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Check out the form-filling agent that automates PDF forms using AI by Jerry Liu 📄🤖 Use any fillable PDF with an agent that fills it out based on your prompts and context files. Our new experiment creates a multi-turn chat experience for form completion. 🔍 Upload fillable PDFs and...

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LlamaIndex 🦙

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55,131 次观看 • 8 个月前 •via X (Twitter)

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I made an AI agent that can fill out complicated forms from unstructured context 📝 For instance: automatically fill out your expense report 💳 by drag and dropping 5-10 receipt pictures/scans 🧾 Uses Claude Agent SDK + LlamaParse to parse unstructured docs + custom tools for form understanding. It now semantically understands each field, handles multi-turn conversations, and lets you drag up to 10 files. Deployed on Vercel/Render. App: Repo: LlamaCloud:
0:39

I made an AI agent that can fill out complicated forms from unstructured context 📝 For instance: automatically fill out your expense report 💳 by drag and dropping 5-10 receipt pictures/scans 🧾 Uses Claude Agent SDK + LlamaParse to parse unstructured docs + custom tools for form understanding. It now semantically understands each field, handles multi-turn conversations, and lets you drag up to 10 files. Deployed on Vercel/Render. App: Repo: LlamaCloud:

Jerry Liu

34,998 次观看 • 8 个月前

I built a form-filling agent that anyone can use 💫 This is an extremely simple but useful (I hope) app. Upload a fillable form 📋, some context files, and chat with the agent to fill the form out automatically ✍️ 1️⃣ Yes it is a Claude Code SDK wrapper 2️⃣ It is better and faster than ChatGPT/Claude UI out of the box 3️⃣ We use LlamaParse to parse the context files, so you can have more trust that we are able to read context without hallucinations (e.g. messy scanned handwriting, drivers license photo, and more). This was one of my holiday Claude Code vibe-coding projects. Built with Opus 4.5, and also powered by Opus 4.5. Feeling the AGI 🫡 App is here: Repo is open-source:
1:48

I built a form-filling agent that anyone can use 💫 This is an extremely simple but useful (I hope) app. Upload a fillable form 📋, some context files, and chat with the agent to fill the form out automatically ✍️ 1️⃣ Yes it is a Claude Code SDK wrapper 2️⃣ It is better and faster than ChatGPT/Claude UI out of the box 3️⃣ We use LlamaParse to parse the context files, so you can have more trust that we are able to read context without hallucinations (e.g. messy scanned handwriting, drivers license photo, and more). This was one of my holiday Claude Code vibe-coding projects. Built with Opus 4.5, and also powered by Opus 4.5. Feeling the AGI 🫡 App is here: Repo is open-source:

Jerry Liu

48,466 次观看 • 8 个月前

I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:
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I vibe-coded an AI agent for making presentations 🤖🖼️ - accessible to everyone and open-source! You can upload both your context files, as well as a style template (a file that captures the style of the presentation you want to create). Then you launch a prompt and watch the agent one-shot your presentation. After the presentation is generated, you can edit the text inline, chat with the agent to add/modify/delete slides, and export to Powerpoint or PDF. Built with Claude Code, powered by Claude Agent SDK + LlamaParse. Hosted on Vercel/Render. To use it, you do need to specify your LlamaCloud API key and Anthropic API key (see below). App: Repo: Sign up to LlamaCloud:

Jerry Liu

21,073 次观看 • 8 个月前

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:
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New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

82,067 次观看 • 1 年前

new experiment: "smart" forms (for onboarding, wizards, etc) LLMs aren't just about chat based conversations; here I'm using an LLM/web search to infer form values while onboarding on to new app/service called Trellis. With just a name and company, it can guess some fields and values that can dramatically reduce the time to enter (and make it a little more delightful?) shoutout to Raindrop where I saw this first.
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new experiment: "smart" forms (for onboarding, wizards, etc) LLMs aren't just about chat based conversations; here I'm using an LLM/web search to infer form values while onboarding on to new app/service called Trellis. With just a name and company, it can guess some fields and values that can dramatically reduce the time to enter (and make it a little more delightful?) shoutout to Raindrop where I saw this first.

sunil pai

49,211 次观看 • 1 个月前

sam altman: - someday everyone should have a personal robot. - we will definitely build humanoid robots, while also exploring other form factors. - the form factor is less important than figuring out the AI brain that makes the robot work. - he also agreed on building a kind of “super-app” that combines ChatGPT and Codex for AI. - he wants one AI interface that can do whatever you need. - it could use your computer and access your context when needed. - you shouldn't have to choose tabs, tools, or modes. AI should figure it out. - AI should be smart enough to understand what you want and act on it.
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sam altman: - someday everyone should have a personal robot. - we will definitely build humanoid robots, while also exploring other form factors. - the form factor is less important than figuring out the AI brain that makes the robot work. - he also agreed on building a kind of “super-app” that combines ChatGPT and Codex for AI. - he wants one AI interface that can do whatever you need. - it could use your computer and access your context when needed. - you shouldn't have to choose tabs, tools, or modes. AI should figure it out. - AI should be smart enough to understand what you want and act on it.

SciTech Era

31,585 次观看 • 25 天前

Introducing 🍿Popcorn - the 1st Movie Agent that produces long-form videos! This is not just an AI video app. It does the workflow for you. Like and RT for credits to make your first movies. 👇
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Introducing 🍿Popcorn - the 1st Movie Agent that produces long-form videos! This is not just an AI video app. It does the workflow for you. Like and RT for credits to make your first movies. 👇

Adi Sideman

17,075 次观看 • 1 年前

🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:
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🚀 The team at Google just released the Agents API, a service for building and running custom agents inside a sandboxed Linux environment, and we built a template that gives these agents access to LlamaParse / LiteParse, enabling them to process unstructured documents automatically 📄⚡ Here’s how it works: 🔹 Configure a Git repository where data and outputs will be stored 🔹 Clone the repository into the agent sandbox 🔹 Install the LiteParse CLI, the LlamaParse SDK, and agent skills to use both 🔹 Prompt the agent with a task and watch it process documents autonomously 🤖 The result? An agent that can work directly with messy, real-world documents using LlamaParse and LiteParse within Google’s new agent runtime. Check out the GitHub repository: Get started with LlamaParse:

LlamaIndex 🦙

21,975 次观看 • 4 个月前

Use AI in every app on Mac, enriched by context from the app you're in. Generate replies in Gmail, summarise your Slack, or let GPT fill out the contents of a web form! Try it now for the best (and cheapest) way to use GPT-4!
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Use AI in every app on Mac, enriched by context from the app you're in. Generate replies in Gmail, summarise your Slack, or let GPT fill out the contents of a web form! Try it now for the best (and cheapest) way to use GPT-4!

Michael Jelly 🏴‍☠️

2,550,769 次观看 • 2 年前

Open-weight MiniMax M3 filled out a US customs form from a driver's license photo For this test we deployed MiniMax M3 Q4 using MLX-VLM on a Mac Studio M3 Ultra 512GB RAM. The model was tasked with reading a scanned document and an ID card photo, then completing a declaration form Output: 736 tokens · Input: 1,847 tokens · Time: ~31s The model analyzed both inputs, streamed its reasoning, and then called three tools: write_field for text fields, mark for Yes/No checkboxes, and sign for the signature and date. It extracted the required information, mapped it to the correct fields and completed the form without any manual input
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Open-weight MiniMax M3 filled out a US customs form from a driver's license photo For this test we deployed MiniMax M3 Q4 using MLX-VLM on a Mac Studio M3 Ultra 512GB RAM. The model was tasked with reading a scanned document and an ID card photo, then completing a declaration form Output: 736 tokens · Input: 1,847 tokens · Time: ~31s The model analyzed both inputs, streamed its reasoning, and then called three tools: write_field for text fields, mark for Yes/No checkboxes, and sign for the signature and date. It extracted the required information, mapped it to the correct fields and completed the form without any manual input

atomic.chat

109,369 次观看 • 3 个月前

Built a vibe-coded presentation app generator that turns natural language into polished slides ✨ This project by Jerry Liu combines the Claude Agent SDK with LlamaParse to create an AI-powered presentation tool that handles everything from content creation to PDF export: 🎯 Chat-based slide creation - just describe what you want and watch slides appear ✏️ Real-time editing through natural conversation - refine slides by chatting with the AI 📄 Smart document parsing with LlamaParse for incorporating reference materials 📊 Full export functionality to PowerPoint and PDF formats Perfect example of how LlamaParse makes document processing seamless while Claude's conversational abilities create an intuitive slide editing experience. Check out the full project:
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Built a vibe-coded presentation app generator that turns natural language into polished slides ✨ This project by Jerry Liu combines the Claude Agent SDK with LlamaParse to create an AI-powered presentation tool that handles everything from content creation to PDF export: 🎯 Chat-based slide creation - just describe what you want and watch slides appear ✏️ Real-time editing through natural conversation - refine slides by chatting with the AI 📄 Smart document parsing with LlamaParse for incorporating reference materials 📊 Full export functionality to PowerPoint and PDF formats Perfect example of how LlamaParse makes document processing seamless while Claude's conversational abilities create an intuitive slide editing experience. Check out the full project:

LlamaIndex 🦙

50,184 次观看 • 8 个月前

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch
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AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

377,673 次观看 • 6 个月前

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.
3:50

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.

Santiago

49,331 次观看 • 11 个月前

One of the hardest problems with using AI agents to automate meaningful document work (contracts, KYC, diligence, claims, and more) is not actually building the agent, but building the UI/UX audit trail so the human can understand decisions linking back to the source documents - in PDFs, Powerpoint, Word, etc. This requires really good layout detection and segmentation, that you need to surface as *metadata context* to the AI agent, beyond any document -> markdown conversion. We've built really-nice VLM-capabilities to identify and segment all the elements within your document: from tables to charts to form boxes. You can use this metadata to directly provide experiences where humans can analyze the source document even as decisions are being made. Come check out LlamaParse!
0:37

One of the hardest problems with using AI agents to automate meaningful document work (contracts, KYC, diligence, claims, and more) is not actually building the agent, but building the UI/UX audit trail so the human can understand decisions linking back to the source documents - in PDFs, Powerpoint, Word, etc. This requires really good layout detection and segmentation, that you need to surface as *metadata context* to the AI agent, beyond any document -> markdown conversion. We've built really-nice VLM-capabilities to identify and segment all the elements within your document: from tables to charts to form boxes. You can use this metadata to directly provide experiences where humans can analyze the source document even as decisions are being made. Come check out LlamaParse!

Jerry Liu

19,319 次观看 • 6 个月前

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.
1:32

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

46,142 次观看 • 1 年前

Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:
2:52

Introducing RAGs, a Streamlit app that allows you to create and customize your own RAG agent and then use it over your own data, all with natural language 🔥 Directly inspired by OpenAI GPTs, you can converse with an agent to help you do search/retrieval over any data you specify. The app contains three main pages: 🏠 Home Page : Have a “builder agent” build your RAG agent through natural language (you specify the data). ⚙️ RAG Config: Look at configured parameters 🤖 Use your RAG agent! Check out details below 👇 Blog: Repo:

LlamaIndex 🦙

475,732 次观看 • 2 年前

Ever wanted to quickly turn a PDF into clean text to paste into your favorite AI agent, without having use CLIs or open the browser? We built exactly that. Using Tauria Penny ps, with a Rust backend powered by LiteParse and a React frontend, we created a cross-platform desktop app that lets you: • Drag and drop PDFs • Convert them into clean Markdown • Preview page screenshots with extracted bounding boxes overlaid for easy inspection It's a simple way to understand your document's structure and verify the parsing results before handing them off to an LLM. Check out the demo below! 👇 GitHub: Get started with LiteParse:
0:31

Ever wanted to quickly turn a PDF into clean text to paste into your favorite AI agent, without having use CLIs or open the browser? We built exactly that. Using Tauria Penny ps, with a Rust backend powered by LiteParse and a React frontend, we created a cross-platform desktop app that lets you: • Drag and drop PDFs • Convert them into clean Markdown • Preview page screenshots with extracted bounding boxes overlaid for easy inspection It's a simple way to understand your document's structure and verify the parsing results before handing them off to an LLM. Check out the demo below! 👇 GitHub: Get started with LiteParse:

LlamaIndex 🦙

21,173 次观看 • 2 个月前