Search Results for "base 64 encoder" - Page 5

Showing 103 open source projects for "base 64 encoder"

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    Secure File Transfer for Windows with Cerberus by Redwood

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    Stop Storing Third-Party Tokens in Your Database

    Auth0 Token Vault handles secure token storage, exchange, and refresh for external providers so you don't have to build it yourself.

    Rolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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  • 1
    Mistral Large 3 675B Base 2512

    Mistral Large 3 675B Base 2512

    Frontier-scale 675B multimodal base model for custom AI training

    ...The model is engineered for reliability, long-context comprehension, and stable performance across many enterprise, scientific, and knowledge-intensive workloads. Its architecture includes a powerful language MoE and a 2.5B-parameter vision encoder, enabling multimodal understanding out of the box. Mistral Large 3 Base supports deployment on-premises using FP8 or NVFP4 formats, enabling high-performance workflows on B200, H200, H100, or A100 hardware.
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  • 2

    cyborgzoscii

    ZOSCII Zero Overhead Secure Coding Information Interchange

    ZOSCII Zero Overhead Secure Code Information Interchange. An innovative character encoding system that eliminates lookup table overhead while providing built-in security properties through direct memory addressing. Overview CyborgZOSCII is an alternative to ASCII/PETSCII that uses direct ROM addressing instead of traditional character-to-value mapping. This approach provides significant advantages for resource-constrained systems while offering unique security properties. Browser...
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  • 3
    Ministral 3 3B Reasoning 2512

    Ministral 3 3B Reasoning 2512

    Compact 3B-param multimodal model for efficient on-device reasoning

    Ministral 3 3B Reasoning 2512 is the smallest reasoning-capable model in the Ministal-3 family, yet delivers a surprisingly capable multimodal and multilingual base for lightweight AI applications. It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require logical reasoning, analysis, or structured thinking. ...
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