Safeshift
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About This Episode
In this episode of DevNTell, Narb welcomes hackathon pros Sahil Kakwani and Umang Patel to showcase SafeShift, their winning project from the Phala Network hackathon. SafeShift is a decentralized content moderation tool designed to prevent explicit (NSFW) content from being uploaded to Web3 social networks like Lens Protocol. The guests explain how the platform leverages Phala's fat contracts and off-chain computation to screen text and images through code-driven logic rather than human intervention. They provide a high-level overview of the architecture, including the use of TensorFlow.js and NSFWJS for image analysis, and demonstrate the app's functionality. The session concludes with a discussion of their future roadmap, which includes video support and integration with additional social protocols like Bluesky and Farcaster.
Key Takeaways
SafeShift addresses the challenge of content moderation in decentralized social networks where censorship resistance can sometimes lead to the spread of explicit material.
The project uses Phala Network's 'fat contracts' to perform off-chain computations, allowing for verifiable, decentralized moderation logic.
Text moderation is handled directly within Rust-based fat contracts, while image moderation utilizes a Node.js backend with TensorFlow.js and NSFWJS libraries.
SafeShift aims to foster a safer Web3 community by providing a code-driven middle layer for content screening before it reaches social protocols.
The future roadmap for SafeShift includes adding support for video content, migrating to Lens V2, and expanding to other decentralized networks like Bluesky and Farcaster.
SafeShift is open-source, and the developers encourage community contributions via their GitHub repository.
Featured Guests
Umang Patel
Founder of LensPlay
Sahil Kakwani
Founder of LensPlay
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