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Take any Python code and build a dynamic GUI on top of it by dragging and dropping components. Python is already the most popular language in the world. This makes it even better! The attached video is only 45 seconds. It will show you how it works. Here is... show more

Santiago

451,998 subscribers

155,179 次观看 • 1 年前 •via X (Twitter)

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10 条评论

Roman B. 🇺🇦 的头像

Roman B. 🇺🇦1 年前

I bookmark way to many of your post 😁

Santiago 的头像

Santiago1 年前

That's good. It means I'm doing a good job of bringing you things that you think are useful 😉

DatJot 的头像

DatJot1 年前

When you have Tableau + Power BI at the fraction of the cost, IMHO, it is not worth the price tag. Don't get me wrong, it looks nice, but I am trying to figure out why someone would switch from Tableau to Taipy with that type of price tag. Does the Community Edition have the designer? Does not appear to have the designer. Maybe there is something I am missing but I don't see a WOW factor compared to software like Tableau that would make me switch products.

whynesspower ⟠ 的头像

whynesspower ⟠1 年前

From the looks of it Rip streamlit and gradio

JoJo Ventures 的头像

JoJo Ventures1 年前

Nice share, this is really good. It would be amazing if the drag-and-drop GUI feature could be applied to @streamlit and @Gradio too, so data scientists could focus on the ML model and dataset instead.

Tomer Nosrati 的头像

Tomer Nosrati1 年前

Have you seen @anvil_works ? It appears to be a more robust implementation of a GUI designer for Python.

Haider. 的头像

Haider.1 年前

you think this kind of tool could be used to create more user-friendly interfaces for complex Python libraries or frameworks?

Santiago 的头像

Santiago1 年前

Yes, that's exactly the goal. You have some Python code that you want to make accessible through an interface. Instead of having to learn HTML/CSS, etc, you can use Taipy Designer to build an interface for it.

MasterChief 的头像

MasterChief1 年前

Machine learning apps

Hesam 的头像

Hesam1 年前

This is a killer data science tool! Already there are amazing libraries to add visuals to the data but this is fascinating 🔥

相关视频

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

233,866 次观看 • 2 年前

Python is the best language in the world. Today, you can build AI and data applications using Python alone! No HTML or JavaScript necessary. 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:

Python is the best language in the world. Today, you can build AI and data applications using Python alone! No HTML or JavaScript necessary. 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

358,933 次观看 • 2 年前

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Santiago

125,928 次观看 • 8 个月前

How to build an AI agent from scratch. This is the question I get the most, so I recorded this video to show you from the very beginning how you can get started. You can do this in Python, TypeScript, JavaScript, or Ruby.

How to build an AI agent from scratch. This is the question I get the most, so I recorded this video to show you from the very beginning how you can get started. You can do this in Python, TypeScript, JavaScript, or Ruby.

Santiago

71,768 次观看 • 1 年前

You can build your 3D models directly from a prompt into Claude code. All thanks to this beautiful python project, build123 a parametric modeling framework for 2D and 3D CAD. It is built on the Open Cascade geometric kernel and it is suitable for 3D printing.

You can build your 3D models directly from a prompt into Claude code. All thanks to this beautiful python project, build123 a parametric modeling framework for 2D and 3D CAD. It is built on the Open Cascade geometric kernel and it is suitable for 3D printing.

Marco Franzon

20,743 次观看 • 2 个月前

Our AI engineer can now build simple agents using the English language! Tell it what to do, and it will try to generate, execute, and deploy code. AI has already replaced SQL. The next step is to replace Python. It's officially in its 3rd month of internship!!

Our AI engineer can now build simple agents using the English language! Tell it what to do, and it will try to generate, execute, and deploy code. AI has already replaced SQL. The next step is to replace Python. It's officially in its 3rd month of internship!!

Bindu Reddy

46,773 次观看 • 1 年前

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Santiago

89,230 次观看 • 1 年前

108 workflow templates you can use to build AI applications without writing any code. You can use these templates with n8n. I recorded the attached video to show you how it works. n8n is the workhorse behind an open-source, self-hosted AI starter kit you can install on your computer. They are sponsoring this post. Here is the link to the starter kit repository: And here is the spreadsheet with the 108 templates: Whatever idea you have, search for something similar in the list of templates, and you'll save a ton of time. Lately, I've talked to many non-coders who want to start using AI more seriously to build things. n8n is perfect for that.

108 workflow templates you can use to build AI applications without writing any code. You can use these templates with n8n. I recorded the attached video to show you how it works. n8n is the workhorse behind an open-source, self-hosted AI starter kit you can install on your computer. They are sponsoring this post. Here is the link to the starter kit repository: And here is the spreadsheet with the 108 templates: Whatever idea you have, search for something similar in the list of templates, and you'll save a ton of time. Lately, I've talked to many non-coders who want to start using AI more seriously to build things. n8n is perfect for that.

Santiago

78,133 次观看 • 1 年前

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,803 次观看 • 2 年前

This is incredibly useful for those using Claude Code! I have two computers, and sharing your Claude Code sessions is almost impossible without this. I often start working on something on my desktop, and then want to continue working on the same session (same context, conversation, etc.) on my laptop. The folks at Depot built an integration that solves this problem: You can now start a session on one computer and resume it anywhere else in the world! You can even share it with other team members so they can also contribute to it. Here is an article showing you how the Depot team uses this feature with Claude Code to automate their GitHub Actions Runner updates: Thanks to the team for the effort in building this integration and collaborating with me on this post.

This is incredibly useful for those using Claude Code! I have two computers, and sharing your Claude Code sessions is almost impossible without this. I often start working on something on my desktop, and then want to continue working on the same session (same context, conversation, etc.) on my laptop. The folks at Depot built an integration that solves this problem: You can now start a session on one computer and resume it anywhere else in the world! You can even share it with other team members so they can also contribute to it. Here is an article showing you how the Depot team uses this feature with Claude Code to automate their GitHub Actions Runner updates: Thanks to the team for the effort in building this integration and collaborating with me on this post.

Santiago

43,931 次观看 • 7 个月前

This is how you can start automating your entire life: (No need to install or configure anything. You don't have to mess with OpenClaw.) 1. Go to SureThing 2. Connect a few apps (1000+ available) 3. Start building automations by just asking I recorded a few examples in the video. Surething is an autonomous agent with a chat interface. There's absolutely nothing to configure. It literally takes 5 seconds to be up and running. You start connecting with different apps. For example, your email, Slack, your calendar, Discord, Zoom, whatever. It has memory. It updates it as you interact with it and as it consumes more information about you. You can also build and schedule automations by just asking. No code necessary. The system does it all automatically. This is pretty incredible! Thanks to the team for partnering with me on this post.

This is how you can start automating your entire life: (No need to install or configure anything. You don't have to mess with OpenClaw.) 1. Go to SureThing 2. Connect a few apps (1000+ available) 3. Start building automations by just asking I recorded a few examples in the video. Surething is an autonomous agent with a chat interface. There's absolutely nothing to configure. It literally takes 5 seconds to be up and running. You start connecting with different apps. For example, your email, Slack, your calendar, Discord, Zoom, whatever. It has memory. It updates it as you interact with it and as it consumes more information about you. You can also build and schedule automations by just asking. No code necessary. The system does it all automatically. This is pretty incredible! Thanks to the team for partnering with me on this post.

Santiago

107,878 次观看 • 3 个月前

Are interactive essays the future of education? I asked Claude Code to build an interactive essay for PCA + SVD that works client side. Notice how python code executes and graphs are generated WITHOUT any backend! Play with it yourself 👇

Are interactive essays the future of education? I asked Claude Code to build an interactive essay for PCA + SVD that works client side. Notice how python code executes and graphs are generated WITHOUT any backend! Play with it yourself 👇

Paras Chopra

18,036 次观看 • 5 个月前

Markdown was doomed from the start. It's just a format with low information density. HTML is better for humans, and agents can now consume and produce it without issues. But nobody wants to type HTML, so here is an alternative: This is an open-source tool for generating dashboards from data without writing a single HTML tag. You define your dashboard in YAML or TSK, and the tool will serve the HTML file for you. It comes with skills for Claude Code and Codex, so they know how to build these dashboards. And you can connect this to Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and many other databases. Repo link below.

Markdown was doomed from the start. It's just a format with low information density. HTML is better for humans, and agents can now consume and produce it without issues. But nobody wants to type HTML, so here is an alternative: This is an open-source tool for generating dashboards from data without writing a single HTML tag. You define your dashboard in YAML or TSK, and the tool will serve the HTML file for you. It comes with skills for Claude Code and Codex, so they know how to build these dashboards. And you can connect this to Postgres, MySQL, Snowflake, BigQuery, Redshift, Databricks, and many other databases. Repo link below.

Santiago

86,275 次观看 • 1 个月前

The hard part of building is no longer writing code. Code is easy. Nobody wins by writing more code or doing it faster. If you want to win, you need better thinking. There are a ton of startups focusing on using AI to write more code. I think they are missing the plot. We need a mental shift. We need tools that help us think faster, make better decisions, and focus on the right problems. I want you to check out Atoms because they are doing exactly this. They don't focus on the code, but on helping you build the right structure: 1. It starts by deep-researching marketing insights 2. It turns that into an execution plan 3. It builds both frontend and backend 4. It helps with deployment, SEO, growth, and analytics More importantly, it specializes in the core components you need to build and sell software online: • Authentication flows • Stripe payments • Infrastructure Their goal here is not to be another autocomplete tool or a copilot to help you write software. Their goal is to become a "vibe business team" that helps you build a business from the ground up. Here is a link for you to check them out: Thanks to the team for helping me onboard on the platform and collaborating with me on the post.

The hard part of building is no longer writing code. Code is easy. Nobody wins by writing more code or doing it faster. If you want to win, you need better thinking. There are a ton of startups focusing on using AI to write more code. I think they are missing the plot. We need a mental shift. We need tools that help us think faster, make better decisions, and focus on the right problems. I want you to check out Atoms because they are doing exactly this. They don't focus on the code, but on helping you build the right structure: 1. It starts by deep-researching marketing insights 2. It turns that into an execution plan 3. It builds both frontend and backend 4. It helps with deployment, SEO, growth, and analytics More importantly, it specializes in the core components you need to build and sell software online: • Authentication flows • Stripe payments • Infrastructure Their goal here is not to be another autocomplete tool or a copilot to help you write software. Their goal is to become a "vibe business team" that helps you build a business from the ground up. Here is a link for you to check them out: Thanks to the team for helping me onboard on the platform and collaborating with me on the post.

Santiago

78,872 次观看 • 5 个月前

If you could only learn one thing that will be relevant for the next 10-20 years, focus on learning how to deal with data. The future is not about faster hardware, smarter algorithms, or better ideas. The future is about DATA, and those who know how to deal with it will stay relevant much longer than anyone else. I recorded a video to show you how easy it is to get started. In the video, I'm using Kestra. For a long time, I was a fan of AirFlow. Then, I moved to AWS Step Functions. Today, I only use Kestra. Kestra is open-source (repo link below) and kind enough to sponsor my work. The video will show you how easy it is to do the following: 1. Run Kestra locally (literally, one command) 2. Build a simple flow 3. Run Python scripts as part of your flow 4. Connect to HuggingFace models If you have never built a data pipeline, open Kestra's Quick Start Guide and follow their examples. (I think it will take you one weekend to feel comfortable with the application and build the courage you need to get into more serious work.)

If you could only learn one thing that will be relevant for the next 10-20 years, focus on learning how to deal with data. The future is not about faster hardware, smarter algorithms, or better ideas. The future is about DATA, and those who know how to deal with it will stay relevant much longer than anyone else. I recorded a video to show you how easy it is to get started. In the video, I'm using Kestra. For a long time, I was a fan of AirFlow. Then, I moved to AWS Step Functions. Today, I only use Kestra. Kestra is open-source (repo link below) and kind enough to sponsor my work. The video will show you how easy it is to do the following: 1. Run Kestra locally (literally, one command) 2. Build a simple flow 3. Run Python scripts as part of your flow 4. Connect to HuggingFace models If you have never built a data pipeline, open Kestra's Quick Start Guide and follow their examples. (I think it will take you one weekend to feel comfortable with the application and build the courage you need to get into more serious work.)

Santiago

51,012 次观看 • 1 年前

English is the most powerful language in the world. MongoDB just added support for MCP, and literally anyone can now query data without knowing anything about databases. If you know English, you are all set. I recorded a quick video to show you how I connect to a sample Airbnb database and run different queries: • How many listings? • How many listings in the US? • What's the list of countries in these listings? It takes 10 seconds to configure Claude, Cursor, WindSurf, or any other AI assistant to connect to MongoDB's MCP Server. By the way, the server supports much more than queries: • You can explore your collections • You can fetch schema information • You can generate app code snippets • You can manage your databases • You can handle cluster/user/admin tasks Here is a link to the MCP server GitHub repository: This is huge for developers. It's even bigger for non-developers. Thanks to the MongoDB team for collaborating with me on this post.

English is the most powerful language in the world. MongoDB just added support for MCP, and literally anyone can now query data without knowing anything about databases. If you know English, you are all set. I recorded a quick video to show you how I connect to a sample Airbnb database and run different queries: • How many listings? • How many listings in the US? • What's the list of countries in these listings? It takes 10 seconds to configure Claude, Cursor, WindSurf, or any other AI assistant to connect to MongoDB's MCP Server. By the way, the server supports much more than queries: • You can explore your collections • You can fetch schema information • You can generate app code snippets • You can manage your databases • You can handle cluster/user/admin tasks Here is a link to the MCP server GitHub repository: This is huge for developers. It's even bigger for non-developers. Thanks to the MongoDB team for collaborating with me on this post.

Santiago

44,818 次观看 • 11 个月前

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to *millions* of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!

An LLM with native access to find and run agents is a huge thing! This large language model (called ASI:One and developed by Fetch.ai) can do this natively when you show it a problem: • Reason about the problem • Identify live agents and tools that can help solve it • Dynamically invoke these agents and tools Honestly, this is pretty nuts! You don't need to set up anything beforehand. You don't need to write any code. Here is the best part: ASI:One has access to any agent published on Agentverse, an open directory of agents! This gives the model access to millions of tools it can execute dynamically when solving a task. Here is a simple example (the one in the attached video): • I went to Google Maps • I copied the latitude and longitude of a location • I asked ASI:One about relevant places around that location • ASI:One went to the marketplace • It identified and called the Google Places agent • It returned a list of museums, restaurants, and landmarks near my location Pretty awesome! You can try this now by going to You don't need to pay or register to test it out. If you want to get ideas of what's possible, check Agentverse, the agent marketplace: The model will have access to all of those tools. This is pretty awesome and opens up a whole new world of possibilities! Thanks to the team for partnering with me on this post!

Santiago

43,782 次观看 • 1 年前