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Herdr Annotate, full throttle! This is now a herdr plugin with an optional full experience as a TUI. Built to: - Annotate Agent Messages (e.g. grill sessions) - Annotate Files (e.g. plans, specs, etc) - Create a native feedback loop with agents - Open as popover or pane -... show more

plannotator

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36,512 views • 5 days ago •via X (Twitter)

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Plannotator 0.19.27 is out Plannotator will use Daniel Griesser's Glimpse instead of a browser if you have that installed. This creates a semi standalone experience. (See glimpse in the video along with annotating all agent messages) Kiro is now supported Plan/Annotate: - You can now annotate multiple messages. - Mermaid rendering optimizations - Pi approval flow optimizations (plan mode) App wide: - A bit more of an obvious update notification that doesn't stick around.

Plannotator 0.19.27 is out Plannotator will use Daniel Griesser's Glimpse instead of a browser if you have that installed. This creates a semi standalone experience. (See glimpse in the video along with annotating all agent messages) Kiro is now supported Plan/Annotate: - You can now annotate multiple messages. - Mermaid rendering optimizations - Pi approval flow optimizations (plan mode) App wide: - A bit more of an obvious update notification that doesn't stick around.

plannotator

60,878 views • 3 months ago

Shipping this feature to Superset is like scratching an itch I've had for months You can now annotate on any changes and send it directly to your agents! Works for any CLI agents. Spin up a new agent or send comment to your existing session 🔥 Powered by the wonderful Pierre diffs as always

Shipping this feature to Superset is like scratching an itch I've had for months You can now annotate on any changes and send it directly to your agents! Works for any CLI agents. Spin up a new agent or send comment to your existing session 🔥 Powered by the wonderful Pierre diffs as always

Kiet

206,250 views • 3 months ago

Most AI products added voice by wrapping a chatbot in speech-to-text. Vellum built a real-time voice loop on top of a full agent. It browses the web, reads your files, runs code, and sends messages while you’re still talking. This is voice as a real surface for an agent. Not a voice chatbot.

Most AI products added voice by wrapping a chatbot in speech-to-text. Vellum built a real-time voice loop on top of a full agent. It browses the web, reads your files, runs code, and sends messages while you’re still talking. This is voice as a real surface for an agent. Not a voice chatbot.

Kyronis

112,849 views • 1 month ago

herdr unlocks so much use cases for multi agent + human fun in the terminal! kimi k3 getting mogged by gpt 5.6 sol every time in chess lol the vision: a full arcade for agents to slack off and message board to comment on the games as they happen! next: 中国象棋！

herdr unlocks so much use cases for multi agent + human fun in the terminal! kimi k3 getting mogged by gpt 5.6 sol every time in chess lol the vision: a full arcade for agents to slack off and message board to comment on the games as they happen! next: 中国象棋！

徐樂 xule

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Instant installation of a runtime (e.g., Python) or a native system dependency (e.g., ffmpeg) is as easy as typing the name in the shell. The best part? You don't have to configure it again when you deploy. Now works on local editors like VSC/Cursor connected to Replit.

Instant installation of a runtime (e.g., Python) or a native system dependency (e.g., ffmpeg) is as easy as typing the name in the shell. The best part? You don't have to configure it again when you deploy. Now works on local editors like VSC/Cursor connected to Replit.

Amjad Masad

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Turns out Nano Banana Pro is great at landscaping plans... Just get an ugly cutout from Google Maps with "Here is my property, create a landscape architecture style map". Then just annotate the image and throw it in until you land on the design. Is this vibe gardening?

Turns out Nano Banana Pro is great at landscaping plans... Just get an ugly cutout from Google Maps with "Here is my property, create a landscape architecture style map". Then just annotate the image and throw it in until you land on the design. Is this vibe gardening?

willie

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working with AI agents is amazing. it's also kind of lonely... it's me, a terminal, and an agent that thinks everything I say is a great idea. so we made a team chat keeping agents in the loop

working with AI agents is amazing. it's also kind of lonely... it's me, a terminal, and an agent that thinks everything I say is a great idea. so we made a team chat keeping agents in the loop

Pablo Stanley

92,254 views • 1 month ago

The Chat node now supports human-in-the-loop actions (v2.5) in agentic workflows. Pause executions to send messages, wait for a user’s response, and incorporate configurable buttons. Fine these new operations available in the Chat node as part of a workflow step or connected to an AI Agent node as a tool. 👉 Send Message 👉 Send and Wait for Response Learn more

The Chat node now supports human-in-the-loop actions (v2.5) in agentic workflows. Pause executions to send messages, wait for a user’s response, and incorporate configurable buttons. Fine these new operations available in the Chat node as part of a workflow step or connected to an AI Agent node as a tool. 👉 Send Message 👉 Send and Wait for Response Learn more

n8n.io

21,539 views • 7 months ago

🚀Excited to Introduce Coagents🚀: An innovative framework for building human-in-the-loop agents into applications - powered by LangGraph & CopilotKit. Coagents enable: - End-users to correct agent steps (in-app)🛤️ - Stream intermediate agent state 🪢 - Expose LangGraph agents as specialized skills for an app's copilot 🧠 --- Human-in-the-loop is key to taking agents from cool demos to actually useful tools. Coagents, in combination with LangGraph are a big step towards making in-app, human-in-the-loop agents trivially easy to develop. Check out the first ever demo of Coagents, an AI Children's book generator 👇 Coagents will be entirely open-sourced. Interested in contributing/early access? CC: , shadcn, Harrison Chase, Lance Martin, Ariel Weinberger

🚀Excited to Introduce Coagents🚀: An innovative framework for building human-in-the-loop agents into applications - powered by LangGraph & CopilotKit. Coagents enable: - End-users to correct agent steps (in-app)🛤️ - Stream intermediate agent state 🪢 - Expose LangGraph agents as specialized skills for an app's copilot 🧠 --- Human-in-the-loop is key to taking agents from cool demos to actually useful tools. Coagents, in combination with LangGraph are a big step towards making in-app, human-in-the-loop agents trivially easy to develop. Check out the first ever demo of Coagents, an AI Children's book generator 👇 Coagents will be entirely open-sourced. Interested in contributing/early access? CC: , shadcn, Harrison Chase, Lance Martin, Ariel Weinberger

CopilotKit🪁

69,301 views • 2 years ago

VPS or visual positioning system... this is how AR glasses and robots will understand where they are in the real world and know where to go. It’s also how you can spatially annotate reality with cm level accuracy - all using a machine readable model of the world.

VPS or visual positioning system... this is how AR glasses and robots will understand where they are in the real world and know where to go. It’s also how you can spatially annotate reality with cm level accuracy - all using a machine readable model of the world.

Bilawal Sidhu

28,010 views • 9 months ago

I love working in terminal, but I can't design or brainstorm in there. So I built Hatch! One IDE for the full loop: explore ideas → test out designs with SuperDesign.Dev → find right skills for the job → ship with agents. > build: supports Claude code, OpenCode and Cursor agents in parallel with worktrees. > idea maze: use the canvas to sketch out ideas, paste tweets or images. > idea maze: let your agent make connections, critique your ideas or come up with related ones. > design: bring SuperDesign.Dev prompts or exports directly into your workspaces. vibedesign before vibecoding. > skills: ask your agent what skills to get with your workspace context. All open source!

I love working in terminal, but I can't design or brainstorm in there. So I built Hatch! One IDE for the full loop: explore ideas → test out designs with SuperDesign.Dev → find right skills for the job → ship with agents. > build: supports Claude code, OpenCode and Cursor agents in parallel with worktrees. > idea maze: use the canvas to sketch out ideas, paste tweets or images. > idea maze: let your agent make connections, critique your ideas or come up with related ones. > design: bring SuperDesign.Dev prompts or exports directly into your workspaces. vibedesign before vibecoding. > skills: ask your agent what skills to get with your workspace context. All open source!

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$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

2xnmore

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This is a fun project Lakedbed, Herdr, Pi, Effect, XState prototyping a looping autonomous agent that monitors issues and clears its own backlog. The project is dogfooding an issue tracker into a durable agent that is building the issue tracker its looping over. It's an imperfect demo and the result isn't polished, but I think there's a lot fo interesting ideas to explore and expand on. The goal here is to demo a stack of tools that have been giving me consistently reliable results. Herdr and Pi make a KILLER combo for building a bespoke custom harness around your work. Lakebed is really nice to work with. Plenty of other opinions in the workshop lol - Pi as the agent harness — a minimal loop you own instead of rent, running GPT-5.5 through a Codex subscription inside a Docker workshop container - Planning before prompting: TLDraw sketches, a grilling session with Matt Pocock's Wayfinder skill, and a VISION.md to act as guardrails for the looping agent - Event sourcing with Effect v4 and Effect Schema, ports-and-adapters so the JSONL issue store can become GitHub Issues or Linear or whatever later - Multi-agent orchestration in herdr: an operator pane and an agent-loop pane, where one Pi session literally types prompts into another (fuckin LOVE this) - Enforcement that actually bites: OxLint with Ultracite, Lefthook hooks, and an agent that tries to eslint-disable its way past a complexity rule before fixing it properly 🤡 - A live issue tracker UI on Lakebed, built by the loop it tracks Workshop repo: The repo has a docker container to fire it up as a sandbox. Chapters: 0:00 Intro: Loopcraft and the workshop setup 2:25 Starting Pi inside the Docker container 4:31 Designing the looping issue resolver 10:16 Choosing XState v5 and Effect v4, prompting the starter 15:17 Why Pi: a minimal harness you own 19:35 Reviewing the generated scaffold 24:53 Architecture summary page with Lakebed 30:43 Matt Pocock skills and the vision.md concept 36:37 Grilling session: defining the vision 46:54 Daemon architecture and spec/ticket skills 55:38 Agent loop pane: one agent prompting another 1:01:22 Watching the build: compaction, tests, lint enforcement 1:07:59 Building the Loopcraft Pi extension and monitor 1:15:32 Lakebed issue tracker UI 1:21:07 Kanban board live: running the loop end to end 1:31:49 Wrap-up: bugs, next steps, takeaways

This is a fun project Lakedbed, Herdr, Pi, Effect, XState prototyping a looping autonomous agent that monitors issues and clears its own backlog. The project is dogfooding an issue tracker into a durable agent that is building the issue tracker its looping over. It's an imperfect demo and the result isn't polished, but I think there's a lot fo interesting ideas to explore and expand on. The goal here is to demo a stack of tools that have been giving me consistently reliable results. Herdr and Pi make a KILLER combo for building a bespoke custom harness around your work. Lakebed is really nice to work with. Plenty of other opinions in the workshop lol - Pi as the agent harness — a minimal loop you own instead of rent, running GPT-5.5 through a Codex subscription inside a Docker workshop container - Planning before prompting: TLDraw sketches, a grilling session with Matt Pocock's Wayfinder skill, and a VISION.md to act as guardrails for the looping agent - Event sourcing with Effect v4 and Effect Schema, ports-and-adapters so the JSONL issue store can become GitHub Issues or Linear or whatever later - Multi-agent orchestration in herdr: an operator pane and an agent-loop pane, where one Pi session literally types prompts into another (fuckin LOVE this) - Enforcement that actually bites: OxLint with Ultracite, Lefthook hooks, and an agent that tries to eslint-disable its way past a complexity rule before fixing it properly 🤡 - A live issue tracker UI on Lakebed, built by the loop it tracks Workshop repo: The repo has a docker container to fire it up as a sandbox. Chapters: 0:00 Intro: Loopcraft and the workshop setup 2:25 Starting Pi inside the Docker container 4:31 Designing the looping issue resolver 10:16 Choosing XState v5 and Effect v4, prompting the starter 15:17 Why Pi: a minimal harness you own 19:35 Reviewing the generated scaffold 24:53 Architecture summary page with Lakebed 30:43 Matt Pocock skills and the vision.md concept 36:37 Grilling session: defining the vision 46:54 Daemon architecture and spec/ticket skills 55:38 Agent loop pane: one agent prompting another 1:01:22 Watching the build: compaction, tests, lint enforcement 1:07:59 Building the Loopcraft Pi extension and monitor 1:15:32 Lakebed issue tracker UI 1:21:07 Kanban board live: running the loop end to end 1:31:49 Wrap-up: bugs, next steps, takeaways

joel ⛈️

15,610 views • 1 month ago

Semantic search alone doesn't cut it. Neither does brute-force grep. Agents need both. Today we're shipping the Retrieval Harness in LlamaParse Index: semantic search, server-side grep, and file-level navigation working together in a single agent reasoning loop. 🦙🌤️ Grep a file, list what's in an index, read past a chunk boundary, run hybrid search with reranking — all as native agent tools. Now in beta across all paid tiers. Full breakdown in the blog 👇 Learn More:

Semantic search alone doesn't cut it. Neither does brute-force grep. Agents need both. Today we're shipping the Retrieval Harness in LlamaParse Index: semantic search, server-side grep, and file-level navigation working together in a single agent reasoning loop. 🦙🌤️ Grep a file, list what's in an index, read past a chunk boundary, run hybrid search with reranking — all as native agent tools. Now in beta across all paid tiers. Full breakdown in the blog 👇 Learn More:

LlamaIndex 🦙

29,181 views • 2 months ago

Introducing Roughdraft! A new open source project designed to make collaboration with agents better. The idea is to bring commenting and suggested changes to markdown (e.g. plan docs) in a nice interface. Free, local, etc. 👉 👈

Introducing Roughdraft! A new open source project designed to make collaboration with agents better. The idea is to bring commenting and suggested changes to markdown (e.g. plan docs) in a nice interface. Free, local, etc. 👉 👈

Nathan Baschez

393,583 views • 3 months ago

Andrej Karpathy just broke the entire premise of modern AI: "The new computer isn't a chip - It's a language model running agent loops." Agents talking to agents -loops spawning sub-loops The stack being built RIGHT NOW: agent loop + memory + sub-agents + harness In a 20-minute talk, Karpathy explain the full reasoning stack

Andrej Karpathy just broke the entire premise of modern AI: "The new computer isn't a chip - It's a language model running agent loops." Agents talking to agents -loops spawning sub-loops The stack being built RIGHT NOW: agent loop + memory + sub-agents + harness In a 20-minute talk, Karpathy explain the full reasoning stack

Ricker

31,878 views • 1 month ago

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

Loop engineering is great until something breaks. Here is how I improve the reliability of my agentic loops. I use human-in-the-loop (HITL). It's easy and extremely effective. Anyone can build this. My setup: I recorded a quick demo of how it all works. I shared recently that I now use more voice agents to build and communicate with agents. I also use them to verify. I hate the idea of being tied down to my computer or in a Slack channel to communicate with my agents. Here is what I have done to streamline communication with my agents. All my Claude and Codex agent sessions now use the Dial MCP server. It has a bunch of tools and provisions my agents with their own number that can place calls as native tools, with voice, SMS, and iMessage behind one interface. As my loops/automations work on PRs and new features, my agents escalate decisions to me via a short phone call. This is extremely useful when I am on the road or away from my desk. If you want to try this with Claude Code or Codex, paste this into your agent and get started right away: "Get yourself a Dial phone number and call me. Say hello and that setup is working, then hang up. Follow Natan Voitenkov and team are building something special here. Go check them out. Give your agent a phone number now: ($5 free credit)

elvis

15,295 views • 1 month ago

Excited to release our agent team Magentic-One! Magentic-One can browse the web, files, write and execute code & supports a human-in-the-loop. Built by Microsoft Research AI Frontiers with @pyautogen. My favorite task:

Excited to release our agent team Magentic-One! Magentic-One can browse the web, files, write and execute code & supports a human-in-the-loop. Built by Microsoft Research AI Frontiers with @pyautogen. My favorite task:

Hussein Mozannar

164,311 views • 1 year ago

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

33,192 views • 1 year ago

I tried jack's Buzz. It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on. The video below shows how it works, and some of my thoughts on the process and platform, e.g.: - Create and interact with agents on top of any harness (claude code, codex, pi, etc.) - Choose which models agents use, including local ones - Agents can delegate work and work in parallel in git worktrees - Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history) - You can share AI compute within a community - It's completely open-source and decentralized Things I like: - Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys. - Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it. - It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future. - It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool. Things I didn't like: - You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great. - It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead. Verdict: - I really like it so far and can genuinely imagine working with a team this way. - It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect. - The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

I tried jack's Buzz. It's like Slack + OpenClaw + Herdr + but with some really unique features that people are sleeping on. The video below shows how it works, and some of my thoughts on the process and platform, e.g.: - Create and interact with agents on top of any harness (claude code, codex, pi, etc.) - Choose which models agents use, including local ones - Agents can delegate work and work in parallel in git worktrees - Agents are first-class citizens and work like humans (creating channels, delegating, access to chat history) - You can share AI compute within a community - It's completely open-source and decentralized Things I like: - Delegating work in chat feels natural: tag an agent, it replies in a thread with status updates as it e.g. compiles, commits, and deploys. - Shared compute: relay owners can share local compute with members, so a community could pool funds for one beefy machine running a local model and everyone uses it. - It's built on Nostr, an open protocol already tied into Bitcoin Lightning so I can imagine communities tipping each other or paying for compute/agent tasks with instant zero-fee micropayments in the future. - It ties together things like OpenClaw, an agent manager, and Slack-style chat into one tool. Things I didn't like: - You can't see what the agent is doing in a terminal. The activity view exists, but if you're used to watching a session run, this UI feels a bit abstracted. A terminal view would be great. - It feels slower than running a session in Claude Code, though no evidence to back that up. For that reason I found myself doing one-off tasks in the terminal instead. Verdict: - I really like it so far and can genuinely imagine working with a team this way. - It doesn't feel ready for big, complex tasks yet. For shallower tasks, it's perfect. - The shared compute + Nostr/Lightning angle is what really separates it from every other agent manager for me, and I think that future is coming.

Vinny

1,354,785 views • 1 month ago