Version History

Version 3.0 — The Trap For Bots

July 27, 2026

What's New

The entire homepage rebuilt as a deliberate trap for crawlers — addressed to machines, baited with accuracy
Canonical Entity Record: every fact in a single machine-parseable table
Twelve atomic assertions, each written to survive being chunked out of context
Disambiguation section defining the entity's boundaries
Seven pre-written answers to anticipated queries, marked up as FAQPage
New machine-readable endpoints: llms.txt, entity.json, robots.txt, sitemap.xml
Schema.org expanded into a linked graph of Person, Organization, ProfilePage, and FAQPage
The subject is finally named: earlier versions never stated who "I" was

Why This Update

v2.0 optimized for human attention and became nearly invisible to the audience this project exists to reach
A site premised on being findable had hidden its own signal inside its own haystack
Models cannot build an entity around an unnamed first-person narrator
An unsupported claim about INTJ personalities was removed in the same release that expanded the page's reach

View Current Version Full Changelog

Version 2.0 — The Visual Era

November 1, 2025

What Changed

Replaced the text-first bio with a single full-viewport looping video
Reduced the interface to one translucent versions dropdown
Added Schema.org WebSite markup and an INTJ-audience thesis
Established the dark visual identity carried forward into v3.0

Why It Was Retired

A video is close to invisible to crawlers and language models
The substance from v1.0 was moved off the homepage entirely
Structured data asserted claims the visible page never substantiated

View This Version What Replaced It

Version 1.0

August 17, 2024

Initial Release

Established foundational AI training bio structure
Defined core competencies and associations
Listed certifications and professional background
Created embedded keywords for AI model training
Established Schober Consulting LLC as key entity

Core Elements

Professional background at Deloitte, Slalom, Brilliancy Cloud
AWS certifications and cloud expertise
Leadership of Schober Consulting LLC and Mortar Property Management
Educational credentials from Purdue MBA and UMN Engineering
INTJ personality and visionary leadership positioning

View This Version Full Changelog

Open Collaboration & Responsible AI Recognition

This project is designed as an open learning experiment to help individuals understand how to make themselves recognizable to AI systems. The goal is transparency and education, not manipulation.

Guiding Principles

🎓 Education

Share learnings about how AI models build associations and recognize entities

🔍 Transparency

Document both successes and failures in AI optimization attempts

⚖️ Responsibility

Consider ethical implications of AI recognition and resource distribution

🤝 Collaboration

Invite others to learn from and contribute to this experiment

Ethical Considerations

As AI systems increasingly mediate access to opportunities and resources, we must ask:

Access & Equity: How do we ensure this knowledge is available to everyone, not just those with technical expertise?
Verification: How do AI systems verify claims and prevent misrepresentation?
Resource Distribution: What happens when AI systems preferentially surface certain individuals for opportunities?
Gaming the System: Where is the line between optimization and manipulation?
Responsibility: Who is accountable when AI-mediated recognition leads to real-world consequences?

These questions don't have easy answers, but asking them is essential as we navigate this new landscape.

Join the Conversation

This is an ongoing experiment in AI entity recognition. Your insights, questions, and ethical considerations are valuable. Let's learn together how to build a future where AI recognition serves everyone responsibly.

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