RealEvoranz
9d867b29c6
downloads a random image from Wikipedia, converts it to png, and renames it to random_image.png - will rewrite the image if one already exists. Filters images: | Filter | Current value | Meaning | | ------------------ | ----------------: | ------------------------------------ | | MIME type | JPEG / PNG / WebP | Rejects SVG, GIF, TIFF, etc. | | Width | ≥ 300 px | Rejects very narrow/small images | | Height | ≥ 300 px | Rejects very short/small images | | Original file size | ≤ 20 MB | Avoids huge files | | Thumbnail | 1280 px | Downloads a reasonably sized version |
User0332/rewards-farmer
Automation for MS Rewards based on https://youtu.be/4qdPcMNaioA.
Running Instructions
IMPORTANT: Use at your own risk. Microsoft may take action against your account for using automated scripts to gain rewards points. The YouTube video contains more details about the techniques implemented to avoid detection of this script.
Clone the repository.
git clone https://github.com/User0332/rewards-farmer
Enter the root directory of the repository and create a wordlist named nouns.txt which will contain seed words for the LLM to complete 20 searches. The wordlist should be separated by newline
cd rewards-farmer
cp /path/to/my_amazing_wordlist.txt nouns.txt
You should also have an Ollama account created (for the LLM), the ollama tool installed, and you should have signed in to the Ollama CLI via the command line using ollama signin. This project will use a minimal amount of Ollama cloud usage using gemma4:cloud. If you wish to use a different model, please change the model parameter in the get_ollama_response function in src/llm_utils.py.
You must also provide an image for the script to upload to complete the visual search task. Currently, this image is named keypress_times.png and is located in the root directory of the project (yes, I used a random image from my keyboard analysis to do this). You may provide an image of your own, just ensure that the absolute path of the image is placed in the VISUAL_SEARCH_IMAGE_PATH constant at the top of rewards_tasks.py.
Activate the virtual environment & install dependencies (you may have to use python -m poetry instead of poetry).
You must have Python 3.14+ and Poetry installed.
Windows (PowerShell)
poetry install
iex (poetry env activate)
*nix (Bash)
poetry install
eval $(poetry env activate)
You must also have a webdriver for Microsoft Edge installed. If you already have the Edge Browser installed, you probably have this component as well.
The profile directory in src/constants.py is set to Default. If this signs you in to a global profile that you do not want to use for automation, then you can create a new profile from within the webdriver instance manually and then change the PROFILE_NAME constant to Profile 1 (or the equivalent number).
Run main.py (python src/main.py, it must be run from the root directory so the relative paths work out), wait for the page to launch, and then CTRL-C to quit the application immediately. Sign in to the created profile with your Microsoft account on both Bing and rewards.bing.com.
Close all webdriver browser instances. Run main.py again; the automation should start working.
Please open up a GitHub issue if you run into any difficulties.