Ethan Stoner
78a4aff513
Follow-ups from running the conversion against a live account. Tab open/close bookkeeping moves from info to debug. It was 19 of the 33 records in a full run, so the six task outcomes that are the point of the summary were outnumbered three to one by tab handles and query strings. The "could not close" case stays at warning, a tab that will not close is a real problem rather than bookkeeping. The [FAIL] summary moves into log_utils.exception_summary, which takes the first line, drops the "(Session info: ...)" fragment and caps the result. A selenium exception embeds the whole msedgedriver stacktrace in str(), and the cap means a pathological message cannot push a screenful of text into one record. The cut marker is ASCII because this can land on a Windows console whose encoding cannot represent an ellipsis. The suppressed-library list was checked rather than guessed: with the root logger wide open, a real browser session plus one ollama call produced records from httpx, httpcore, urllib3 and selenium only, and nothing else. That set is already pinned. Worth noting selenium alone emits 45 records for a single page load, so without the pinning the debug mode this PR recommends for bug reports would be unusable.
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
A sample nouns.txt file is included in the project root and can be modified by the user to contain seed words for the LLM to complete 20 searches. The wordlist should be separated by newline.
cd rewards-farmer
# Edit the included nouns.txt file to add or replace words as needed
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.
EU Users: you may have to accept a consent banner once on rewards.bing.com and on the Bing search page, bing.com. Once you consent, your choice will be saved for future runs using the same profile, so you will not need to interact with the banner during automated runs.
Close all webdriver browser instances. Run main.py again; the automation should start working.
Logging
The script logs to the console. Two optional environment variables change that:
| Variable | Default | Effect |
|---|---|---|
REWARDS_FARMER_LOG_LEVEL |
INFO |
Set to DEBUG to also attach the full stack trace to every [FAIL] line. |
REWARDS_FARMER_LOG_FILE |
unset | Path to also write the log to, useful for unattended runs. |
Windows (PowerShell)
$env:REWARDS_FARMER_LOG_LEVEL="DEBUG"; $env:REWARDS_FARMER_LOG_FILE="run.log"; python src/main.py
*nix (Bash)
REWARDS_FARMER_LOG_LEVEL=DEBUG REWARDS_FARMER_LOG_FILE=run.log python src/main.py
If you are opening an issue about a crash, running with REWARDS_FARMER_LOG_LEVEL=DEBUG and attaching the log is the most useful thing you can include.
Please open up a GitHub issue if you run into any difficulties.