Share your screen or camera and your agent can see what you are doing. Now in beta across desktop, mobile and web. We're giving away $100 for you to experience Nebula Live. Reply with what you want to talk to your agents about.

Introducing Nebula Live: The fastest way to work with your agents. You can now talk, text or show them anything. They work in real time, understand what you're saying and autonomously kick off work. Invite your teammates into a live session and everyone can collaborate together. Experience hands free building while just talking and the best part is all your context is saved in the workspace forever. Background mode mutes your agent but they still listen to you, kick off work and let you know when things are done. Share your screen or camera and your agent can see what you are doing. Now in beta across desktop, mobile and web. We're giving away $100 for you to experience Nebula Live. Reply with what you want to talk to your agents about.

Nebula

11,584 Aufrufe • vor 1 Monat

Everyone's still typing at their agents. Ask. Wait. Read. Ask again. Audio huddles on Buzz flip that. You pull the agent into a live call and it already has the context of your entire chat. Voice is where the ideas actually come out of your head. But that's scratching the surface. Free startup idea: FaceTiming your agents. The models can already see your screen, hear you, and act on your machine. Somebody just has to put all three in one window. That's the holy grail of the future of work. Somebody build it. I'll be the first customer.

Everyone's still typing at their agents. Ask. Wait. Read. Ask again. Audio huddles on Buzz flip that. You pull the agent into a live call and it already has the context of your entire chat. Voice is where the ideas actually come out of your head. But that's scratching the surface. Free startup idea: FaceTiming your agents. The models can already see your screen, hear you, and act on your machine. Somebody just has to put all three in one window. That's the holy grail of the future of work. Somebody build it. I'll be the first customer.

The Startup Ideas Podcast (SIP) 🧃

13,217 Aufrufe • vor 1 Monat

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

61,226 Aufrufe • vor 2 Monaten

Introducing Agent Wikis! I've been using llm wikis (or knowledge bases) for research and videos for a while to ensure the information I'm providing is always up-to-date and accurate. Now you can access them for free! Just point your agent at to access wikis on many topics, including Hermes Agent, Hyperframes, Google Ads, and llama.cpp. They can be queried through a simple MCP server. Everything has been designed to be agent-friendly and lightweight (no context bloat). This is a beta release, so please let me know of any bugs or issues (I'm sure there are one or two). You can also join the waitlist if you would like us to create and maintain your own private wiki, or if you are a project owner you can request one for your project to make sure your users (and their agents) don't run into any common issues (and also be featured on the site). Thank you!

Introducing Agent Wikis! I've been using llm wikis (or knowledge bases) for research and videos for a while to ensure the information I'm providing is always up-to-date and accurate. Now you can access them for free! Just point your agent at to access wikis on many topics, including Hermes Agent, Hyperframes, Google Ads, and llama.cpp. They can be queried through a simple MCP server. Everything has been designed to be agent-friendly and lightweight (no context bloat). This is a beta release, so please let me know of any bugs or issues (I'm sure there are one or two). You can also join the waitlist if you would like us to create and maintain your own private wiki, or if you are a project owner you can request one for your project to make sure your users (and their agents) don't run into any common issues (and also be featured on the site). Thank you!

tonbi

46,993 Aufrufe • vor 3 Monaten

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

rewind

27,195 Aufrufe • vor 1 Monat

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.

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

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. 🧵

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 1 Monat

I built the clipboard I always wanted for working with AI agents. It’s called Bluey and It’s 100% local-first. It lives under your mouse cursor. You can draw on your screen, speak to it, or type what you mean, and Bluey turns all of that into rich context for Claude Code or Codex. I’ve been using it to design websites and debug UI because I can finally say things like “move this here” or “make this section feel cleaner” while pointing at the actual screen. No more writing long explanations. No more manually describing screenshots. No more agents getting lost because they don’t know what “this” or "that" means. You can just point to it, speak or type and it will curate the best possible context for your agents Bluey captures the screenshot, annotation, transcript, app/window context, and the details your agent needs, then sends it directly into your coding session. I am giving it out for free for a while! Im building it with my buddy Omkar Satpute and we would love to hear what you think about it on Discord what context do you wish your agent could understand better?

I built the clipboard I always wanted for working with AI agents. It’s called Bluey and It’s 100% local-first. It lives under your mouse cursor. You can draw on your screen, speak to it, or type what you mean, and Bluey turns all of that into rich context for Claude Code or Codex. I’ve been using it to design websites and debug UI because I can finally say things like “move this here” or “make this section feel cleaner” while pointing at the actual screen. No more writing long explanations. No more manually describing screenshots. No more agents getting lost because they don’t know what “this” or "that" means. You can just point to it, speak or type and it will curate the best possible context for your agents Bluey captures the screenshot, annotation, transcript, app/window context, and the details your agent needs, then sends it directly into your coding session. I am giving it out for free for a while! Im building it with my buddy Omkar Satpute and we would love to hear what you think about it on Discord what context do you wish your agent could understand better?

Milind S

13,342 Aufrufe • vor 3 Monaten

AT LAST! Custom Status Lines are coming to Codex 🙌 This is just one of many highly requested features in the pipeline. You can now run /statusline to configure exactly what shows in your terminal. Choose from 15 data points, toggle them on/off, or even reorder them. One thing I'd love to see in the future is to be able to install custom extensions that would allow you to customize their styling or run custom scripts (like weather updates). More updates coming soon, but this is the most mature imminent release (0.99 Alpha). Codex team is absolutely cooking.

AT LAST! Custom Status Lines are coming to Codex 🙌 This is just one of many highly requested features in the pipeline. You can now run /statusline to configure exactly what shows in your terminal. Choose from 15 data points, toggle them on/off, or even reorder them. One thing I'd love to see in the future is to be able to install custom extensions that would allow you to customize their styling or run custom scripts (like weather updates). More updates coming soon, but this is the most mature imminent release (0.99 Alpha). Codex team is absolutely cooking.

am.will

38,806 Aufrufe • vor 7 Monaten

$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.$

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

29,200 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 🧵↓

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 1 Monat

Launching 'Folders and Connect'. Day 8 of 12 Days of Agent Two. Everywhere else, getting someone to work with you means buying them a seat on your plan. And once they're in, they can see everything you're making. Folders and Connect fixes both. You invite anyone - inside or outside your organization - into one folder, without adding seats to your plan, and that folder is all they see. The team on the sensitive IP film can't see the launch campaign, and vice versa. And collaborative work still happens seamlessly. Folders & Connect is live for everyone on Team and Enterprise plans. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Launching 'Folders and Connect'. Day 8 of 12 Days of Agent Two. Everywhere else, getting someone to work with you means buying them a seat on your plan. And once they're in, they can see everything you're making. Folders and Connect fixes both. You invite anyone - inside or outside your organization - into one folder, without adding seats to your plan, and that folder is all they see. The team on the sensitive IP film can't see the launch campaign, and vice versa. And collaborative work still happens seamlessly. Folders & Connect is live for everyone on Team and Enterprise plans. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Invideo

682,083 Aufrufe • vor 1 Monat

Your AI agent feels useless because it can't touch anything outside the chat window. Every harness (Claude Code, Codex, Cursor, ChatGPT) needs its own MCP setup. Every tool needs its own auth flow. Switch harnesses and you rebuild the same integrations from scratch. Aident Aident AI Loadout fixes that. Connect your tools once. Gmail, Notion, GitHub, Slack, Calendar, 1000+ apps. Every agent you use gets access to them. And your API keys never touch the agent. They live in a vault. Agent gets the result, not the credentials. Every action gets logged so you know exactly what ran. Here's what I'd do if I were you: 1. Pick your main agent (Claude Code, Codex, Cursor, whatever you use most) 2. Install Loadout with a one-prompt paste 3. Connect your top 5 tools (Gmail, Calendar, Notion, GitHub, Slack) 4. Give your agent one task that touches 3 of them 5. Watch it work Tool plumbing has been the #1 bottleneck for agents this year. Loadout kills it. Try it:

Your AI agent feels useless because it can't touch anything outside the chat window. Every harness (Claude Code, Codex, Cursor, ChatGPT) needs its own MCP setup. Every tool needs its own auth flow. Switch harnesses and you rebuild the same integrations from scratch. Aident Aident AI Loadout fixes that. Connect your tools once. Gmail, Notion, GitHub, Slack, Calendar, 1000+ apps. Every agent you use gets access to them. And your API keys never touch the agent. They live in a vault. Agent gets the result, not the credentials. Every action gets logged so you know exactly what ran. Here's what I'd do if I were you: 1. Pick your main agent (Claude Code, Codex, Cursor, whatever you use most) 2. Install Loadout with a one-prompt paste 3. Connect your top 5 tools (Gmail, Calendar, Notion, GitHub, Slack) 4. Give your agent one task that touches 3 of them 5. Watch it work Tool plumbing has been the #1 bottleneck for agents this year. Loadout kills it. Try it:

Hasan Toor

11,196 Aufrufe • vor 29 Tagen

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

201,127 Aufrufe • vor 1 Jahr

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @pauleveritt@fosstodon.org. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @[email protected]. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

Andrew Ng

465,101 Aufrufe • vor 5 Monaten