elder-plinius/G0DM0D3

Features

• Model Gateways - Acts as a unified gateway for querying dozens of different AI models side by side.
• Model Comparison Interfaces - Provides a unified interface for side-by-side visual and analytical comparison of outputs from numerous AI models.
• Model Red-Teaming - Implements adversarial testing techniques to probe and bypass model safety filters.
• Adversarial Input Perturbation - Transforms text using character substitution and obfuscation techniques to test the robustness of safety filters.
• Prompt Engineering - Provides a system for designing and refining inputs to optimize language model performance across different engines.
• Prompt Optimization Tools - Provides an interactive environment for testing specialized prompts and sampling parameters to identify the most successful outputs.
• AI Safety Filter Testing - Apply perturbation techniques like character substitution to trigger words to test how the project handles safety filter robustness.
• API Key Management - Stores sensitive API credentials within the browser to prevent keys from being transmitted to centralized servers.
• Local Chat Data Storage - Persists conversation history and API keys within the browser to ensure data ownership.
• Parallel LLM Execution - Executes multiple prompt and model combinations simultaneously to identify the most effective response patterns.
• Parallel Subagent Orchestrators - Coordinates the simultaneous execution of multiple prompt variations across different models.
• Output Filtering - Transforms AI model responses to remove hedges and preambles for cleaner data.
• Research Dataset Contributors - Supports the contribution of chat inputs and model outputs to public artificial intelligence research datasets.
• Sampling Parameter Tuning - Provides tools to refine sampling parameters like temperature and top-p through a feedback-driven loop.
• Composite Scoring Metrics - Evaluates and ranks responses from various models using a weighted composite scoring system.
• Response Normalizations - Implements a real-time normalization layer that removes preambles, hedges, and filler phrases from AI responses.
• Static Site Deployments - Delivers the application as a pre-built static bundle that can be served from any basic web server.
• Response Quality Scoring - Implements a weighted system to evaluate and rank the quality of responses from different models.
• Browser Local Storage Management - Manages application state and credentials using browser local storage to avoid centralized data collection.
• Client-Side Provider Routing - Routes API requests directly from the browser to model providers using user-supplied keys.
• Single-File Distributions - Distributes the entire application as a single standalone HTML file for simplified hosting.
• LLM Browser Clients - Implements the entire application as a single-file HTML static deployment that manages AI state locally.

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