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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

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93,988 görüntüleme • 2 ay önce •via X (Twitter)

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herdr profil fotoğrafı

herdr2 ay önce

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.

herdr profil fotoğrafı

herdr2 ay önce

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:

zebra profil fotoğrafı

zebra2 ay önce

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

herdr profil fotoğrafı

herdr2 ay önce

of course!

zebra profil fotoğrafı

zebra2 ay önce

Thank you!

Makaroni profil fotoğrafı

Makaroni2 ay önce

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

Elijah Wilt profil fotoğrafı

Elijah Wilt2 ay önce

Herdr is the best

Felipe Alves profil fotoğrafı

Felipe Alves2 ay önce

Herdr is the best

Ugur Tarlig profil fotoğrafı

Ugur Tarlig2 ay önce

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:

Pres Mihaylov profil fotoğrafı

Pres Mihaylov2 ay önce

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

Pho profil fotoğrafı

Pho2 ay önce

Windows version when? I really love your windows builds

Crokily profil fotoğrafı

Crokily2 ay önce

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:

Vranac Srdjan profil fotoğrafı

Vranac Srdjan2 ay önce

that is awesome, thank you

Konrad Krasowski profil fotoğrafı

Konrad Krasowski2 ay önce

Fire 🔥. Thank you for the floating pane 🙏

Satyaa Goyal profil fotoğrafı

Satyaa Goyal2 ay önce

Bring that web server 😔

Tomas Varga profil fotoğrafı

Tomas Varga2 ay önce

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

Daniel profil fotoğrafı

Daniel2 ay önce

w herdr 🙏

Fede profil fotoğrafı

Fede2 ay önce

Herdr is the GOAT.

Daniel White profil fotoğrafı

Daniel White2 ay önce

Goated 🐐🐐

Ugur Tarlig profil fotoğrafı

Ugur Tarlig2 ay önce

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?

Synthetic Sea profil fotoğrafı

Synthetic Sea2 ay önce

Herdr is so good, well done

Zinger Bee profil fotoğrafı

Zinger Bee2 ay önce

really cool

Eric Jeker profil fotoğrafı

Eric Jeker2 ay önce

Adopted!

Ray Todd profil fotoğrafı

Ray Todd2 ay önce

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

Tosu of Black Wind 🌪️ profil fotoğrafı

Tosu of Black Wind 🌪️1 ay önce

we like the sheep 🐑

Monty profil fotoğrafı

Monty2 ay önce

keep up the good work sir. thank u.

jintong.liu profil fotoğrafı

jintong.liu2 ay önce

pretty cool!

BlockedPath profil fotoğrafı

BlockedPath2 ay önce

Yes this is amazing!

ljudina83 profil fotoğrafı

ljudina832 ay önce

is there a way to launch apps when starting herdr?

Raj Josyula profil fotoğrafı

Raj Josyula2 ay önce

Can we have @herdrdev integrated with @orca_build ?

Salem Sayed 🔻 profil fotoğrafı

Salem Sayed 🔻2 ay önce

i need that pac-man

Nik profil fotoğrafı

Nik2 ay önce

really enjoying herdr thanks for all the hard work!

Bernhard Keprt profil fotoğrafı

Bernhard Keprt2 ay önce

Nice. Gonna check it out 👍

Spencer profil fotoğrafı

Spencer2 ay önce

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

Waldo Milanes profil fotoğrafı

Waldo Milanes2 ay önce

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

Tài Ba Pi-Si profil fotoğrafı

Tài Ba Pi-Si2 ay önce

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?

pol profil fotoğrafı

pol2 ay önce

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.

Sebastian Buzdugan profil fotoğrafı

Sebastian Buzdugan2 ay önce

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

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22,362 görüntüleme • 1 ay önce

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?

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am.will

38,806 görüntüleme • 7 ay önce

$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 görüntüleme • 1 ay önce

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 görüntüleme • 1 ay önce

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 görüntüleme • 1 ay önce

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 görüntüleme • 29 gün önce

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 görüntüleme • 1 yıl önce

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 görüntüleme • 5 ay önce