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You have to check out the new ChatGPT “Code Interpreter”… Game-changing for data analysis 🤯

Justin Fineberg

8,085 subscribers

298,386 Aufrufe • vor 3 Jahren •via X (Twitter)

Anya Rossi• Live Now

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10 Kommentare

Profilbild von Akkio

Akkiovor 3 Jahren

We have a similar feature called "Chat Explore". After you upload your dataset, we create a conversational interface for you to chat with your data. Chat Explore also generates a URL you can share with teammates and they can use it without logging in.

Profilbild von Melanie Lauren

Melanie Laurenvor 3 Jahren

Holy shit!!

Profilbild von Marc In The Matrix⚡️🧠

Marc In The Matrix⚡️🧠vor 3 Jahren

This is insane 🤯🤯🤯

Profilbild von Joel Moran

Joel Moranvor 3 Jahren

This is sick! Thanks for sharing.

Profilbild von Kimchi_AI

Kimchi_AIvor 3 Jahren

Heard amazing stuff yesterday, in Ethan's newsletter! -Kim

Profilbild von Edrove

Edrovevor 3 Jahren

I'm still trying to figure out how to use this. It seems like it has a lot of potential uses but I still don't know them 😭

Profilbild von Abbakar M

Abbakar Mvor 3 Jahren

Thank you More of these please

Profilbild von oscar

oscarvor 3 Jahren

Why nobody asks how to get access to that chatgpt versión? .. is it publicly available?

Profilbild von doug

dougvor 3 Jahren

Great video. Thanks!

Profilbild von Vanessa Nacamura

Vanessa Nacamuravor 3 Jahren

@ezdubs_bot English German

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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!

👀 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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Open Interpreter's new release looks great for locally running LLMs The tool lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing. Just do `pip install open-interpreter` and then just run `- interpreter --local` sets up fast, local LLMs. Congrats killian 👏

Open Interpreter's new release looks great for locally running LLMs The tool lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing. Just do `pip install open-interpreter` and then just run `- interpreter --local` sets up fast, local LLMs. Congrats killian 👏

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