mirror of
https://github.com/User0332/rewards-farmer.git
synced 2026-09-16 01:31:36 +00:00
Ethan Stoner
af03afcc6d
Idea taken from TheNetsky/Microsoft-Rewards-Script, which builds search terms from public feeds rather than a model. No code from it: that project is GPL-3.0 and this one is MIT, so only the approach crosses over. The LLM has exactly two call sites here, both producing a short string to type into Bing. Everything the dependency costs, an Ollama account, cloud usage and the provider work in #15, is paid for search strings. Three keyless sources answer the same question: Google Trends RSS queries people are actually typing right now Wikipedia most-read topic seeds when trends is unavailable Bing autosuggest expands a seed into related queries Autosuggest is what makes the chaining work. Asking Bing what follows a term returns queries Bing already expects, which is nearer to what the prompt in llm_utils was reaching for than a model guessing unaided. Selected with QUERY_SOURCE=trends. The default stays llm, so no existing setup changes. stdlib only, no new dependencies. Measured against the LLM on the same cards from a live account: card llm trends airport parking best rates airport parking reservations reserve airport parking best rates checking vs savings compare checking vs savings accounts compare checking savings account options cruise deals best cruise deals and destinations cruise deals destinations Verified live with OLLAMA_HOST pointed at a dead port, so nothing could reach a model: five queries generated from feeds and three typed into Bing, each landing on a real results page. Every source degrades to an empty list rather than raising, and both entry points fall back, to the trimmed task description and to nouns.txt. A search that does not happen costs points; a run that dies costs the rest of the day.
69 lines
4.1 KiB
Markdown
69 lines
4.1 KiB
Markdown
# User0332/rewards-farmer
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Automation for MS Rewards based on [https://youtu.be/4qdPcMNaioA](https://youtu.be/4qdPcMNaioA).
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# Running Instructions
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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.
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Clone the repository.
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```sh
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git clone https://github.com/User0332/rewards-farmer
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```
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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.
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```sh
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cd rewards-farmer
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# Edit the included nouns.txt file to add or replace words as needed
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```
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# Where search queries come from
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The bot needs short strings to type into Bing. Two backends produce them, set with `QUERY_SOURCE`:
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| `QUERY_SOURCE` | Needs | Notes |
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| --- | --- | --- |
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| `llm` (default) | Ollama account + model | Current behaviour, unchanged |
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| `trends` | nothing | Google Trends, Wikipedia and Bing autosuggest |
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```sh
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QUERY_SOURCE=trends python src/main.py # bash
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$env:QUERY_SOURCE="trends"; python src/main.py # PowerShell
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```
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`trends` needs no account, no API key and no model download, so the Ollama setup below is optional if you use it. If every feed is unreachable it falls back to `nouns.txt` rather than failing the run.
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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`.
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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`.
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Activate the virtual environment & install dependencies (you may have to use `python -m poetry` instead of `poetry`).
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You must have Python 3.12+ and Poetry installed.
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If `iex (poetry env activate)` fails with *"Cannot bind argument to parameter 'Command' because it is null"*, `poetry install` did not create an environment. Run `python --version` first: an older Python leaves poetry with nothing to activate, and the message explaining that goes to stderr rather than into `iex`.
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Windows (PowerShell)
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```sh
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poetry install
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iex (poetry env activate)
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```
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*nix (Bash)
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```sh
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poetry install
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eval $(poetry env activate)
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```
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You must also have a [webdriver for Microsoft Edge](https://learn.microsoft.com/en-us/microsoft-edge/webdriver/?tabs=c-sharp) installed. If you already have the Edge Browser installed, you probably have this component as well.
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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).
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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`.
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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.
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Close all webdriver browser instances. Run `main.py` again; the automation should start working.
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Please open up a GitHub issue if you run into any difficulties. |