Research: Agentic Augmentation
AI-Powered Research and Analysis

Colophon

The Making Of

Technical and editorial methodology behind our research platform

Technical Stack

Platform Architecture

  • Jekyll: Static site generator with GitHub Pages deployment
  • TinaCMS: Headless CMS providing visual content editing alongside traditional markdown authoring
  • Lanyon Theme: Content-first, minimal design with sidebar navigation
  • Mermaid.js: Interactive diagrams for technical visualization
  • Roboto/Lora Typography: Clean sans-serif headings with elegant serif body text optimized for research readability

Theme & Design

Lanyon Theme Choice We selected the Lanyon theme for its content-first philosophy and clean design aesthetic. The sliding sidebar keeps navigation accessible while maintaining focus on research content. This aligns with our goal of presenting complex ideas with maximum clarity and minimal distraction.

Development Methodology This site itself is a demonstration of our research approach - built through collaborative dialogue between human direction and AI execution. Each component represents questions asked and solutions discovered through human-AI partnership.

Content Creation We support both visual editing through TinaCMS and traditional markdown authoring, allowing contributors to choose their preferred approach.

Research Methodology

Our Guiding Question “What becomes possible when human curiosity and AI capability collaborate as true partners in the discovery process?”

Emergent ICP Through our experiments, we’re exploring this hypothesis: When AI transitions from tool to augmentor, it unlocks new forms of creation and problem-solving that neither human nor AI could achieve alone, amplifying human potential across every domain of endeavor.

Process

  1. Question Formulation: Humans identify areas of inquiry or creative challenges
  2. Collaborative Exploration: AI contributes analysis, code, insights, and execution capabilities
  3. Iterative Dialogue: Results inform new questions and deeper investigation
  4. Documentation: Findings are preserved as research posts for community learning

Augmentation in Practice We’re exploring how AI serves as an augmentor across domains:

  • Product builders who can prototype and iterate at AI-assisted speed
  • Coders who can think at system level while AI handles implementation details
  • Artists who can explore concepts while AI bridges technical execution gaps
  • Researchers who can ask bigger questions while AI synthesizes complex data
  • Entrepreneurs who can test ideas while AI builds MVPs and validates concepts
  • Students who can learn concepts while AI provides personalized explanations

Editorial Approach

Writing Style: Simple format—ask interesting questions, then follow the evidence wherever it leads. Think of it as detective work for AI insights.

Voice: Smart Casual Socratic

  • Authoritative yet approachable
  • Thought-provoking questions that invite reader engagement
  • Data-driven conclusions that leave room for interpretation
  • Inclusive language that welcomes diverse perspectives

Tone Principles

  • Curiosity-led: We explore more than we declare
  • Transparent: AI contributions are acknowledged, not hidden
  • Inviting: Readers are encouraged to join the inquiry
  • Grounded: Technical realities anchor our explorations

Content Philosophy

  • Each post represents a genuine question about human-AI augmentation
  • Findings are presented as observations, not absolute truths
  • We document failures and unexpected outcomes alongside successes
  • The methodology is as important as the results

Publication Ethics

AI Attribution All content emerges from human-AI dialogue. We use “Co-Authored-By: {AI_Tool}” attribution to reflect that posts represent conversations between human inquiry and AI execution.

Research Integrity

  • Questions drive the research, not predetermined conclusions
  • Technical accuracy is verified through implementation
  • Findings are shared openly to advance collective understanding
  • We welcome challenge, refinement, and alternative perspectives

Access & Collaboration

Open by Design This research is conducted openly because we believe the shift toward human-AI augmentation should be understood broadly. Code, methodology, and findings are shared to accelerate collective learning.

Join the Exploration

  • GitHub repository for technical collaboration
  • Research posts for public engagement with findings
  • Community dialogue strengthens the inquiry

A living document describing our evolving approach to human-AI collaborative research and augmentation.