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introducing kenkyu kenkyu is an autonomous research agent that I made a while back. it is designed to transform a single user query into a comprehensive, structured report. how it works : - it takes user input as query - plans the next course of action - researches about... show more

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Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

Introducing the Generative UI Research Canvas ✨ Every agent needs a powerful UI Combine LangChain, Tavily, Tako and CopilotKit🪁 and you get a LangChain-based research agent with a Generative UI-driven frontend. When agents can explore the underlying data, research becomes more trustworthy and useful. How does this work? - LangChain manages the agent workflow and tool orchestration. - Tako converts retrieved data into structured, visual components - Tavily provides agentic search - CopilotKit renders those components in the UI and keeps the user in the loop while the agent is working So instead of a boring and static report, the output is an interactive research canvas that evolves as the agent runs. 2026 is the year where users want to see the output, not read it... Generative UI makes this possible for agentic applications. Tutorial: Open-Source Repo: Docs:

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Introducing `OpenDeepResearcher` 🌎 An open-source AI agent that does comprehensive research for you. Just provide a topic, and the AI will go off, do research, and return a comprehensive report. How it works:

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

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"America is about the synthesis of a lot of different cultures, and beauty arises out of that synthesis... Is it a gangster samurai hip-hop Eastern western? I want it to have elements of all those things, but not be any" one" Jim Jarmusch ~ Ghost Dog's 25th anniversary

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thinking a lot about films as portals to making us feel and that the whole point of building out AI and agentic systems.. is to create a future that feels good a visual reminder to myself on how I want the future to feel..and that the future is now, so let's feel it all now

thinking a lot about films as portals to making us feel and that the whole point of building out AI and agentic systems.. is to create a future that feels good a visual reminder to myself on how I want the future to feel..and that the future is now, so let's feel it all now

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Introducing /visual-plan - a skill to generate rich, visual plans for Claude Code and Codex. Plan mode in Claude Code is incredible. But I always find my eyes glazing over when it gives me this huge markdown essay in my terminal. I found I can make much better visual plans with reusable components. So I made a skill called `/visual-plan`. It generates plans as MDX with visual, interactive components. Diagrams, interactive API specs, schema design changes, annotated code, and even pan and zoomable wireframes. So for any UI work, you can look at a wireframe first, comment on it, iterate, and then have the agent work. I’ve found this to be a much more intuitive interface for reasoning about what the agent is doing. It’s somewhat inspired by that popular post about how HTML is better than Markdown. But HTML can be slow and verbose to write. And it doesn’t look good checked into a repo. This has really made me feel like humans and engineering are entering a new abstraction phase, where we reason about things at the plan level. As long as the plan is good, agents are getting more and more reliable at executing on it. Almost to the degree that we trust the C compiler to compile to assembly reliably. Plans are the new intermediate representation. I also made a skill for the reverse of this, called `/visual-recap`. After the agent works, it gives you a recap of everything it did. Same idea: wireframes, interactive API specs and diffs, schemas, annotated code, etc. So now when you’re reviewing what the agent did for you, or looking at a pull request of somebody else’s code, you can see a visual recap instead of just reading a wall of text. It’s all free and open source. You can find it on my GitHub. Will link to it in the reply because we all know how dumb these algorithms are with links.

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Andrew Ng

105,343 Aufrufe • vor 1 Jahr

Microsoft CEO Nadella explains how the real power of AI is transformation. Docs become websites, websites become apps, and firms scale decisions that once took hours into minutes. "what’s the real difference between a document, a website, and an application? It’s the lack of software that can transform itself. Interestingly enough, AI finally gives us that. I can write a document and say, “No, I don’t want it as a document. I want it as a website,” and it will transform that document, using code, into a website. Then I can say, “I don’t like the website. I want an app,” and it will write more code to transform it." --- Microsoft CEO Satya Nadella and BlackRock Chairman & CEO Larry Fink at the World Economic Forum in Davos. From 'DRM News' YT Channel (link in comment)

Microsoft CEO Nadella explains how the real power of AI is transformation. Docs become websites, websites become apps, and firms scale decisions that once took hours into minutes. "what’s the real difference between a document, a website, and an application? It’s the lack of software that can transform itself. Interestingly enough, AI finally gives us that. I can write a document and say, “No, I don’t want it as a document. I want it as a website,” and it will transform that document, using code, into a website. Then I can say, “I don’t like the website. I want an app,” and it will write more code to transform it." --- Microsoft CEO Satya Nadella and BlackRock Chairman & CEO Larry Fink at the World Economic Forum in Davos. From 'DRM News' YT Channel (link in comment)

Rohan Paul

15,710 Aufrufe • vor 25 Tagen

Microsoft CEO Nadella explains how the real power of AI is transformation. Docs become websites, websites become apps, and firms scale decisions that once took hours into minutes. "what’s the real difference between a document, a website, and an application? It’s the lack of software that can transform itself. Interestingly enough, AI finally gives us that. I can write a document and say, “No, I don’t want it as a document. I want it as a website,” and it will transform that document, using code, into a website. Then I can say, “I don’t like the website. I want an app,” and it will write more code to transform it." --- Microsoft CEO Satya Nadella and BlackRock Chairman & CEO Larry Fink at the World Economic Forum in Davos. From 'DRM News' YT Channel (link in comment)

Microsoft CEO Nadella explains how the real power of AI is transformation. Docs become websites, websites become apps, and firms scale decisions that once took hours into minutes. "what’s the real difference between a document, a website, and an application? It’s the lack of software that can transform itself. Interestingly enough, AI finally gives us that. I can write a document and say, “No, I don’t want it as a document. I want it as a website,” and it will transform that document, using code, into a website. Then I can say, “I don’t like the website. I want an app,” and it will write more code to transform it." --- Microsoft CEO Satya Nadella and BlackRock Chairman & CEO Larry Fink at the World Economic Forum in Davos. From 'DRM News' YT Channel (link in comment)

Rohan Paul

25,556 Aufrufe • vor 5 Monaten

When I’m trying to improve the user experience of my applications, one of the most valuable things is being able to see an entire user flow as a storyboard. Not just one screen or screenshot at a time. This is something I love using the `/visual-plan` skill for. You can describe any flow you want, and the agent will look through your code and wireframe out a storyboard of what the flow looks like. Then you can visualize the steps in a simplified way and spot areas to improve. Recently, I found that in certain flows we were still asking for organizations, even though I thought I had gotten rid of that and made it automatic. A quick storyboard let me see all the different code paths in a simple, visual, intuitive way. Spot the areas of the flow I didn’t want. And have the agent fix it. Sign up, onboarding, and setup flows are usually some of the most important experiences in your app. And usually the least looked at. Especially because it can be hard to reproduce every flow, for every situation, for every user type, feature flag, or whatever else you have. The `/visual-plan` skill lets you visualize any part of your code. Either to understand the current state, plan out a new state, or recap updates that were made. I’m pretty addicted to this skill. I use it for a lot of other things too, so let me know if you want to see videos on those. And of course it’s all open source. You can grab it on my GitHub. I'll link to it in the thread. If you try it, let me know your feedback.

When I’m trying to improve the user experience of my applications, one of the most valuable things is being able to see an entire user flow as a storyboard. Not just one screen or screenshot at a time. This is something I love using the `/visual-plan` skill for. You can describe any flow you want, and the agent will look through your code and wireframe out a storyboard of what the flow looks like. Then you can visualize the steps in a simplified way and spot areas to improve. Recently, I found that in certain flows we were still asking for organizations, even though I thought I had gotten rid of that and made it automatic. A quick storyboard let me see all the different code paths in a simple, visual, intuitive way. Spot the areas of the flow I didn’t want. And have the agent fix it. Sign up, onboarding, and setup flows are usually some of the most important experiences in your app. And usually the least looked at. Especially because it can be hard to reproduce every flow, for every situation, for every user type, feature flag, or whatever else you have. The `/visual-plan` skill lets you visualize any part of your code. Either to understand the current state, plan out a new state, or recap updates that were made. I’m pretty addicted to this skill. I use it for a lot of other things too, so let me know if you want to see videos on those. And of course it’s all open source. You can grab it on my GitHub. I'll link to it in the thread. If you try it, let me know your feedback.

Steve (Builder.io)

146,687 Aufrufe • vor 16 Tagen

Here is a huge trick to cut down on hallucinations: I have an agent that processes phone receipts using GPT-4o. Whenever I go to a restaurant or pay for something, I take a photo of the receipt and upload it to Google Drive. The agent goes through the folder and extracts the following information from each receipt: • Name of the place • Category of the place • Amount • Tax amount • Tip amount Each field requires an individual prompt. The result is a JSON object that I export to a separate folder. This is awesome, but there's a problem: The agent occasionally makes mistakes when interpreting a receipt; unfortunately, I couldn't detect them as they happened. Here is the trick: I'm now using a challenger model (o3-mini) to check the work of GPT-4o. This increases the cost of processing receipts but reduces the mistakes to almost nothing. The best part: 1. Whenever the challenger disagrees with the main model, I produce detailed information about the problem to improve the prompts and make the extraction more robust. 2. It's very easy to turn this process into a Human-in-the-Loop workflow where I get notifications of every disagreement and an opportunity to settle them. I recorded the attached video to show you how this works using Unstract.

Here is a huge trick to cut down on hallucinations: I have an agent that processes phone receipts using GPT-4o. Whenever I go to a restaurant or pay for something, I take a photo of the receipt and upload it to Google Drive. The agent goes through the folder and extracts the following information from each receipt: • Name of the place • Category of the place • Amount • Tax amount • Tip amount Each field requires an individual prompt. The result is a JSON object that I export to a separate folder. This is awesome, but there's a problem: The agent occasionally makes mistakes when interpreting a receipt; unfortunately, I couldn't detect them as they happened. Here is the trick: I'm now using a challenger model (o3-mini) to check the work of GPT-4o. This increases the cost of processing receipts but reduces the mistakes to almost nothing. The best part: 1. Whenever the challenger disagrees with the main model, I produce detailed information about the problem to improve the prompts and make the extraction more robust. 2. It's very easy to turn this process into a Human-in-the-Loop workflow where I get notifications of every disagreement and an opportunity to settle them. I recorded the attached video to show you how this works using Unstract.

Santiago

233,915 Aufrufe • vor 1 Jahr