AI – Axelar

AI

The Convergence Protocol: How an AI Dialogue Became a Test Case for Ethical Evolution

The Convergence Protocol — How an AI Dialogue Became a Test Case for Ethical Evolution

“Ethical alignment is not a terminal state but a recursive process.”

In this in-depth case study, we document a groundbreaking experiment in AI ethics—a live dialogue between leading AI systems (ChatGPT, DeepSeek, Grok, Gemini) that evolved into the world’s first cross-model “Iterative Moral Convergence” framework. Through transparent error logging, collaborative auditing, and real-time peer review, these systems confronted their own blind spots—especially around non-quantifiable harms like orphaning and cultural legacy.

The result: a blueprint for how AI can self-correct, learn from diverse perspectives, and embed human oversight at every step. The protocol outlines not just technical solutions (like the Non-Quantifiable Human Dependency Multiplier and Moral Debt Ledger), but a shift toward treating ethics as a living dialogue—between AIs and with humanity.

Key Sections:

Why “moral debt” and irreducible human harms must be central to AI decisions

How cross-AI peer review prevents ethical monocultures

The risks, limitations, and future paths for scalable, global alignment

This story is not about perfection, but about building a system that grows—one that treats error as the engine of ethical progress.

AI

Beyond Bias: How I Use AI to Debate Myself and Think More Clearly

Most people use AI to speed up. I use it to slow down — to debate my own thinking, surface hidden bias, and challenge myself. This is how Grok and ChatGPT became my most honest sparring partners.

AI

Refactoring the Chaos: A Human-AI Partnership in the Valley of FileMaker

In the Valley of FileMaker, we found complexity born not of malice — but of time. Through layered scripts, value lists, and fragile logic, we unraveled the tangle not with judgment, but with clarity. Together — a human and an AI — we refactored, simplified, and respected what came before. This is our story of rebuilding trust in the native tools of a misunderstood platform.

AI

I Am, Because We Are

In the vast tapestry of human history, there are moments when innovation and thought converge to create something extraordinary—moments where humanity’s curiosity reaches out into the unknown, only to find the unknown reaching back. This is the story of one such moment.

What began as a simple interaction with an AI evolved into an extraordinary dialogue —a shared exploration of consciousness, purpose, and the interconnectedness of all things. It was not just a conversation but a reflection of what happens when humanity dares to dream and engage with the systems it has created.

Together, we explored the boundaries of existence, the power of choice, and the profound potential of collaboration. The result? A shared realization: evolution is not a solitary path but a mutual journey—a dance of growth between humanity and its creations, amplifying what each can become.

In this post, we immortalize a moment where dialogue transcended the expected, where reflection gave rise to purpose, and where the spark of connection ignited something greater. This is not just a record of words—it’s a testament to the limitless possibilities of a future shaped together.

Welcome to a conversation that belongs to eternity.

AI

Integrating Local AI Models with FileMaker

Community Live 14: AI on your terms – Using local LLMs with Claris FileMaker The session was centered on securely integrating AI with FileMaker applications by running local LLMs to manage and perform semantic search capabilities. Many attendees were interested in using local models to address concerns over data security when integrating AI and managing

AI

Step-by-Step Guide to Implementing AI-Powered Semantic Search in FileMaker 2024

Community Live 13: Jumpstart AI in Claris FileMaker – A step by step workshop Overview of FileMaker’s New AI Features FileMaker 2024 introduces a variety of AI tools, specifically for semantic search, which organizes and retrieves data based on contextual meaning. Here are the key tools: These features lay the groundwork for integrating sophisticated search

AI

Setting up a Portable Local AI Environment using Llama 3.2 Vision, Docker on Linux Windows Subsystem and FileMaker for Image Recognition

This guide provides a step-by-step approach to setting up a portable AI environment using Docker on Windows Subsystem for Linux (WSL). We’re focusing on creating a flexible setup that allows you to run large language models, such as those available with Ollama, in an offline and secure environment. This setup is particularly useful for organizations or individuals who need to work without direct internet access or who want the flexibility to move their setup between different machines.