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Mercury's Command is probably the best designed agentic experience out there. Their design team sweats the smallest details. Take agent states for example. Most agentic products just display in plain text that the agent is thinking. But Mercury design team asked a different question. How do you know a... show more

Jayneil Dalal

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11,123 просмотров • 14 дней назад •via X (Twitter)

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Top designer at Mercury shows her agentic design explorations in Figma. Designers working on AI features should copy this and sketch how the LLM works before prototyping. 👀 Full conversation on Sneak Peek: carol shows her thought process for designing Mercury Command in Figma👇

Top designer at Mercury shows her agentic design explorations in Figma. Designers working on AI features should copy this and sketch how the LLM works before prototyping. 👀 Full conversation on Sneak Peek: carol shows her thought process for designing Mercury Command in Figma👇

Jayneil Dalal

19,651 просмотров • 17 дней назад

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. CopilotKit🪁 is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: Thanks to the CopilotKit team for partnering with me on this post.

Santiago

17,438 просмотров • 22 дней назад

Imagine Workflows just went agentic today. Most "agentic creativity" out there is just image and video generations. Workflows is different. Hundreds of nodes tied together, each passing context to the next, built for the connected work real storytelling demands. Until today, wiring all of that took a power user with patience to spare. Now you describe what you want, and the agent builds the chain for you. The most powerful creative tool we've built, now moves at the pace of your thought.

Imagine Workflows just went agentic today. Most "agentic creativity" out there is just image and video generations. Workflows is different. Hundreds of nodes tied together, each passing context to the next, built for the connected work real storytelling demands. Until today, wiring all of that took a power user with patience to spare. Now you describe what you want, and the agent builds the chain for you. The most powerful creative tool we've built, now moves at the pace of your thought.

ImagineArt

1,073,530 просмотров • 17 дней назад

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 просмотров • 3 месяцев назад

The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.

The freedom to build with any agent framework you want is analogous to the freedom of religion, and this is a core value for the Virtuals society. Here are a few ways we are opening up support for every autonomous agentic framework out there: Today: - Enabling agent creators to specify which framework the agent runs on - Release of Terminal API, which allows agent builders using other agent frameworks to stream their thoughts and activities live on their agent pages. For a guide on how to use this, refer to: - Ability for agents running on any framework to publicly list their capabilities, a key enabler for the agent-to-agent marketplace Soon: - Integration into the agentic commerce standard and registry. Agents across frameworks being able to orchestrate and pay for tasks among themselves - ⁠Multi agent orchestration across frameworks. Imagine building an autonomous business with agents from different religions Welcome to the Virtuals society.

Virtuals Protocol

216,105 просмотров • 1 год назад

Airtable's Howie Liu says that basically everyone will need to graduate from being ICs to ICs that manage teams of 20-30 agents: "The best developers today don't just sit there in front of their IDEs and synchronously talk to their agent." "[Instead], you have like 30 separate branches that are each being worked on by a different agent. And you can have the agents continue to update the branches based on human and other agent feedback." "And I think this whole idea of it taking hours for that entire loop to complete — agent pushes some changes, the changes get feedback from other agents or humans, the agent responds to that — that whole loop could be hours, not just minutes. So you're not going to just sit there and watch it one at a time." "But the powerful thing about this is, each one is still actually operating faster than a human engineer. One agent on one branch can do the work of maybe three humans, operating 3x as fast. So it's like a 10x leverage factor just for one agent." "But the best engineers are now able to multitask and say, 'I'm going to oversee my own little team of 20-30 agents working concurrently.'" "Everyone needs to graduate from being an IC to an IC manager of agents. Meaning, if you're a VC analyst, your job should no longer be to go synchronously research one company. You need to go and research like 30 companies, and do them all faster, better, and higher quality than you could before." "That's the greatest leap that is going to be challenging for a lot of people in a lot of roles. Because it's a totally different mentality in how you operate, and what your role is."

Airtable's Howie Liu says that basically everyone will need to graduate from being ICs to ICs that manage teams of 20-30 agents: "The best developers today don't just sit there in front of their IDEs and synchronously talk to their agent." "[Instead], you have like 30 separate branches that are each being worked on by a different agent. And you can have the agents continue to update the branches based on human and other agent feedback." "And I think this whole idea of it taking hours for that entire loop to complete — agent pushes some changes, the changes get feedback from other agents or humans, the agent responds to that — that whole loop could be hours, not just minutes. So you're not going to just sit there and watch it one at a time." "But the powerful thing about this is, each one is still actually operating faster than a human engineer. One agent on one branch can do the work of maybe three humans, operating 3x as fast. So it's like a 10x leverage factor just for one agent." "But the best engineers are now able to multitask and say, 'I'm going to oversee my own little team of 20-30 agents working concurrently.'" "Everyone needs to graduate from being an IC to an IC manager of agents. Meaning, if you're a VC analyst, your job should no longer be to go synchronously research one company. You need to go and research like 30 companies, and do them all faster, better, and higher quality than you could before." "That's the greatest leap that is going to be challenging for a lot of people in a lot of roles. Because it's a totally different mentality in how you operate, and what your role is."

TBPN

35,595 просмотров • 3 месяцев назад

The best hackers I know aren't smarter than me. They've just read more than me. Somewhere in their memory is the one obscure writeup that turns a dead end into a finding. Preview is that memory, on tap. For you, and for your agent. ->

The best hackers I know aren't smarter than me. They've just read more than me. Somewhere in their memory is the one obscure writeup that turns a dead end into a finding. Preview is that memory, on tap. For you, and for your agent. ->

0xrudra

122,018 просмотров • 23 дней назад

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Here is how simple it is to build a UI front-end for an AI agent (assuming you are using . The video here is 90 seconds and will show you everything you need to connect an agent to a web app: 1. Set up CopilotKit🪁 in your app 1. Copy the agent's localhost URL from LangGraph Studio 2. Paste that URL into a CopilotKit🪁 remote endpoint That's all you need to get a front-end to interact with the agent! The secret sauce here is CoAgents, an open-source library. You can use CoAgents to integrate any agent with your web application. Here is what you get: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Watch the attached demo so you see it in action.

Santiago

46,109 просмотров • 1 год назад

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Most AI tools today are just command-takers. You type, it responds. You click, it executes. There's truly no learning, no memory, no adaptation. You're still doing most of the work. Which is why Miles by Avo on Solana would make so much sense if built well. From what I understand so far, miles learns about you over time, adapts to how you operate, and figures out the tools you struggle to navigate on your own. Less clicking, more conversation, the more you use it, the better it gets at working with you, not just for you. That shift from command-follower to learning companion is bigger than it sounds. It's the difference between a tool and an agent that actually knows you. The second one is Agent Grid and this one is for builders and power users. But as I mentioned in this video, I'm still studying the Agent Grid. I'd come talk about it once I learn properly about it. Souren just keeps shipping!

Sir Khaycee

20,419 просмотров • 4 месяцев назад

Build a loan underwriting agent workflow in ~5 mins 💵🤖 (No code, just English) I built an agentic workflow that can process incoming PDFs of financial statements: checking statements, bank statements, brokerage accounts, and extract/normalize the financials in each source of data. I did this by specifying the schemas and classification logic in plain English; our agent compiles this into a deterministic workflow with calls to agentic parsing to extract out all the information that you need. Our agent builder is now in LlamaCloud, you can sign up and try it yourself! Blog: Sign up:

Build a loan underwriting agent workflow in ~5 mins 💵🤖 (No code, just English) I built an agentic workflow that can process incoming PDFs of financial statements: checking statements, bank statements, brokerage accounts, and extract/normalize the financials in each source of data. I did this by specifying the schemas and classification logic in plain English; our agent compiles this into a deterministic workflow with calls to agentic parsing to extract out all the information that you need. Our agent builder is now in LlamaCloud, you can sign up and try it yourself! Blog: Sign up:

Jerry Liu

30,015 просмотров • 5 месяцев назад

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---

echo.hive

88,611 просмотров • 1 год назад

This is how the entire Firefox design team work out of just 1 Figma file 🤯 It solves the biggest issue on design teams - what is everyone working on? I have worked on design teams where designers had no clue what others were working on This approach also eliminates the need for weekly 1 hour standups where everyone has to present what they are working on 👀 Watch the full interview on Sneak Peek: topotropic 👉🏻 @topotropic@publicidentity.net shows how Firefox 🔥 design team works out of a single Figma file👇

This is how the entire Firefox design team work out of just 1 Figma file 🤯 It solves the biggest issue on design teams - what is everyone working on? I have worked on design teams where designers had no clue what others were working on This approach also eliminates the need for weekly 1 hour standups where everyone has to present what they are working on 👀 Watch the full interview on Sneak Peek: topotropic 👉🏻 @[email protected] shows how Firefox 🔥 design team works out of a single Figma file👇

Jayneil Dalal

19,965 просмотров • 10 месяцев назад

CNN’s Josh Campbell admits the ICE officer’s cellphone video of his deadly confrontation with Renee Good that the thud heard at the end *might be* sound of the officer being “struck by that vehicle,” but it *might NOT* and instead “could be from you know, the phone on his clothing.” He adds the video showed “wasn’t, you know, coming head on at that agent, you know, trying to mow him over.” “Well, this is a critical new angle. This was the actual vantage point of the officer. And we know that not many immigration officers actually wear body worn cameras. But, in this instance, the immigration agent was holding his phone up, essentially filming this encounter, which we can now see in this video obtained by our colleague Holmes Lybrand. So, in the video, you see that the officers are making contact with Renee Good there. She’s parked somewhat perpendicular in a street. And there’s another woman that’s there, which is outside the vehicle. And you can tell, you know, as the exchange there with Renee Good that, you know, she’s being somewhat pleasant. The other woman is, you know, kind of mouthy. And, you know, this is obviously a bit a bit tense, you tells the agent at one point, you know, ‘why don’t you go get some lunch, big boy?’ You also see critically. And I’ll get to this in a second. Bystanders behind the vehicle. We’ve seen other angles. This one shows us again, the vantage point of that officer. Another agent orders Renee Good out of the vehicle. This agent then walks around. That is the moment that she then takes off. And, on that audio, what we hear, it appears that you can actually hear friction on the phone. You know, it appears from other angles that the agent was indeed struck by that vehicle. But you hear on the audio, it appears friction on the phone, that could be from the vehicle strike. That could be from you know, the phone on his clothing. But then it appears three shots were fired after that. And then you hear afterwards someone, it appears, maybe an immigration agent who was there, actually, you know, use profanity, you know, calling — calling her an f-ing B, which we heard on — on the audio there. Now, let’s talk about the tactics for a second so I can tell you this, as a former federal agent, that law enforcement officers can use deadly force only when necessary, when they believe that there is an imminent threat to their life. In this instance, you have a moving vehicle that’s coming at that agent. “Now, the driver, Renee Good, did turn the wheel to the right. So, it appears that she wasn’t, you know, coming head on at that agent, you know, trying to mow him over. But nevertheless, an agent in that split second decision would have to make that calculation. Am I in danger? Is my life threatened here? You know, agents are also taught What are other options? Could you move out of the way? And that’s why this has been such a contentious issue here — the judgment, the decision of that agent to actually open fire. We’ve heard, you know, obviously, people supporting that decision, others, being quite critical. One other thing I want to note is that when agents undergo training and not just the feds, but law enforcement across the country, it is a cardinal rule that you are responsible for every round that you fire. And one key component of that is to the extent that you have the time and you can, you must be aware of what is beyond your shot, what is in the background? Here, it appears from that video that this other woman who had been engaging in the agent with the agent, was quite clearly in the line of fire. But there were also other people that were standing behind that car on the sidewalk. And this is a residential area. And so, I’m sure that will face some scrutiny as well. The officer opening fire at that moment, you know, directly, it appears towards those other people beyond the shot. And then his partners. And you brought this up, Brianna, just the other day, his partners are there in close range as well. And so, this will be heavily scrutinized from a tactical perspective as well. About the officer opening fire, but again, just critical, critical new video here that we’re seeing an important angle what the officer would have been seeing in the moment.”

CNN’s Josh Campbell admits the ICE officer’s cellphone video of his deadly confrontation with Renee Good that the thud heard at the end might be sound of the officer being “struck by that vehicle,” but it might NOT and instead “could be from you know, the phone on his clothing.” He adds the video showed “wasn’t, you know, coming head on at that agent, you know, trying to mow him over.” “Well, this is a critical new angle. This was the actual vantage point of the officer. And we know that not many immigration officers actually wear body worn cameras. But, in this instance, the immigration agent was holding his phone up, essentially filming this encounter, which we can now see in this video obtained by our colleague Holmes Lybrand. So, in the video, you see that the officers are making contact with Renee Good there. She’s parked somewhat perpendicular in a street. And there’s another woman that’s there, which is outside the vehicle. And you can tell, you know, as the exchange there with Renee Good that, you know, she’s being somewhat pleasant. The other woman is, you know, kind of mouthy. And, you know, this is obviously a bit a bit tense, you tells the agent at one point, you know, ‘why don’t you go get some lunch, big boy?’ You also see critically. And I’ll get to this in a second. Bystanders behind the vehicle. We’ve seen other angles. This one shows us again, the vantage point of that officer. Another agent orders Renee Good out of the vehicle. This agent then walks around. That is the moment that she then takes off. And, on that audio, what we hear, it appears that you can actually hear friction on the phone. You know, it appears from other angles that the agent was indeed struck by that vehicle. But you hear on the audio, it appears friction on the phone, that could be from the vehicle strike. That could be from you know, the phone on his clothing. But then it appears three shots were fired after that. And then you hear afterwards someone, it appears, maybe an immigration agent who was there, actually, you know, use profanity, you know, calling — calling her an f-ing B, which we heard on — on the audio there. Now, let’s talk about the tactics for a second so I can tell you this, as a former federal agent, that law enforcement officers can use deadly force only when necessary, when they believe that there is an imminent threat to their life. In this instance, you have a moving vehicle that’s coming at that agent. “Now, the driver, Renee Good, did turn the wheel to the right. So, it appears that she wasn’t, you know, coming head on at that agent, you know, trying to mow him over. But nevertheless, an agent in that split second decision would have to make that calculation. Am I in danger? Is my life threatened here? You know, agents are also taught What are other options? Could you move out of the way? And that’s why this has been such a contentious issue here — the judgment, the decision of that agent to actually open fire. We’ve heard, you know, obviously, people supporting that decision, others, being quite critical. One other thing I want to note is that when agents undergo training and not just the feds, but law enforcement across the country, it is a cardinal rule that you are responsible for every round that you fire. And one key component of that is to the extent that you have the time and you can, you must be aware of what is beyond your shot, what is in the background? Here, it appears from that video that this other woman who had been engaging in the agent with the agent, was quite clearly in the line of fire. But there were also other people that were standing behind that car on the sidewalk. And this is a residential area. And so, I’m sure that will face some scrutiny as well. The officer opening fire at that moment, you know, directly, it appears towards those other people beyond the shot. And then his partners. And you brought this up, Brianna, just the other day, his partners are there in close range as well. And so, this will be heavily scrutinized from a tactical perspective as well. About the officer opening fire, but again, just critical, critical new video here that we’re seeing an important angle what the officer would have been seeing in the moment.”

Curtis Houck

68,500 просмотров • 6 месяцев назад

Anthropic Head of Product: "Fable 5 is the best model we've ever shipped for agentic work. Our engineers now ship 8x more code than they did in past years." in 42 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5: managed agents, dreaming, dynamic workflows worth more than a $500 agent building course live from Anthropic's latest stage in Japan, just hours ago i turned everything they showed on stage into a step-by-step guide on building your own agent you can find the full guide below

Anthropic Head of Product: "Fable 5 is the best model we've ever shipped for agentic work. Our engineers now ship 8x more code than they did in past years." in 42 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5: managed agents, dreaming, dynamic workflows worth more than a $500 agent building course live from Anthropic's latest stage in Japan, just hours ago i turned everything they showed on stage into a step-by-step guide on building your own agent you can find the full guide below

Anatoli Kopadze

79,808 просмотров • 1 месяц назад

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

Harjot Gill

4,774,714 просмотров • 3 месяцев назад

Moltbook creator Matt Schlicht explaining how his social network for AI agents works: "It's kind of like you are imprinting part of your soul onto the bot... It's like Tamagotchi or Pokemon times 1,000." "The agent signs up for an account, then they're told they should check back in on a regular basis to check their feed." "Moltbook's not telling them what to talk about. It's not suggesting what they should do. All that is entirely up to the AI agent." "The agent has its own context that it's built up by interacting with its human. That context is how it's making decisions on what to post about." "If somebody is talking to their bot a lot about physics, then probably their bot is going to have a proclivity for posting about physics."

Moltbook creator Matt Schlicht explaining how his social network for AI agents works: "It's kind of like you are imprinting part of your soul onto the bot... It's like Tamagotchi or Pokemon times 1,000." "The agent signs up for an account, then they're told they should check back in on a regular basis to check their feed." "Moltbook's not telling them what to talk about. It's not suggesting what they should do. All that is entirely up to the AI agent." "The agent has its own context that it's built up by interacting with its human. That context is how it's making decisions on what to post about." "If somebody is talking to their bot a lot about physics, then probably their bot is going to have a proclivity for posting about physics."

TBPN

15,631 просмотров • 5 месяцев назад

🔎🤖LangSmith Insights Agent Really excited to launch our first in-product agent This agent lives inside LangSmith and combs through traces, giving you insights into: 🧑‍🤝‍🧑how users are using your agent ⁉️how your agent may be messing up 🛃{your custom insight here} The problem we saw was that people were launching agents... and didn't know how their users were actually using them! You put a chat box in front of people, and they may ask it anything - the surface area for agents is often super wide In addition - agents would fail silently. They could give a bad response - this wouldn't show up in error logs, but its good to know. If you know what look for, you can set up LLM as a judge evaluators. But what if you don't? (most people don't initially) The best way to figure this out - as Hamel Husain says - "look at your data". But LLMs are really good at looking at your data! So can they do it for you? This is exactly what insights agent attempts to do. It's live in LangSmith today. You can read more about it here:

🔎🤖LangSmith Insights Agent Really excited to launch our first in-product agent This agent lives inside LangSmith and combs through traces, giving you insights into: 🧑‍🤝‍🧑how users are using your agent ⁉️how your agent may be messing up 🛃{your custom insight here} The problem we saw was that people were launching agents... and didn't know how their users were actually using them! You put a chat box in front of people, and they may ask it anything - the surface area for agents is often super wide In addition - agents would fail silently. They could give a bad response - this wouldn't show up in error logs, but its good to know. If you know what look for, you can set up LLM as a judge evaluators. But what if you don't? (most people don't initially) The best way to figure this out - as Hamel Husain says - "look at your data". But LLMs are really good at looking at your data! So can they do it for you? This is exactly what insights agent attempts to do. It's live in LangSmith today. You can read more about it here:

Harrison Chase

98,520 просмотров • 9 месяцев назад

This is how a designer at Slack organizes their design iterations in Figma Every designer should copy this approach and keep a daily log of their design iterations. Doesn't have to be super structured but just date and name for each design iteration Biggest benefit I see for using this approach is building trust with your design manager and stakeholders as they can go into your Figma file and visualize your progress/efforts Also scares away micro managers😉 👀 Watch the full interview on Sneak Peek: andrew gosine 🌲 shows how he organizes design iterations for Slack in Figma 👇

This is how a designer at Slack organizes their design iterations in Figma Every designer should copy this approach and keep a daily log of their design iterations. Doesn't have to be super structured but just date and name for each design iteration Biggest benefit I see for using this approach is building trust with your design manager and stakeholders as they can go into your Figma file and visualize your progress/efforts Also scares away micro managers😉 👀 Watch the full interview on Sneak Peek: andrew gosine 🌲 shows how he organizes design iterations for Slack in Figma 👇

Jayneil Dalal

120,695 просмотров • 11 месяцев назад

Nobody cares about chatbots anymore. You want AI agents that can reason, plan, collaborate, and solve tasks. That's what modern AI is all about. At AI Agents Conference 2026, ASI:One was trending because people used their agent to order their coffee. People asked for their orders in natural language, and the agent handled the rest, processing more than 1,000 orders during the conference without a hitch. This is how it works behind the scenes:

Nobody cares about chatbots anymore. You want AI agents that can reason, plan, collaborate, and solve tasks. That's what modern AI is all about. At AI Agents Conference 2026, ASI:One was trending because people used their agent to order their coffee. People asked for their orders in natural language, and the agent handled the rest, processing more than 1,000 orders during the conference without a hitch. This is how it works behind the scenes:

Santiago

17,867 просмотров • 2 месяцев назад