Artificial intelligence is one of the fastest-moving technological fields in history, but understanding it often feels fragmented. New models are released constantly, terminology changes quickly, and practical implementation often lags behind public discussion.
This resource library exists to make that landscape easier to navigate.
Rather than focusing on hype cycles or short-lived trends, this page organizes durable AI knowledge: terminology, models, infrastructure, governance, workflows, and research. It is designed for professionals, researchers, builders, and curious learners who want a clearer understanding of how modern AI systems work and how they are being applied.
Whether you are trying to understand a term, compare models, improve your workflows, or explore broader questions about AI adoption and governance, this page acts as a central starting point.
Understanding AI Terminology
One of the biggest barriers to understanding AI is language. Terms like inference, fine-tuning, embeddings, retrieval, agents, alignment, and multimodal systems are often used interchangeably or without explanation.
To help reduce that complexity, I maintain a growing AI Glossary.
The glossary is designed as a practical reference rather than an academic dictionary. It focuses on explaining concepts in plain language while preserving technical accuracy, making it useful for both newcomers and experienced practitioners.
If you encounter unfamiliar terminology while reading about AI, this is the best place to start.
Resource: AI Glossary
Understanding AI Models
Not all AI models are the same.
Different models have different architectures, capabilities, trade-offs, and intended use cases. Understanding the differences between proprietary and open models, large and small models, multimodal systems, and agent-oriented architectures is increasingly important for anyone working with AI.
The AI Model Glossary is designed to serve as a reference layer for that ecosystem.
It tracks model families, explains naming conventions, and provides context for how various models fit into the broader AI landscape. This includes commercial systems, open source alternatives, and emerging specialized models.
For anyone comparing systems or trying to understand the model ecosystem beyond marketing language, this is one of the most useful starting points.
Resource: AI Model Glossary
AI Research and Analysis
Beyond tools and terminology, AI raises broader questions about infrastructure, trust, governance and the future of knowledge work.
My research and writing in this area focuses less on model announcements and more on structural questions:
- How is AI changing information systems?
- What does trustworthy AI infrastructure look like?
- How do humans adapt their workflows around AI?
- What new risks emerge when provenance becomes unclear?
These longer-form essays and research papers explore those questions in more depth.
Topics include AI adoption, digital trust systems, provenance, knowledge workflows, commercialization, and infrastructure resilience.
Resource: Research Archive
AI in Practice
Understanding AI conceptually is only part of the equation. The other part is implementation.
A major focus of my work is practical AI integration: how individuals and organizations can build repeatable, effective workflows around AI tools.
This includes prompt systems, structured workflows, summarization pipelines, information triage systems, and knowledge preservation strategies.
The goal is not simply using AI more often, but using it more intentionally.
This area of the resource library will continue to expand as new frameworks and implementation guides are published.
Open AI Projects
I also maintain and contribute to several open source and research-oriented AI projects.
These projects explore areas like provenance systems, knowledge infrastructure, maintainability standards, and digital trust architectures.
They represent a practical extension of the ideas explored throughout these resources: building systems that make AI more understandable, more trustworthy, and more durable.
Selected projects include:
- ProvenanceDB — provenance-native data systems for trust-aware AI workflows.
- AetherInstitute — open research into AI systems, autonomy, and digital infrastructure.
As these projects mature, they will increasingly feed back into the resources published here.
Why This Exists
AI literacy is becoming foundational.
Not because everyone needs to build models, but because AI is rapidly becoming part of how information is created, distributed, interpreted, and acted upon.
Understanding the systems behind it matters.
This resource library is designed as a long-term knowledge base for that purpose: a place to document concepts clearly, track changes in the ecosystem, and build practical understanding over time.
It will continue to grow alongside my research, writing, and open source work.