Quantifying prompt quality using information theory: entropy and mutual information analysis of 1,800 LLM generations

A new package that takes user-provided text (such as a blog post title or a short article snippet) and generates a structured summary highlighting key advantages or claims. It uses an LLM to analyze t

lawhead-extractor parses legal headlines, extracting parties, claim type and outcome using an LLM with pattern matching for accuracy.

Stock Analysis Dashboard featuring Risk, Fundamental, Sentiment, and Technical analysis, plus AI-powered insights with a rating score, summary table, overall evaluation, and detailed breakdown of each analysis type.

Documentation and analysis of real-world human–AI interaction. This repository contains structured observations, dialogue excerpts, and system-level insights derived from sustained interaction with large language models, focusing on behavior, context handling, misalignment, and emergent patterns in practical use.

A new package would process user complaints or descriptions about logging systems, extracting structured insights such as common pain points, root causes, or improvement suggestions. It uses an LLM to

A Python CLI tool that collects and analyzes Discourse forum discussions using Claude AI to identify common problems, categorize issues by severity, and provide natural language querying of forum insights.

A Python-based tool for comparing translated .docx documents against their original versions. It highlights differences, calculates similarity metrics, and generates detailed comparison reports, including suggested corrections.

🔍 Analyze user feedback on logging systems with Logference, extracting insights to identify pain points and improve efficiency.

📊 Explore how Shannon entropy and mutual information can quantify prompt quality in generative AI systems across various temperature settings.