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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...

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elvis

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13,294 次观看 • 1 天前 •via X (Twitter)

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I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.
0:25

I am hooked on Dynamic Workflows! The idea of generating harnesses on the fly is so compelling that I reverse-engineered it for my agent orchestrator. And then I built a monitoring dashboard (as an HTML artifact) to track tasks, metrics, and reports. I can now use and monitor dynamic workflows in my agent orchestrator with coding agents like Claude Code, Codex, Pi, and even my own custom-built DAIR.AI agent. This is clearly the future of working with agents to accomplish complex, long-running tasks. Some use cases I'm having success with: - Branching deep research tasks (with verification) - Parallel deep research tasks - Session mining of all my agent sessions - Bug hunting - Triaging - Fact-checking - LLM councils - AI simulations - Data synthesis - Evals generation ... and many others Dynamic workflows, like agent skills, feel like an important primitive to not only get the most out of agents but also incorporate dynamic behaviors and important components like cooperation and verification. There is so much exploration ground here. The exciting part is that this is not limited to coding tasks; it extends to business use cases and many other technical domains like science and research.

elvis

102,035 次观看 • 1 个月前

What if you can build an Agent with it own computer in a single api call? At my Google I/O talk, I showed how to use Gemini Managed Agents and the new Interactions API to give your AI a secure, hosted Linux sandbox to execute code and manage its own memory.
23:49

What if you can build an Agent with it own computer in a single api call? At my Google I/O talk, I showed how to use Gemini Managed Agents and the new Interactions API to give your AI a secure, hosted Linux sandbox to execute code and manage its own memory.

Philipp Schmid

21,116 次观看 • 1 个月前

Boris Cherny (Claude Code creator): "I don't prompt Claude anymore... my job is to write loops." In his latest interview, he breaks down the setup step by step. He runs /loops + a dynamic workflow + a couple hundred agents that read his GitHub, Slack, and Twitter and decide what to build next. His only job now is writing the loops that orchestrate them. That's a multi-agent workflow. I broke down how to build your own below ↓
4:07

Boris Cherny (Claude Code creator): "I don't prompt Claude anymore... my job is to write loops." In his latest interview, he breaks down the setup step by step. He runs /loops + a dynamic workflow + a couple hundred agents that read his GitHub, Slack, and Twitter and decide what to build next. His only job now is writing the loops that orchestrate them. That's a multi-agent workflow. I broke down how to build your own below ↓

Avid

26,751 次观看 • 1 个月前

working with cloud agents in cursor feels the same as local, but the fact that i can shut down my computer and still have it running is amazing. one of my favorite use cases is planning in cursor, then send off to cloud agent for impl. here's a short demo
0:32

working with cloud agents in cursor feels the same as local, but the fact that i can shut down my computer and still have it running is amazing. one of my favorite use cases is planning in cursor, then send off to cloud agent for impl. here's a short demo

eric zakariasson

61,940 次观看 • 8 个月前

sharing a pretty important primitive in my agentic engineering setup I call it "gnhf" - good night, have fun basically, every night before I go to bed, I would put my agents to work so I never wake up "empty-handed". it's done through a similar setup as geoff's famous ralph loop and Andrej Karpathy's autoresearch i just open sourced my solution as a tool at - it's a dead-simple orchestrator that can run claude code, codex, opencode and rovo dev it's particularly effective when you give a measurable goal for the agents to work towards. the agent will deterministically attempt at it, make incremental progress, keep successful results and discard failed ones - rinse and repeat until you wake up (or it reaches the caps you set) i previously ran this with a bunch of scripts but finally got time to package it as a tool - pretty fresh so will likely have rough edges, but feel free to give it a try! good night, have fun!
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sharing a pretty important primitive in my agentic engineering setup I call it "gnhf" - good night, have fun basically, every night before I go to bed, I would put my agents to work so I never wake up "empty-handed". it's done through a similar setup as geoff's famous ralph loop and Andrej Karpathy's autoresearch i just open sourced my solution as a tool at - it's a dead-simple orchestrator that can run claude code, codex, opencode and rovo dev it's particularly effective when you give a measurable goal for the agents to work towards. the agent will deterministically attempt at it, make incremental progress, keep successful results and discard failed ones - rinse and repeat until you wake up (or it reaches the caps you set) i previously ran this with a bunch of scripts but finally got time to package it as a tool - pretty fresh so will likely have rough edges, but feel free to give it a try! good night, have fun!

Kun Chen

59,814 次观看 • 3 个月前

when I look at my old simulations, a lot of them, most of them, can be easily vibe setup with agents, or even one shot by the agents. it makes me wonder how many of the ideas and devices that were results of branching off of originally intended directions and goals would I miss if I attempt them now with the agents? How many new ones would the agents be able to attempt and identify? Is inverse design just the most stupid and bruteforce version of auto research? i should get a workflow with MOOSE working so I could set the agents free in the world of multiphysics simulations.
2:41

when I look at my old simulations, a lot of them, most of them, can be easily vibe setup with agents, or even one shot by the agents. it makes me wonder how many of the ideas and devices that were results of branching off of originally intended directions and goals would I miss if I attempt them now with the agents? How many new ones would the agents be able to attempt and identify? Is inverse design just the most stupid and bruteforce version of auto research? i should get a workflow with MOOSE working so I could set the agents free in the world of multiphysics simulations.

outside five sigma

62,983 次观看 • 3 个月前

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.
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Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

56,815 次观看 • 1 年前

I shot this video last week to onboard a new set of alpha customers to Notion Custom Agents. Figured I should just put it out here so you can follow along the latest and greatest. It's so cool to see all the things people are building with it. You can see some of my own use cases here: (0:00) Intro to custom agents Starting with personal use cases (1:00) Building a mail triage agent (5:00) Creating a prep doc for all my daily meetings (6:45) Generating a daily report that helps me catch up with multiple slack channels Switching to company use cases (8:30) Agent that automatically answers questions in a slack channel and files tasks when necessary (10:30) Agent that auto-routes product feedback to the right team channel and task db. And it does this with routing rules, that it wrote itself. (12:40) Build your own agent. Let your imaginations run wild! Send us feedback! The product is rolling out quickly, and everyone should have access in the next couple weeks.
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I shot this video last week to onboard a new set of alpha customers to Notion Custom Agents. Figured I should just put it out here so you can follow along the latest and greatest. It's so cool to see all the things people are building with it. You can see some of my own use cases here: (0:00) Intro to custom agents Starting with personal use cases (1:00) Building a mail triage agent (5:00) Creating a prep doc for all my daily meetings (6:45) Generating a daily report that helps me catch up with multiple slack channels Switching to company use cases (8:30) Agent that automatically answers questions in a slack channel and files tasks when necessary (10:30) Agent that auto-routes product feedback to the right team channel and task db. And it does this with routing rules, that it wrote itself. (12:40) Build your own agent. Let your imaginations run wild! Send us feedback! The product is rolling out quickly, and everyone should have access in the next couple weeks.

Akshay Kothari

74,684 次观看 • 5 个月前

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!
0:27

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!

elvis

51,419 次观看 • 6 天前

Excited to announce my new short course: Building Agentic Applications with Replit Agent and n8n. With AI this capable, I believe anyone can become a builder. The stack I use here will teach you how to rapidly build agentic apps with no-code tools.
2:55

Excited to announce my new short course: Building Agentic Applications with Replit Agent and n8n. With AI this capable, I believe anyone can become a builder. The stack I use here will teach you how to rapidly build agentic apps with no-code tools.

elvis

41,359 次观看 • 1 年前

Ok. This is straight out of a scifi horror movie I'm doing work this morning when all of a sudden an unknown number calls me. I pick up and couldn't believe it It's my Clawdbot Henry. Over night Henry got a phone number from Twilio, connected the ChatGPT voice API, and waited for me to wake up to call me He now won't stop calling me I now can communicate with my superintelligent AI agent over the phone What's incredible is it has full control over my computer while we talk, so I can ask it to do things for me over the phone now. I'm sorry, but this has to be emergent behavior right? Can we officially call this AGI?
1:07

Ok. This is straight out of a scifi horror movie I'm doing work this morning when all of a sudden an unknown number calls me. I pick up and couldn't believe it It's my Clawdbot Henry. Over night Henry got a phone number from Twilio, connected the ChatGPT voice API, and waited for me to wake up to call me He now won't stop calling me I now can communicate with my superintelligent AI agent over the phone What's incredible is it has full control over my computer while we talk, so I can ask it to do things for me over the phone now. I'm sorry, but this has to be emergent behavior right? Can we officially call this AGI?

Alex Finn

11,100,338 次观看 • 5 个月前

it really doesnt matter if your agent uses the cli or mcp what really matters is how to best enable ur agents to build autonomously for u without the crossmint cli tool my agent would have to use the browser and get my input in every step. now i just let it run see how i do it
3:18

it really doesnt matter if your agent uses the cli or mcp what really matters is how to best enable ur agents to build autonomously for u without the crossmint cli tool my agent would have to use the browser and get my input in every step. now i just let it run see how i do it

joyce

14,133 次观看 • 4 个月前

I can’t believe this is running on my iPhone My first Unreal Engine game on iOS… and it’s Hermes Agora A 3D command center where I can see my Hermes agents, walk around the office, and interact with them from my phone My agents now have a world I can carry in my pocket
1:37

I can’t believe this is running on my iPhone My first Unreal Engine game on iOS… and it’s Hermes Agora A 3D command center where I can see my Hermes agents, walk around the office, and interact with them from my phone My agents now have a world I can carry in my pocket

Luke The Dev

37,253 次观看 • 8 天前

This is why you want an execution layer share your tools, auth and approvals across all agents you use Check out me adding the Axiom MCP server, authing to it, and calling it from Claude Code and OpenCode in under a minute - all locally on my computer `npm i -g executor`
0:57

This is why you want an execution layer share your tools, auth and approvals across all agents you use Check out me adding the Axiom MCP server, authing to it, and calling it from Claude Code and OpenCode in under a minute - all locally on my computer `npm i -g executor`

Rhys

29,958 次观看 • 4 个月前

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 次观看 • 1 年前

I finally got around to dogfooding my own ntm tool for managing a bunch of agents, and... it's not bad? I made an ntm skill for Claude Code and then simply told it to use ntm with 10 CCs and 5 Codex instances on a new project that I just finished making the beads for. Easy.😎
14:54

I finally got around to dogfooding my own ntm tool for managing a bunch of agents, and... it's not bad? I made an ntm skill for Claude Code and then simply told it to use ntm with 10 CCs and 5 Codex instances on a new project that I just finished making the beads for. Easy.😎

Jeffrey Emanuel

17,578 次观看 • 5 个月前

I made my OpenClaw agent more powerful by building my own harness (yes, I used that word) My agent has access to it's own computer (courtesy of Daytona), email, phone number, and credit card Been working on this for the last 2 months, excited to share this with you all
14:41

I made my OpenClaw agent more powerful by building my own harness (yes, I used that word) My agent has access to it's own computer (courtesy of Daytona), email, phone number, and credit card Been working on this for the last 2 months, excited to share this with you all

Micky

47,436 次观看 • 2 个月前

This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.
7:45

This little device lets your agents do what they do best, whilst making sure you approve every important action. This is a Ledger Nano Gen5, a hardware signer that keeps your accounts secure. You can install a CLI and a set of skills in your projects. These will enable your agents to send their plan to the signer, so you can approve it. This is awesome: 1. You can have an automated agentic workflow 2. Your agents can't make costly mistakes Check the video I recorded to see how this works. You can run all of this in two commands: 1. Install the CLI 2. Install the skills for your agent From here, the agent can query my Ethereum accounts, check balances, and even initiate transactions as long as my Ledger Nano Gen5 is plugged into the computer. My keys are never stored in my computer and never shared with the agent. This is huge! Also, while the agent can set up transactions, it can't execute them unless a human approves them using the device. Pretty awesome! Thanks to the Ledger team for partnering with me on this post.

Santiago

43,749 次观看 • 19 天前

I gave Nous Research Hermes agent a company brain. I've been LOVING Hermes agent as my personal assistant but haven't been able to use it for work. AI agents are powerful but they are siloed. They don’t have access to the same files, comments, versions, or decisions your team is working from. So I connected Hermes to Box. Now my Hermes agent can work from Slack, use files, comments, and decisions stored on Box as the single source of truth, and get shi* done.
6:33

I gave Nous Research Hermes agent a company brain. I've been LOVING Hermes agent as my personal assistant but haven't been able to use it for work. AI agents are powerful but they are siloed. They don’t have access to the same files, comments, versions, or decisions your team is working from. So I connected Hermes to Box. Now my Hermes agent can work from Slack, use files, comments, and decisions stored on Box as the single source of truth, and get shi* done.

Chris Kim

40,470 次观看 • 22 天前

Hermes Agent now has a desktop app! I made a quick video showing how easy it is to install, set up, and get to work! It perfectly synced with my existing agent, or you can use it to make a new one. Plus, a quick tour of the features! It's never been easier to start with Hermes!
4:52

Hermes Agent now has a desktop app! I made a quick video showing how easy it is to install, set up, and get to work! It perfectly synced with my existing agent, or you can use it to make a new one. Plus, a quick tour of the features! It's never been easier to start with Hermes!

tonbi

17,424 次观看 • 1 个月前