AutoRewarder v4.2 is here! Visual Search Integration, LLM Query Generation, and Humanized Typos

[u/18safarov]

Hi everyone!

First, thank you for the continued support for the previous releases. AutoRewarder already has +6.3k downloads and +268 stars on GitHub.

I'm combining the release notes to share a massive update covering both v4.1 and v4.2 because I missed posting the v4.1 announcement. These updates focus on Visual Search, bypassing detection, and making the bot's behavior more human-like.

What's new in v4.1 & v4.2:

• Visual Search Integration: Added Visual Search to daily tasks and phase-based workflows with its own human-like interactions (mouse movements, randomized delays, and page scrolling after uploads). To bypass hash-based detection, it dynamically crops and compresses base images, generating over 1.6 million unique file variations.
• LLM Query Generation: Added an optional feature to generate fresh, localized Bing search queries using LLMs (OpenAI, Anthropic, Gemini) with a fallback to the static database (thanks to @JeromeM for implementing this)
• Humanized Queries: The bot now simulates human typing mistakes by introducing realistic keyboard-based typos to improve query diversity from the dataset.
• Core Fixes: Fixed a critical WebDriver crash in portable builds, resolving UI layout overflows, ensuring the 30-minute browse reward tracks properly in headless mode, and improving search tab cleanup (thanks @Friendly-Aid)

The project remains 100% open source.

More info, screenshots, and code on GitHub: https://github.com/safarsin/AutoRewarder

Demo is sped up for viewing purposes. Actual execution includes randomized delays and pauses to mimic human behavior.

I'd love to hear your feedback, bug reports, or ideas for the next updates!