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I've done analyses in Jupyter notebook for years. Now Code Interpreter can write all the code, but I haven't used it daily because you can't tweak code, re-run analyses, install packages, etc. I built Juno w/ Alexi Robbins to bring GPT into Jupyter for the best of the worlds

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Andrew Mauboussin

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49,845 görüntüleme • 2 yıl önce •via X (Twitter)

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Built an open-source Jupyter Notebook that brings the power of OpenAI's code interpreter into your local Python development environment. Watch how easy it is to use Thread to edit code I wrote, chat with my notebook, or generate charts just using prompts. We built Thread using Next.js, Chakra and Project Jupyter and you can install it by running: pip install thread-dev
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Built an open-source Jupyter Notebook that brings the power of OpenAI's code interpreter into your local Python development environment. Watch how easy it is to use Thread to edit code I wrote, chat with my notebook, or generate charts just using prompts. We built Thread using Next.js, Chakra and Project Jupyter and you can install it by running: pip install thread-dev

Ali Shobeiri

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AI coding just arrived in Jupyter notebooks - and bhupesh ganger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free):
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AI coding just arrived in Jupyter notebooks - and bhupesh ganger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearningAI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free):

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🕹️ HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels 🌌 Jupyter Notebook 🥳 I added quantization to HunyuanWorld 1.0, and it now runs with 24GB of VRAM 🥳 Thanks to Tencent Hunyuan - HunyuanWorld Team ❤ 🧬code: 🧬quant-code: 📄paper: 🌐page: 🍊jupyter:
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🕹️ HunyuanWorld 1.0: Generating Immersive, Explorable, and Interactive 3D Worlds from Words or Pixels 🌌 Jupyter Notebook 🥳 I added quantization to HunyuanWorld 1.0, and it now runs with 24GB of VRAM 🥳 Thanks to Tencent Hunyuan - HunyuanWorld Team ❤ 🧬code: 🧬quant-code: 📄paper: 🌐page: 🍊jupyter:

camenduru

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Introducing MakeReal in jupyter-tldraw ✨ Now you can hand-draw a plot on a tldraw whiteboard and turn it into Matplotlib code directly in a notebook.
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Introducing MakeReal in jupyter-tldraw ✨ Now you can hand-draw a plot on a tldraw whiteboard and turn it into Matplotlib code directly in a notebook.

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🎞 ToonCrafter: Generative cartoon interpolation 🤯 Jupyter Notebook 🥳 Thanks to ToonCrafter Team ❤ 🧬code: 🍊jupyter by please try it 🐣
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🎞 ToonCrafter: Generative cartoon interpolation 🤯 Jupyter Notebook 🥳 Thanks to ToonCrafter Team ❤ 🧬code: 🍊jupyter by please try it 🐣

camenduru

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Did you know that you can use GitHub Copilot in notebooks? Accept and run generated code with just a click. You can even attach variables from the Jupyter kernel in your requests ✨
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Did you know that you can use GitHub Copilot in notebooks? Accept and run generated code with just a click. You can even attach variables from the Jupyter kernel in your requests ✨

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You won’t believe what Agent Mode can do in Jupyter Notebooks 🤯
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You won’t believe what Agent Mode can do in Jupyter Notebooks 🤯

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Open Interpreter is like ChatGPT Code Interpreter, but you can run it locally on your computer, and it is 100% open-source. Write and execute code, analyze data, control your computer, and more. Great work killian Here's how to install and use it: 👇
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Open Interpreter is like ChatGPT Code Interpreter, but you can run it locally on your computer, and it is 100% open-source. Write and execute code, analyze data, control your computer, and more. Great work killian Here's how to install and use it: 👇

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🎙️ FLOAT: Generative Motion Latent Flow Matching for Audio-driven Talking Portrait 👄 Jupyter Notebook 🥳 Thanks to Taekyung Ki ❤ DongchanMin ❤ Gyeongsu Chae ❤ Thanks to Baku ❤ for the tutorial. 🌐page: 🧬code: 📄paper: 🍊jupyter:

camenduru

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Run MATLAB code directly in Jupyter notebooks, JupyterLab, and JupyterHub using the MATLAB kernel. Just open a browser-based MATLAB environment from Jupyter to directly access MATLAB apps and features. Get started with our repo: #Jupyter #Coding
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Run MATLAB code directly in Jupyter notebooks, JupyterLab, and JupyterHub using the MATLAB kernel. Just open a browser-based MATLAB environment from Jupyter to directly access MATLAB apps and features. Get started with our repo: #Jupyter #Coding

MATLAB

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👀 I used OpenAI's Code Interpreter to make Flappy Bird 🐦in 7 minutes: Code Interpreter/GPT-4 for code generation. Pre-existing or AI-generated assets for graphics. --- Here's how to make the game in only 6 steps: (1): Enter the following prompt: "write p5.js code for Flappy Bird where you control a yellow bird continuously flying between a series of green pipes. The bird flaps every time you left click the mouse. If the bird falls to the ground or hits a pipe, you lose. This game goes on infinitely until you lose and you get points the further you go". (2): Use generative AI or existing game assets and spirits. I searched "flappy bird assets" on Google and used the first link, a GitHub repo with pngs from the original Flappy Bird. (3): Use this prompt to link assets to the code: "Please generate the entire file again based on the fact I'm using a unique background, spirits for the bird, and pipes. Here is the list of assets I'm using: [list of file names]." Code Interpreter should modify the code accordingly to include the list of file names. (4) Make an account OpenProcessing -> create a sketch -> paste in the code generated by Code Interpreter -> upload in-game assets from step (2). (5) (Optional) Ask ChatGPT to make changes to improve the in-game experience e.g., adding a high score, restarting the game when the bird dies, etc. Copy the new code into your OpenProcessing sketch and reload the game. (6) If something doesn't work, ask GPT4 to fix it. Copy and paste the error message and ask it to regenerate the code. --- Bonus Tips: - Iteratively test code. Each time you make a change using Code Interpreter, test the updated code by playing the game so you catch new bugs early. - Learn programming by asking questions: "Act as a senior programmer very good at explaining concepts to a beginner. Tell me how gravity works in this game and how you used code to make this happen."Code Interpreter/GPT4 for code generation. Download Pre-existing assets or generate new images for graphics. Excited to see what you make!
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👀 I used OpenAI's Code Interpreter to make Flappy Bird 🐦in 7 minutes: Code Interpreter/GPT-4 for code generation. Pre-existing or AI-generated assets for graphics. --- Here's how to make the game in only 6 steps: (1): Enter the following prompt: "write p5.js code for Flappy Bird where you control a yellow bird continuously flying between a series of green pipes. The bird flaps every time you left click the mouse. If the bird falls to the ground or hits a pipe, you lose. This game goes on infinitely until you lose and you get points the further you go". (2): Use generative AI or existing game assets and spirits. I searched "flappy bird assets" on Google and used the first link, a GitHub repo with pngs from the original Flappy Bird. (3): Use this prompt to link assets to the code: "Please generate the entire file again based on the fact I'm using a unique background, spirits for the bird, and pipes. Here is the list of assets I'm using: [list of file names]." Code Interpreter should modify the code accordingly to include the list of file names. (4) Make an account OpenProcessing -> create a sketch -> paste in the code generated by Code Interpreter -> upload in-game assets from step (2). (5) (Optional) Ask ChatGPT to make changes to improve the in-game experience e.g., adding a high score, restarting the game when the bird dies, etc. Copy the new code into your OpenProcessing sketch and reload the game. (6) If something doesn't work, ask GPT4 to fix it. Copy and paste the error message and ask it to regenerate the code. --- Bonus Tips: - Iteratively test code. Each time you make a change using Code Interpreter, test the updated code by playing the game so you catch new bugs early. - Learn programming by asking questions: "Act as a senior programmer very good at explaining concepts to a beginner. Tell me how gravity works in this game and how you used code to make this happen."Code Interpreter/GPT4 for code generation. Download Pre-existing assets or generate new images for graphics. Excited to see what you make!

Alex Ker 🔭

739,811 görüntüleme • 2 yıl önce

🖼 Advanced Live Portrait 🔥 Jupyter Notebook + Tost AI + + ComfyUI 🥳 Thanks to Live Portrait Team ❤ github/PowerHouseMan ❤ 🧬code: 🍇runpod: 🗺workflow: 🍊jupyter: 🥪tost: please try it 🐣
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🖼 Advanced Live Portrait 🔥 Jupyter Notebook + Tost AI + + ComfyUI 🥳 Thanks to Live Portrait Team ❤ github/PowerHouseMan ❤ 🧬code: 🍇runpod: 🗺workflow: 🍊jupyter: 🥪tost: please try it 🐣

camenduru

50,816 görüntüleme • 1 yıl önce

i vibecoded a fully working terminal from scratch using parallel remote agents, w/o editing a single line of code. took the best agent just ~6 hrs would take me ~6-12 mo to do the same by hand w/o ai the terminal def has bugs (i posted the gh link in the article so you can test it) but can run opencode, claude code, etc! i've been able to replace iterm with it. wrote up a guide for Extreme Vibecoding with Remote Agents👇
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i vibecoded a fully working terminal from scratch using parallel remote agents, w/o editing a single line of code. took the best agent just ~6 hrs would take me ~6-12 mo to do the same by hand w/o ai the terminal def has bugs (i posted the gh link in the article so you can test it) but can run opencode, claude code, etc! i've been able to replace iterm with it. wrote up a guide for Extreme Vibecoding with Remote Agents👇

andrew gao

57,629 görüntüleme • 4 ay önce

Zerve has been the best platform for writing data science code for a while. It's 100x better than Jupyter notebooks. Now, they have a specialized, fine-tuned AI assistant who knows all about data science code and how to use the platform. This is a killer feature. A generalist model will never be better than a specialized one. Watch the video. I promise you'll be jealous in 3 minutes. Some other features, in case you've never used Zerve: 1. It's web-based 2. You can share and work with your team on the same canvas 3. You can deploy your code with a click 4. Blocks are the center of attention. Promotes code modularity 5. You can combine multiple languages in a single canvas 6. You can run code blocks in parallel Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. By the way, you can use Zerve for FREE. Visit this link: Thanks to the team for partnering with me on this post.
5:24

Zerve has been the best platform for writing data science code for a while. It's 100x better than Jupyter notebooks. Now, they have a specialized, fine-tuned AI assistant who knows all about data science code and how to use the platform. This is a killer feature. A generalist model will never be better than a specialized one. Watch the video. I promise you'll be jealous in 3 minutes. Some other features, in case you've never used Zerve: 1. It's web-based 2. You can share and work with your team on the same canvas 3. You can deploy your code with a click 4. Blocks are the center of attention. Promotes code modularity 5. You can combine multiple languages in a single canvas 6. You can run code blocks in parallel Zerve organizes your code as a DAG. Each block inherits the state of every block before it. Everything runs in a serverless architecture. By the way, you can use Zerve for FREE. Visit this link: Thanks to the team for partnering with me on this post.

Santiago

27,942 görüntüleme • 1 yıl önce

I built 3 functional web apps in 4min… on my phone! Replit ⠕ Agent don’t just review and write code, their AI agents plan the functions, create development environment, install dependencies, write the code, configure databases and deploy. Is this the beginning of the end of paying for software? What’d you create?
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I built 3 functional web apps in 4min… on my phone! Replit ⠕ Agent don’t just review and write code, their AI agents plan the functions, create development environment, install dependencies, write the code, configure databases and deploy. Is this the beginning of the end of paying for software? What’d you create?

Sander Saar

86,072 görüntüleme • 1 yıl önce

Excited to release: Jupyter Agent 2 The agent can load data, execute code, plot results inside Jupyter faster than you can scroll! 🤖 Powered by Qwen3-Coder ⚡️ Running on Cerebras ⚙️ Executed in E2B ↕️ Upload your files All videos are in *real time*!
1:15

Excited to release: Jupyter Agent 2 The agent can load data, execute code, plot results inside Jupyter faster than you can scroll! 🤖 Powered by Qwen3-Coder ⚡️ Running on Cerebras ⚙️ Executed in E2B ↕️ Upload your files All videos are in *real time*!

Leandro von Werra

66,086 görüntüleme • 9 ay önce

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.
1:40

I built an AI data visualization Agent. The AI Agent can automatically generate the code and visualize the data using code interpreter sandbox. 100% Opensource Code with step-by-step tutorial.

Shubham Saboo

67,845 görüntüleme • 1 yıl önce

After reaching AI-native terminal velocity we are swamped in code review. The core insight is that as long as you can write code in parallel you will continue to be stuck until you can review it in parallel. I was surprised by how quickly this became my daily driver for not just review code, but writing it as well.
0:30

After reaching AI-native terminal velocity we are swamped in code review. The core insight is that as long as you can write code in parallel you will continue to be stuck until you can review it in parallel. I was surprised by how quickly this became my daily driver for not just review code, but writing it as well.

Evan Boyle

23,954 görüntüleme • 4 ay önce