Interesting finds, updated throughout the week. Showing latest 3 additions.
An open-source toolkit for large-scale network analysis, providing parallel graph algorithms and a Python interface.
An open-source library providing a plug-and-play memory layer for large language models, designed for personal use and integration.
An open-source text-to-speech model for production use, featuring zero-shot voice cloning and emotion intensity control.
LLM-generated research, prompt explorations, and AI-assisted deep dives.
A deep dive comparing open-source and closed-source AI memory/knowledge graph solutions (vector DBs, graph DBs, hybrid) for agentic workflows, evaluating features like retrieval style, scalability, and LLM optimization.
A comprehensive analysis of leading open-source LLM observability, evaluation, and testing platforms, including pros, cons, intended audience, hosting requirements, and a comparison table.
A deep research exploration of AI agent patterns, their evaluation, and comparison for solutions architects.
Experimental projects and learning explorations in AI and development
Detailed tutorials and best practices for AI development and implementation
Brewing the perfect guides...
Just need one more cup of coffee to finish writing these.
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