Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Today we're excited to launch `create-llama` - a single CLI command that allows you to scaffold a full stack LLM app over your data, powered by LlamaIndex 🦙 🔥 It’s the equivalent of `create-react-app` for AI engineers. Choose from 3 different backends: ✅ FastAPI (w/ LlamaIndex 🦙 Python) ✅... ExpressJS (w/ LlamaIndex 🦙 TS) ✅ Next.js (w/ LlamaIndex 🦙 TS) The frontend is a Next.js template using initial chat components from the Vercel AI SDK - with full streaming support, + shadcn UI or vanilla HTML/CSS for styling. This is a full-stack starting point, from which you can customize any component to build the LLM app of your liking, whether it’s simple extensions (change data source) or complete revamps (upgraded chat UX)! Next steps 🔮: - We’re going to be coming out with more examples in the next few weeks showing what you can build. - We’re going to be upgrading `create-llama` with a variety of different templates Check out the video (also attached) to see how it works 🎥: Repo directory: Full blog post detailing the usage here:show more

Jerry Liu

83,500 subscribers

259,026 views • 2 years ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

11 Comments

Braintrust2 years ago

@llama_index @FastAPI Need an evaluations script for your AI app? We just opened a PR adding in Braintrust for create-llama to test and evaluate the LLM calls in the templates.

Jerry Liu2 years ago

@llama_index @FastAPI thanks for the contribution!

RTTS2 years ago

Testing Salesforce presents unique challenges due to its complexity, scalability and customizability. RTTS can plan, design & automate a successful testing process for you.

Franck SN2 years ago

@llama_index @FastAPI LLM packages are really going full-stack. Next step PaaS

Parzival - ∞/892 years ago

This is awesome! Love seeing this in the JS ecosystem. I would love to see something sinilar but with the UI as optional. The ability to quickly spin up a document ingestion and querying API quickly to start customizing would be huge for us, and likely many others who want to add document ingestion to their own apps.

yikes (:D/acc)2 years ago

@llama_index @FastAPI would be keen to organize an event to hack on this! :3 ig DM me or something (ill probably do it anyway but having Official Llamaindex People and/or a prize pool would be dope :3)

Jake Harrison2 years ago

@llama_index @FastAPI Awesome!

Zugbob2 years ago

@llama_index @FastAPI Awesome! Often TS comes 2nd to Python with Ai.

top_nexus2 years ago

@llama_index @FastAPI U da best! Now we just need to bring typescript to be on par with python and u will win the framework wars... Literally

tim2 years ago

@llama_index @FastAPI does this support the new Assistant API or is it still Chat Completion under the hood?

Andreas Nigg 💻2 years ago

@llama_index @FastAPI AI coming for you all 5 million JS framework create-app clis 😬

Related Videos

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

Andrew Ng

218,703 views • 2 years ago

We stealth launched on Product Hunt a few weeks back and hit top 5 🔥 Now, we’re releasing it to the public, fully open-source! Use as a full template to build a production-quality RAG app 🛠️ It’s not any chat app, it contains a set of key features that took weeks/months of tuning to get right. And we did it so you don’t have to! ✅ Intermediate steps: get full transparency in how the LLM arrives at its answer. ✅ Granular highlights in sources: see *exactly* where the source context came from for a given response ✅ Full streaming support: everything from intermediate steps/responses to the final response is streamed. ✅ Ask a variety of complex questions: ask over a single doc or across multiple docs 📄 Tech Stack 📚: - 🤖 OpenAI (gpt-3.5-turbo) - 🏃‍♂️FastAPI + PostgreSQL - ▲ Next.js + Tailwind CSS - 🦙 and of course, LlamaIndex 🦙 FULL Repo here: use it as reference code or clone/fork it and use as you wish: And link to the secinsights app if you want to play around: HUGE shoutout to Sourabh Desai for driving this project 🙌 - it's been awesome to see it come to fruition.

We stealth launched on Product Hunt a few weeks back and hit top 5 🔥 Now, we’re releasing it to the public, fully open-source! Use as a full template to build a production-quality RAG app 🛠️ It’s not any chat app, it contains a set of key features that took weeks/months of tuning to get right. And we did it so you don’t have to! ✅ Intermediate steps: get full transparency in how the LLM arrives at its answer. ✅ Granular highlights in sources: see exactly where the source context came from for a given response ✅ Full streaming support: everything from intermediate steps/responses to the final response is streamed. ✅ Ask a variety of complex questions: ask over a single doc or across multiple docs 📄 Tech Stack 📚: - 🤖 OpenAI (gpt-3.5-turbo) - 🏃‍♂️FastAPI + PostgreSQL - ▲ Next.js + Tailwind CSS - 🦙 and of course, LlamaIndex 🦙 FULL Repo here: use it as reference code or clone/fork it and use as you wish: And link to the secinsights app if you want to play around: HUGE shoutout to Sourabh Desai for driving this project 🙌 - it's been awesome to see it come to fruition.

Jerry Liu

171,843 views • 3 years ago

We’re excited to feature the AI Chatbot Starter 🤖🧰 - a web server powered by AstraDB (@DataStax) and LlamaIndex 🦙 that you can easily spin up to chat over any web documentation 🌐📑 Easily use this as a standalone service or integrate it with your full-stack application. Set it up by simply setting a few credentials and ingesting your documents. A huge shoutout to the @Datastax team for creating this: check it out below 👇 Repo:

We’re excited to feature the AI Chatbot Starter 🤖🧰 - a web server powered by AstraDB (@DataStax) and LlamaIndex 🦙 that you can easily spin up to chat over any web documentation 🌐📑 Easily use this as a standalone service or integrate it with your full-stack application. Set it up by simply setting a few credentials and ingesting your documents. A huge shoutout to the @Datastax team for creating this: check it out below 👇 Repo:

LlamaIndex 🦙

23,939 views • 2 years ago

Introducing LlamaCloud 🦙🌤️ Today we’re thrilled to introduce LlamaCloud, a managed service designed to bring production-grade data for your LLM and RAG app. Spend less time data wrangling and more time on application logic. Launching with the following components: 1️⃣ LlamaParse 📑: a proprietary parser designed to be really really good at complex documents with embedded tables. Build advanced RAG over semi-structured PDFs, and ask questions that simply aren’t possible with the naive stack. Available publicly day 1 🔥 2️⃣ Managed Ingestion/Retrieval API ⚙️: An API letting you easily ingest/retrieve data from data sources. Opening up in private beta to select enterprises. We’re excited to be joined by launch users, partners, and collaborators: Mendable @DataStax MongoDB Qdrant NVIDIA + some awesome hackathon projects at the LlamaIndex 🦙 hackathon Check out our FULL blog post on LlamaCloud and LlamaParse: LlamaParse Client Repo: Signup for a LlamaCloud account to use LlamaParse: Interested in the broader LlamaCloud offering? Come talk to us: Also we have a slick new website 🌐:

Introducing LlamaCloud 🦙🌤️ Today we’re thrilled to introduce LlamaCloud, a managed service designed to bring production-grade data for your LLM and RAG app. Spend less time data wrangling and more time on application logic. Launching with the following components: 1️⃣ LlamaParse 📑: a proprietary parser designed to be really really good at complex documents with embedded tables. Build advanced RAG over semi-structured PDFs, and ask questions that simply aren’t possible with the naive stack. Available publicly day 1 🔥 2️⃣ Managed Ingestion/Retrieval API ⚙️: An API letting you easily ingest/retrieve data from data sources. Opening up in private beta to select enterprises. We’re excited to be joined by launch users, partners, and collaborators: Mendable @DataStax MongoDB Qdrant NVIDIA + some awesome hackathon projects at the LlamaIndex 🦙 hackathon Check out our FULL blog post on LlamaCloud and LlamaParse: LlamaParse Client Repo: Signup for a LlamaCloud account to use LlamaParse: Interested in the broader LlamaCloud offering? Come talk to us: Also we have a slick new website 🌐:

LlamaIndex 🦙

141,282 views • 2 years ago

I’ve created a full-stack financial analysis bot that can query both text + embedded tables across multiple SEC 10Ks 📊🤖 It’s made possible with `create-llama` scaffolding + LlamaIndex 🦙 advanced RAG, and I’m sharing the full template below for you to clone! It’s more sophisticated than a basic RAG setup: 🦾 Unstructured to parse embedded tables into a node graph 🦾 Recursive retriever to retrieve/query embedded tables + text 🦾 An agent to do chain of thought + document comparisons 🦾 Custom callback to stream intermediate function calls to the UI Our goal is to make building full-stack advanced RAG as easy as possible. Created a repo to host this template + future templates here: Want to submit a project? We’d love contributions! 🙌 One caveat: - Right now the index is lazily built/cached during the first query, we’re working to decouple the ingestion process - Check out `create-llama` if you haven’t already:

I’ve created a full-stack financial analysis bot that can query both text + embedded tables across multiple SEC 10Ks 📊🤖 It’s made possible with `create-llama` scaffolding + LlamaIndex 🦙 advanced RAG, and I’m sharing the full template below for you to clone! It’s more sophisticated than a basic RAG setup: 🦾 Unstructured to parse embedded tables into a node graph 🦾 Recursive retriever to retrieve/query embedded tables + text 🦾 An agent to do chain of thought + document comparisons 🦾 Custom callback to stream intermediate function calls to the UI Our goal is to make building full-stack advanced RAG as easy as possible. Created a repo to host this template + future templates here: Want to submit a project? We’d love contributions! 🙌 One caveat: - Right now the index is lazily built/cached during the first query, we’re working to decouple the ingestion process - Check out `create-llama` if you haven’t already:

Jerry Liu

131,442 views • 2 years ago

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:

Jerry Liu

55,620 views • 9 months ago

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:

We’re excited to officially launch LlamaParse, the first genAI-native document parsing solution. Not only is it better at parsing out images/tables/charts 📊📈 than virtually every other parser, it is now steerable through natural language instructions - output the document in whatever format you desire! It is also the only parsing solution that seamlessly allows you to build accurate RAG over complex documents, free of hallucinations 🔥 We launched it in private preview a few weeks ago and hit 2k users, 1M total PDF pages parsed. And now it’s better than ever. LlamaParse contains the following killer features: ✅ SOTA table/chart extraction ✅ Seamless integration with LlamaIndex 🦙 advanced RAG/agents ✅✨ Natural language Parsing Instructions ✅✨JSON mode and image extraction ✅✨Support for ~10 document types (.pdf, .pptx, .docx, .xml) and more Our pricing is simple: 1k free per day, and additional pages at 0.3c a page, or $3 for 1k pages. If you want advanced document RAG and/or private deployments, come get in touch with us to chat about LlamaCloud. Check out our full blog post here: LlamaParse client repo: Signup at 🦙☁️: Come talk to us:

LlamaIndex 🦙

143,189 views • 2 years ago

Introducing NotebookLlama - an open-source version of NotebookLM! 📓🦙 NotebookLlama is a full implementation of NotebookLM that includes all the capabilities that makes it so great for researchers+business users: ✅ Create a knowledge repository of documents. Has likely higher accuracy than NotebookLM since it’s using LlamaCloud under the hood for high-quality parsing/extraction over complex docs. ✅ Generate summaries and knowledge graph mind-maps 🤯 ✅ Generate podcasts thanks to ElevenLabs 🗣️ ✅ Agentic chat with docs and view metrics with OpenTelemetry This all lives within an open-source repo so you can clone/modify at will to swap in your own components! Huge shoutout to Clelia Bertelli (🦙/acc) for leading this. Repo: LlamaCloud helps power the parsing/ingestion. You can always use your own stuff too, but in the meantime you can check out LlamaCloud here:

Introducing NotebookLlama - an open-source version of NotebookLM! 📓🦙 NotebookLlama is a full implementation of NotebookLM that includes all the capabilities that makes it so great for researchers+business users: ✅ Create a knowledge repository of documents. Has likely higher accuracy than NotebookLM since it’s using LlamaCloud under the hood for high-quality parsing/extraction over complex docs. ✅ Generate summaries and knowledge graph mind-maps 🤯 ✅ Generate podcasts thanks to ElevenLabs 🗣️ ✅ Agentic chat with docs and view metrics with OpenTelemetry This all lives within an open-source repo so you can clone/modify at will to swap in your own components! Huge shoutout to Clelia Bertelli (🦙/acc) for leading this. Repo: LlamaCloud helps power the parsing/ingestion. You can always use your own stuff too, but in the meantime you can check out LlamaCloud here:

Jerry Liu

137,793 views • 1 year ago

Check out mistral.rs, our #Rust-based open source inference engine allowing for fast #LLM serving for a variety of architectures including X-LoRA mixture-of-expert (MoE) models, Llama-3, Mistral/Mixtral, Gemma & many others. Built on the Hugging Face #Candle framework for #Rust w/ custom CUDA kernels in the backend (as well as support for Metal, Apple Accelerate, and Intel MKL for CPU use), you can easily create a REST API OpenAI compatible server or run via Python bindings. Key features include: ✅Prefix caching, continuous batching ✅Flash Attention V2 ✅Device offloading ✅GGUF or Hugging Face models ✅2, 3, 4, 5, 6 and 8 bit quantization ✅X-LoRA MoE non-granular scalings for fast inference ✅Grammar support ✅Continuous batching ✅LoRA support with weight merging ✅LlamaIndex 🦙 integration ...and much more. Incorporation into our GraphReasoning multi-agent modeling framework & LlamaIndex 🦙 allows you to combine in-context learning with adversarial agentic strategies, to dive deep into complex scientific analyses, such as to predict material behaviors, generate hypotheses, analyze papers and data, develop new research concepts, and much more. Check out mistral.rs: Join our Discord here: Rust Trending Rust Language

Check out mistral.rs, our #Rust-based open source inference engine allowing for fast #LLM serving for a variety of architectures including X-LoRA mixture-of-expert (MoE) models, Llama-3, Mistral/Mixtral, Gemma & many others. Built on the Hugging Face #Candle framework for #Rust w/ custom CUDA kernels in the backend (as well as support for Metal, Apple Accelerate, and Intel MKL for CPU use), you can easily create a REST API OpenAI compatible server or run via Python bindings. Key features include: ✅Prefix caching, continuous batching ✅Flash Attention V2 ✅Device offloading ✅GGUF or Hugging Face models ✅2, 3, 4, 5, 6 and 8 bit quantization ✅X-LoRA MoE non-granular scalings for fast inference ✅Grammar support ✅Continuous batching ✅LoRA support with weight merging ✅LlamaIndex 🦙 integration ...and much more. Incorporation into our GraphReasoning multi-agent modeling framework & LlamaIndex 🦙 allows you to combine in-context learning with adversarial agentic strategies, to dive deep into complex scientific analyses, such as to predict material behaviors, generate hypotheses, analyze papers and data, develop new research concepts, and much more. Check out mistral.rs: Join our Discord here: Rust Trending Rust Language

Markus J. Buehler

73,581 views • 2 years ago

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Introducing FlowMaker 🌊🤖 A fully open-source, low-code way of building custom agent workflows. Build agents via a drag and drop interface, run it directly in the app, and also directly export it into a deeply custom workflow backed by LlamaIndex 🦙.TS. It’s a fantastic visual tool to help you get started with agents but also gives you full customizability to build the deepest, most advanced workflows, since everything is backed by code. There’s a lot of great low-code agent tools already, but this is a fully-open source template that you can use or clone/customize for your own needs. It was a fun project by Laurie Voss and is intended to give users an easy-on ramp onto our powerful agent orchestration tooling. Also integrates with LlamaCloud in case you want to integrate your own knowledge base into your agent. FlowMaker app: FlowMaker Repo: LlamaCloud:

Jerry Liu

20,434 views • 1 year ago

🦙 Since you loved NotebookLlaMa, our open-source, LlamaCloud-backed NotebookLM alternative, we decided to give you more features! In the latest release you can: 📊 Extract and download images and tables from your files, and interactively visualize all tabular data ✅ Chat with your document and verify immediately everything that AI is saying about it, using the new claim verification and citation finding functionality 👇 Watch the video below for a full demo of the new functionalities 👩‍💻🧑‍💻 There is also a bonus: thanks to the community effort and contribution, you can now customize the embedding model you want to use to build the LlamaCloud index, through a textualize.io CLI! ⭐ Star NotebookLlaMa on GitHub: 🦙☁️ Sign-up for LlamaCloud and test our awesome products: 📚 Learn more about LlamaCloud:

🦙 Since you loved NotebookLlaMa, our open-source, LlamaCloud-backed NotebookLM alternative, we decided to give you more features! In the latest release you can: 📊 Extract and download images and tables from your files, and interactively visualize all tabular data ✅ Chat with your document and verify immediately everything that AI is saying about it, using the new claim verification and citation finding functionality 👇 Watch the video below for a full demo of the new functionalities 👩‍💻🧑‍💻 There is also a bonus: thanks to the community effort and contribution, you can now customize the embedding model you want to use to build the LlamaCloud index, through a textualize.io CLI! ⭐ Star NotebookLlaMa on GitHub: 🦙☁️ Sign-up for LlamaCloud and test our awesome products: 📚 Learn more about LlamaCloud:

LlamaIndex 🦙

11,466 views • 1 year ago

Most AI app builders can generate a prototype. The real question is: Can they generate something you can actually launch? I recently explored MeDo, an AI-powered full-stack app generation platform that goes beyond just creating a frontend. With a single prompt, it can generate the frontend UI, backend logic, database structure, and plugin integrations. All in one workflow, without requiring coding or deployment experience. What stood out to me is the focus on building real products rather than simple demos. Whether you're creating SaaS tools, internal dashboards, e-commerce applications, games, or business automation tools. the goal is to move from idea → working product as quickly as possible. A few notable features: ✅ Full-stack generation from a single prompt ✅ Plugin ecosystem with integrations like Stripe and AI tools ✅ Auto bug-fixing capabilities ✅ Website cloning from screenshots ✅ Built-in UI enhancement options ✅ Marketplace of ready-made apps you can customize The AI app builder space is becoming increasingly competitive, and platforms are starting to differentiate themselves based on how close they get to a production-ready result rather than just generating code. For builders, founders, and creators exploring no-code AI development, it's an interesting category worth watching. Try it today and launch your next project with just a prompt:

Most AI app builders can generate a prototype. The real question is: Can they generate something you can actually launch? I recently explored MeDo, an AI-powered full-stack app generation platform that goes beyond just creating a frontend. With a single prompt, it can generate the frontend UI, backend logic, database structure, and plugin integrations. All in one workflow, without requiring coding or deployment experience. What stood out to me is the focus on building real products rather than simple demos. Whether you're creating SaaS tools, internal dashboards, e-commerce applications, games, or business automation tools. the goal is to move from idea → working product as quickly as possible. A few notable features: ✅ Full-stack generation from a single prompt ✅ Plugin ecosystem with integrations like Stripe and AI tools ✅ Auto bug-fixing capabilities ✅ Website cloning from screenshots ✅ Built-in UI enhancement options ✅ Marketplace of ready-made apps you can customize The AI app builder space is becoming increasingly competitive, and platforms are starting to differentiate themselves based on how close they get to a production-ready result rather than just generating code. For builders, founders, and creators exploring no-code AI development, it's an interesting category worth watching. Try it today and launch your next project with just a prompt:

Markandey Sharma

49,777 views • 3 months ago

we released our react package build an ai chat w/ interactive components in a few lines of code. add it to your app with: npx tambo full-send we support: - streaming for both messages and components - message history and message threads - state manage for generated components - suggested actions based on available components - component library that uses the react package the react package is open-source. server package will be open-sourced later this year.

we released our react package build an ai chat w/ interactive components in a few lines of code. add it to your app with: npx tambo full-send we support: - streaming for both messages and components - message history and message threads - state manage for generated components - suggested actions based on available components - component library that uses the react package the react package is open-source. server package will be open-sourced later this year.

Michael Magán

65,473 views • 1 year ago

Forget about HTML, JavaScript, and CSS. You can build AI and data applications using Python alone! Python is the best programming language out there. You can now use it across the stack. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds:

Forget about HTML, JavaScript, and CSS. You can build AI and data applications using Python alone! Python is the best programming language out there. You can now use it across the stack. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds:

Santiago

233,921 views • 2 years ago

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

I’m excited to kick off the first of our short courses focused on agents, starting with Building Agentic RAG with LlamaIndex, taught by Jerry Liu, CEO of LlamaIndex 🦙. This covers an important shift in RAG (retrieval augmented generation), in which rather than having the developer write explicit routines to retrieve information to feed into the LLM context, we instead build a RAG agent that that has access to tools for retrieving information. This lets the agent decide what information to fetch, and enables it to answer more complex questions using multi-step reasoning. In detail, you'll learn about: - Routing: Where your agent will use decision-making to route requests to multiple tools. - Tool Use: Where you'll create an interface for agents to select what tool (function call) to use as well as generate the right arguments. - Multi-step reasoning with tool use: Where you'll use an LLM to carry out multiple steps of reasoning, while retaining memory throughout the process. You’ll also learn how to step through what your agent is doing to debug and improve it iteratively. It’s an exciting time to build agents. Sign up and get started here!

Andrew Ng

297,131 views • 2 years ago

I'm building a localhost AI app builder, inspired by tools like Lovable / Bolt / v0. Works with your own coding agents: ✅ Claude Code CLI ✅ Codex CLI ✅ GitHub Copilot (Chat extension + CLI) ✅ Cursor CLI So no new subscription is required. It also works with your app framework: ✅ Next.js ✅ React (Vite) ✅ Vue ✅ Svelte ✅ Angular ✅ TanStack Start ✅ Other frameworks (.NET, Ruby, etc.) You can create & open apps from a GUI, chat with your coding agent directly in the browser, view diffs, and more. It's live today as a VS Code extension called 'Wuunu'. Let me know what you think. :) Wuunu is completely independent, it's not affiliated with or endorsed by Lovable, Bolt, or v0.

I'm building a localhost AI app builder, inspired by tools like Lovable / Bolt / v0. Works with your own coding agents: ✅ Claude Code CLI ✅ Codex CLI ✅ GitHub Copilot (Chat extension + CLI) ✅ Cursor CLI So no new subscription is required. It also works with your app framework: ✅ Next.js ✅ React (Vite) ✅ Vue ✅ Svelte ✅ Angular ✅ TanStack Start ✅ Other frameworks (.NET, Ruby, etc.) You can create & open apps from a GUI, chat with your coding agent directly in the browser, view diffs, and more. It's live today as a VS Code extension called 'Wuunu'. Let me know what you think. :) Wuunu is completely independent, it's not affiliated with or endorsed by Lovable, Bolt, or v0.

Wesley

37,104 views • 9 months ago

Today, we are excited to launch a new world of AI apps and agents. We’re welcoming you to AITOPIA. We’re announcing 3 big launches today: 1. A new AI App and Agent Store: With a single subscription, you can access all the best AI models, apps, and agents in one place 2. SuperAgents: Agents for creativity that can work hours: With SuperAgents, you can create a few minutes of Instagram Reels or 15 minutes of an audiobook or 40 minutes of an educational video 3. A new AI economy model: AIconomy You’ll earn from what you create with AI, or what you remix from others’ creations and you’ll keep earning when others remix what you created.

Today, we are excited to launch a new world of AI apps and agents. We’re welcoming you to AITOPIA. We’re announcing 3 big launches today: 1. A new AI App and Agent Store: With a single subscription, you can access all the best AI models, apps, and agents in one place 2. SuperAgents: Agents for creativity that can work hours: With SuperAgents, you can create a few minutes of Instagram Reels or 15 minutes of an audiobook or 40 minutes of an educational video 3. A new AI economy model: AIconomy You’ll earn from what you create with AI, or what you remix from others’ creations and you’ll keep earning when others remix what you created.

AITOPIA

197,262 views • 5 months ago

Introducing... the NoCodeFusion MVP builder! 🥳 Type in your app idea, get a full-stack Bubble app in ~10 minutes with authentication, database, UI, and basic create/update/delete workflows. The perfectionist in me is dying, but f**k it - we're launching. See a demo below. It's not perfect, but an excellent way to kickstart a project. It uses my component library. You can easily change the main font and primary colors from the Bubble style variables. You can then download the Chrome extension which lets you continue to build on your app in the Bubble editor with a copilot chat interface OR hire a developer through us to help you finish the project. This project was built 100% by myself entirely on Bubble (and of course using NoCodeFusion) and is very much in beta. If there's a bug or something, go easy on me. Send me a message and I'll take a look. AI + Visual editor is now an incredible option to create full stack web apps. It allows you the power of creating something in minutes, then having an intuitive interface to make edits and understand what's actually going on in the logic. Let me know what you think ✌️

Introducing... the NoCodeFusion MVP builder! 🥳 Type in your app idea, get a full-stack Bubble app in ~10 minutes with authentication, database, UI, and basic create/update/delete workflows. The perfectionist in me is dying, but f**k it - we're launching. See a demo below. It's not perfect, but an excellent way to kickstart a project. It uses my component library. You can easily change the main font and primary colors from the Bubble style variables. You can then download the Chrome extension which lets you continue to build on your app in the Bubble editor with a copilot chat interface OR hire a developer through us to help you finish the project. This project was built 100% by myself entirely on Bubble (and of course using NoCodeFusion) and is very much in beta. If there's a bug or something, go easy on me. Send me a message and I'll take a look. AI + Visual editor is now an incredible option to create full stack web apps. It allows you the power of creating something in minutes, then having an intuitive interface to make edits and understand what's actually going on in the logic. Let me know what you think ✌️

Peter Kwitny

20,070 views • 1 year ago