Search Results for "decryption"
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Showing 3 open source projects for "decryption"
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DataHub is the leading open-source data catalog helping teams discover, understand, and govern their data assets.Built on an open source foundation with a thriving community of 13,000+ members, DataHub gives you unmatched flexibility to customize and extend without vendor lock-in. DataHub Cloud is a modern metadata platform with REST and GraphQL APIs that optimize performance for complex queries, essential for AI-ready data management and ML lifecycle support.
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Securely stream and govern industrial data to power intelligent operations with agentic insights.Trusted MQTT Platform — Fully-managed and cloud-native MQTT platform for bi-directional IoT data movement.
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Stake Crash Predictor
Stake Crash Predictor is a toolkit for stake mines predictor & Plinko.
The Stake Crash Predictor is a focused toolkit that combines statistical analysis, optional server fairness seed hash decrypt helpers, and AI-assisted summaries to help you study rounds on Stake.us. This project centers on the stake mines predictor and stake predictor workflows Demo-focused stake crash predictor app — seed-inspection helpers (SHA-512 / SHA-256), AI-assisted summaries, and demo bot templates for stake mines predictor too, Start in demo mode to test safely. Disclaimer:... -
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Sulla
Javascript Whatsapp API library for chatbots
...Scan it with your phone and you are ready to go! Sulla will remember the session so there is no need to authenticate every time. By default QR code will appear on the terminal. The decryption is being done as fast as possible (outruns native methods). Supports big files! -
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CrypTen
A framework for Privacy Preserving Machine Learning
...Its design mirrors PyTorch’s modular and library-based structure, enabling flexible experimentation, debugging, and model development. The framework supports both encryption and decryption of tensors and operations such as addition and multiplication over encrypted values. Although not yet production-ready, CrypTen focuses on advancing real-world secure ML applications, such as training and inference over private datasets, without exposing sensitive data.
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