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LLMs/general agents still struggle to make sense of messy and complex Excel data. You can't easily dump all cells into the context window, and using the code interpreter is inefficient. LlamaSheets is one of my favorite releases from last year. We've embarked on an effort to build state-of-the-art algorithms...

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Jerry Liu

77,810 subscribers

30,162 次观看 • 7 个月前 •via X (Twitter)

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Claude Code over Excel++ 🤖📊 Claude already 'works' over Excel, but in a naive manner - it writes raw python/openpyxl to analyze an Excel sheet cell-by-cell and generally lacks a semantic understanding of the content. Basically the coding abstractions used are too low-level to have the coding agent accurately do more sophisticated analysis. Our new LlamaSheets API lets you automatically segment structure complex Excel sheets into well-formatted 2D tables. This both gives Claude Code immediate semantic awareness of the sheet, and allows it to run Pandas/SQL over well-structured dataframes. We've written a guide showing you how specifically to use LlamaSheets with coding agents! Guide: Sign up to LlamaCloud:
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Claude Code over Excel++ 🤖📊 Claude already 'works' over Excel, but in a naive manner - it writes raw python/openpyxl to analyze an Excel sheet cell-by-cell and generally lacks a semantic understanding of the content. Basically the coding abstractions used are too low-level to have the coding agent accurately do more sophisticated analysis. Our new LlamaSheets API lets you automatically segment structure complex Excel sheets into well-formatted 2D tables. This both gives Claude Code immediate semantic awareness of the sheet, and allows it to run Pandas/SQL over well-structured dataframes. We've written a guide showing you how specifically to use LlamaSheets with coding agents! Guide: Sign up to LlamaCloud:

Jerry Liu

75,897 次观看 • 9 个月前

An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!
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An underrated issue with document parsing for RAG / agent use cases is dealing with multi-page tables - sometimes a big table spills over into multiple pages. This breaks chunking algorithms that generally operate at the page-level or smaller, and causes LLMs to lose the full view of the data. With LlamaParse Continuous Mode (in beta), you can now parse a document with multi-page tables and join them into a single table! This means you can now: 💡 Do contiguous chunking for RAG use cases OR 💡 Parse the table for text-to-SQL Check out our blog post highlighting this feature. Huge shoutout to Pierre-Loic Doulcet and Sacha Bron : Signup here: It's in beta, let us know your feedback!

Jerry Liu

24,245 次观看 • 1 年前

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
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New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 次观看 • 1 年前

English Is The New SQL LLMs have made it easy for everyone to create on-the-fly dashboards and plots. You can connect to your database and then use a SOTA LLM like 4o, Opus, or Gemini to generate SQL and Python code to create on-the-fly dashboards and plots. ChatLLM makes it possible across all state-of-the-art LLMs -
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Bindu Reddy

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EVERYTHING you NEED to know about Jack Dorsey's AI agent "Slack killer" Buzz (set up, use-cases etc in 38 mins) What we get into: 1. What Buzz actually is and should founders switch from Slack? 2. How to swap the model under any agent and keep all your context? 3. How to talk to your agents live with audio huddles? 4. How to get agents to build and deploy real apps for you, like a full CRM from one ask? 5. How to set up the context loop that feeds your live app data back to your agents? 6. How to share compute so a few people split one machine running a local model? 7. Who it's actually for right now, and what's still rough? Full breakdown on the pod The Startup Ideas Podcast (SIP) 🧃. Thanks to Vinny for jumping on and clearly explaining jack and team's latest product. My TLDR take is Buzz is a glimpse into the future of work. Some of you will roll your eyes at that, and I get it, it's alpha software and it's slow in places. But the core idea, that your context is the foundation and agents build out from there, is right, and that's worth seeing early. Watch Curious what you think
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GREG ISENBERG

556,084 次观看 • 1 个月前

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LlamaSheets is our new way to handle complex, messy spreadsheets that come as many sheets disguised as one, multiple regions that provide different sets of information, and much more. Check out this example of a (generated, fake) company budget sheet. It actually has 4 sub-sheets, each containing multiple regions. LlamaSheets identifies each sub-region, creates summaries about what information they provide, and returns all of this informaiton as a parquet file! Try it out and let us know what you think while it's in public beta! Get started here:

LlamaIndex 🦙

14,676 次观看 • 8 个月前

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Andrew Ng

139,978 次观看 • 1 年前

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.
4:50

How can you solve complex tasks using a Large Language Model? Here is a 2-minute introduction to everything you need to know to 10x the quality of your results. Let's talk about three techniques, in order of complexity, starting with the easiest one: • In-Context Learning • Indexing + In-Context Learning • Fine-tuning In-Context Learning The team that trained GPT-3 found something they couldn't explain: You can condition a model using examples of how you want it to behave. I included an example prompt in the attached video. You can "teach" the model how you want it to interpret questions, select the correct answers, and format the results by giving a few examples. You can also give specific knowledge to the model that will be helpful when formulating answers. We call this approach "grounding the model." There's another example in the video. Indexing + In-Context Learning Unfortunately, there is a limit to how much data you can include in a prompt. We call this the "context size." One version of GPT-4 supports a context of approximately 6,000 words, while the other supports 25,000 words. Although this sounds like a lot, many applications need more than that. Imagine you wrote a book and want to build an application to answer any questions about your story. What happens if your book is longer than the context? That's where Indexing comes in. Using a model, you can turn every book passage into an embedding. These are vectors, numbers that "encode" the passage's text. You can then store these embeddings in a particular database that supports fast retrieval of these vectors. You can then turn any question into an embedding and search the database for the list of passages that are similar to that query. Instead of using the entire book to ask the model, you can now use the relevant passages as in-context information, effectively working around the context size limitation. Fine-tuning Fine-tuning can give you an extra boost to get reliable outputs from your LLM. It is, however, the most complex approach on the list. There are different approaches to fine-tuning a model with your data. A popular technique is to process your data with your LLM and use the outputs to train a new classifier that solves your specific task. Notice that here you aren't modifying the LLM. Instead, you are chaining it with your trained classifier. Another approach is to modify the parameters of the LLM using your data. Think of this as "rewiring" the model in a way that solves your particular task. The results and costs will vary depending on how many layers you want to fine-tune from the original model. Many companies think that fine-tuning is the solution to their problems. In my experience, many will benefit from exploring the other two approaches. I love explaining Machine Learning and Artificial Intelligence ideas. If you enjoy in-depth content like this, follow me Santiago so you don't miss what comes next.

Santiago

384,510 次观看 • 3 年前

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

55,747 次观看 • 1 个月前

🚨Governor DeSantis announces ROBUST Legislation regulating A.I. and banning data centers in Florida IN SPITE of Trump! “There's some people saying the federal government should just take over and not let the State do anything. I REJECT that! It is my responsibility to stand up for you! I think it's wrong if they come in to build a data center and then charge the normal customers higher for electricity. We're not going to let that happen. I think it's wrong if they are using your likeness and putting out false information under you. Because sometimes it's hard to even tell who it is. I think it's wrong if things are targeting children. Particularly for s*xual exploitation, and we've seen technology being done this. This is to kind of take it to a whole new level. So, we're going to defend our right to be able to protect your freedom. And the way I view it is, yes, my job is to protect your freedom FROM government and the state of Florida.”
3:31

🚨Governor DeSantis announces ROBUST Legislation regulating A.I. and banning data centers in Florida IN SPITE of Trump! “There's some people saying the federal government should just take over and not let the State do anything. I REJECT that! It is my responsibility to stand up for you! I think it's wrong if they come in to build a data center and then charge the normal customers higher for electricity. We're not going to let that happen. I think it's wrong if they are using your likeness and putting out false information under you. Because sometimes it's hard to even tell who it is. I think it's wrong if things are targeting children. Particularly for s*xual exploitation, and we've seen technology being done this. This is to kind of take it to a whole new level. So, we're going to defend our right to be able to protect your freedom. And the way I view it is, yes, my job is to protect your freedom FROM government and the state of Florida.”

Chris Nelson 🏝️🇺🇸

55,685 次观看 • 9 个月前

We're winning the AI browser wars, and we're not even trying. Our mission isn't to build a browser. We're an agent company, not a browser company. We've seen firsthand how teams struggle to get started with AI. The tools they tried are too complex and lack the context you need. We decided to build a browser since it's already the tool you work in. We all know how to use a browser. You can pick up Strawberry and start using AI agents from day one. By building a browser we are also positioning ourselves to build towards the internet of the future. An internet where trillions of agents will be communicating with each other. This is the biggest opportunity of our lifetime. But someone has to make sure it is safe, effective and accessible to teams across the world. That is our mission.
0:52

We're winning the AI browser wars, and we're not even trying. Our mission isn't to build a browser. We're an agent company, not a browser company. We've seen firsthand how teams struggle to get started with AI. The tools they tried are too complex and lack the context you need. We decided to build a browser since it's already the tool you work in. We all know how to use a browser. You can pick up Strawberry and start using AI agents from day one. By building a browser we are also positioning ourselves to build towards the internet of the future. An internet where trillions of agents will be communicating with each other. This is the biggest opportunity of our lifetime. But someone has to make sure it is safe, effective and accessible to teams across the world. That is our mission.

Charles Maddock

42,142 次观看 • 2 个月前

Hi everyone!! Our first public (beta) release of cardano-init is out! 🥳🎉 This tool creates new projects ready to go; it encodes how Cardano tools fit together, analyzes and helps you set up the environment, and more! We're making it easy to build new Cardano DApps for newcomers and LLM agents alike! 🤖 This is an early beta release; we haven’t integrated all the tools, we can still make a lot of changes, and we have many improvements in the pipeline! Be sure to check out our plan/roadmap (in the repo) and share your thoughts! 💪😄 Check it out here:
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Hi everyone!! Our first public (beta) release of cardano-init is out! 🥳🎉 This tool creates new projects ready to go; it encodes how Cardano tools fit together, analyzes and helps you set up the environment, and more! We're making it easy to build new Cardano DApps for newcomers and LLM agents alike! 🤖 This is an early beta release; we haven’t integrated all the tools, we can still make a lot of changes, and we have many improvements in the pipeline! Be sure to check out our plan/roadmap (in the repo) and share your thoughts! 💪😄 Check it out here:

🪏 Robertino

47,371 次观看 • 9 天前

Ossoff: The first thing that I want you to hear from me is that I'm your servant. I work for you. You are the boss. And whenever and however I can be of service to you, I want you to reach out to me and let me know. Because my fundamental purpose in this office is to support you and help you and solve problems for you so that you and your families and this community can flourish.
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Ossoff: The first thing that I want you to hear from me is that I'm your servant. I work for you. You are the boss. And whenever and however I can be of service to you, I want you to reach out to me and let me know. Because my fundamental purpose in this office is to support you and help you and solve problems for you so that you and your families and this community can flourish.

Acyn

80,180 次观看 • 12 天前

Introducing the AI Analyst 3.0✨ Since we launched it last year, the Analyst answered more than 300k of your questions and was used by more than 100k people. But many of you asked us for more capabilities: to help you visualize your analysis, enrich your datasets with new columns, format data and more. We packed all of that feedback into today's launch: a new Analyst rebuilt from the ground up to be a smarter, faster and a more versatile data analyst for your spreadsheets. Available today. See it in action 👇
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Introducing the AI Analyst 3.0✨ Since we launched it last year, the Analyst answered more than 300k of your questions and was used by more than 100k people. But many of you asked us for more capabilities: to help you visualize your analysis, enrich your datasets with new columns, format data and more. We packed all of that feedback into today's launch: a new Analyst rebuilt from the ground up to be a smarter, faster and a more versatile data analyst for your spreadsheets. Available today. See it in action 👇

Rows

12,386 次观看 • 1 年前

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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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!

Andrew Ng

201,127 次观看 • 1 年前