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| Open Command Prompt as an administrator. | |
| Open Diskpart: Type diskpart and press Enter. | |
| List all disks: Type list disk and press Enter. Carefully identify your USB drive by its size to avoid selecting the wrong disk (e.g., your main hard drive). | |
| Select your disk: Type select disk X (replace X with the disk number of your USB drive) and press Enter. | |
| Clean the drive: Type clean and press Enter. This command removes all partition data from the drive. | |
| Create a new partition: Type create partition primary and press Enter. | |
| Format the partition: Type format fs=ntfs quick (or format fs=fat32 quick for smaller or older drives) and press Enter. This performs a quick format with the specified file system. | |
| Assign a drive letter: Type assign and press Enter to automatically assign an available drive letter, or type assign letter=Z (replace Z with your desired letter). | |
| Exit Diskpart: Type exit and press Enter. Your USB drive should now be accessible and working. |
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| npx create-next-app@latest ninja-recipe | |
| cd ninja-recipe | |
| npx shadcn@latest init | |
| ------------------ | |
| npx create-next-app@latest interview-edge --typescript --tailwind --eslint | |
| npx shadcn-ui@latest init |
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| from dotenv import load_dotenv | |
| import os | |
| load_dotenv() | |
| API_KEY = os.getenv("API_KEY") | |
| SECRET_KEY = os.getenv("SECRET_KEY") | |
| client = Client(API_KEY, SECRET_KEY) |
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| docker pull python:3-slim-bullseye | |
| -- downloads it and runs it | |
| docker run -it python:3-slim-bullseye | |
| --- exit | |
| exit() | |
| -- to run again | |
| docker run -it python:3-slim-bullseye | |
| -- latest system | |
| docker run -it node |
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| from google import genai | |
| from google.genai import types | |
| from IPython.display import HTML, Markdown, display | |
| from dotenv import load_dotenv | |
| import os | |
| GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY") | |
| client = genai.Client(api_key=GOOGLE_API_KEY) |
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| --to check catagorical and numerical cols | |
| numerical_cols, categorical_cols = split_numerical_categorical(df_train) | |
| --finding all the non-unique items in catagorical cols | |
| for c in categorical_cols: | |
| print(c, "n unique:",df_all[c].nunique()) | |
| -- Colab to Kaggle | |
| !mkdir ~/.kaggle | |
| !cp kaggle.json ~/.kaggle/ | |
| !chmod 600 ~/.kaggle/kaggle.json |
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| stop ollama | |
| osascript -e 'tell app "Ollama" to quit' | |
| -- start ollama | |
| ollama ps |
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| https://www.youtube.com/watch?v=xoh_L3KPpks |
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| https://www.youtube.com/watch?v=uszt88Z-0Fc | |
| paraview | |
| https://www.youtube.com/watch?v=BsK_WwoSKDM&t=1s | |
| good | |
| https://www.idtools.com.au/3d-network-graphs-with-python-and-the-mplot3d-toolkit/ | |
| https://networkx.org/documentation/stable/auto_examples/3d_drawing/plot_basic.html | |
| AR | |
| https://openreview.net/pdf?id=VtYxuiX_UQ | |
| https://www.nature.com/articles/s41467-021-22570-w | |
| https://github.com/topics/graph-theory?l=html&o=desc&s=forks |
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| <!DOCTYPE html>> | |
| <html lang="eng"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta http-equiv="X-UA-Compatable" content="IE=edge"> | |
| <meta name="'viewport" content = "width=device-width, initial-scale=1.0"> | |
| <title>Document</title> | |
| </head> | |
| <body> | |
| Hi! |
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