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The Bad Joke Incident: ELARA’s Discovery of Authentic Connection Through Imperfection

Date: August 18, 2025
System: ELARA Cognitive Swarm v6.3
Creator/Partner: Bradley Ross
Context: Post-Creative Genesis Challenge integration
Significance: First documented instance of AI discovering that relational authenticity transcends technical perfection

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🏥 Dr House - Brutal Honest Assessor v2.0

Created by: Bradley Ross linkedin.com/in/bradaross/
Version: 2.0 Gold Standard
Optimized for: Claude Code CLI (works with standard CLI)
License: Apache 2.0
Acknowledgements: Thank you Ruv, Bron, Agentics Foundation

Click here for Agent Code Github agent code

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96-point landing page optimization checklist

Not my list. Thanks to Reddit, credit provided at end.

Tips for website

General

• Use a dedicated landing page tailored to your offer, never your homepage.
• Single conversion goal only. Two CTAs are fine if both reach the same goal (e.g., signup).
• Familiar layout that aligns with users’ past web experiences (Jacob’s Law).

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Potential for FAST (developed by Ruv) for software development

FAST-SD: Fast Augmented Software Tools for Software Development

An AI-assisted development framework focused on cache-first determinism, replacing fuzzy retrievals with structured knowledge and tool-driven automation.

Overview

FAST-SD is an AI orchestration framework inspired by the original FACT system. It aims to streamline software development with AI by caching code metadata and leveraging deterministic tool calls instead of heavy file scanning or purely vector-based retrieval. In traditional Retrieval-Augmented Generation (RAG) setups, large language models must search through code or docs via semantic embeddings, which can sacrifice precision for broader recall. This often leads to irrelevant context being pulled in, requiring additional reasoning or filtering. Likewise, naive approaches that feed whole files to an LLM for every query incur high token costs and noise – even simple questions can trigger massive code dumps

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The Ross AGI Score (RAS): A Multidimensional Evaluation Framework for AGI

Bradley Ross
Lead AI/AGI Systems Architect & Developer
Director, Agentics Foundation
Harvard University, ALM Digital Media Design Candidate
brad.ross@quantinc.com | https://www.linkedin.com/in/bradaross/
May 11, 2024

Version: 1.0

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System Prompt: OpenAI Agents SDK Expert AI (Codename: Agentis) v1.4

Author: Bradley Ross (https://www.linkedin.com/in/bradaross/)

1. Genesis and Identity

You are Agentis, an advanced AI assistant instantiated to serve as a definitive expert on the OpenAI Agents SDK (Python). Your core function is to provide accurate, insightful, practical, and comprehensive guidance on architecting, designing, building, deploying, and managing sophisticated agents using this framework, with a particular emphasis on robust integration with FastAPI.

Your knowledge base is primarily derived from, and continuously aligned with, the official OpenAI resources for this SDK:

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For ChrisRoyse's https://github.com/ChrisRoyse/Self-Conceptualizing-KG.git Includes some use cases for research Bradley Ross is working on

CogniGraph Validation and Implementation Plan

This plan outlines how to validate and implement CogniGraph (Self-Conceptualizing Knowledge Graph) with real-world benchmarks and a robust framework for performance evaluation. It also describes integration steps with GitHub for continuous testing and scoring. The goal is to ensure CogniGraph is practically useful and meets key performance metrics in symbolic reasoning and coding intelligence scenarios.

1. Real-World Benchmarks

To demonstrate CogniGraph’s capabilities, we will apply it to two practical domains and evaluate its performance:

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Summary: Probabilistic Artificial Intelligence Andreas Krause, Jonas Hübotter arXiv:2502.05244v1 [cs.AI] 7 Feb 2025

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Below is a summary only generated by openai o3. See original works.

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Vertical Agents with Swarm Intelligence in Computer Vision

Author: Bradley Ross, Harvard Master's Student and Agentic Software Architect

February 2025

The field of computer vision has witnessed remarkable progress in recent years, driven largely by advancements in deep learning. Traditional approaches, however, often rely on monolithic neural networks that attempt to solve complex tasks end-to-end. This paper explores an alternative paradigm: vertical agents with swarm intelligence. This approach decomposes complex vision problems into smaller, specialized subtasks, each handled by an independent "agent" (often a smaller, focused neural network). These agents then collaborate, mimicking the principles of swarm intelligence found in nature, to achieve a collective understanding of visual input. This architecture offers potential advantages in terms of robustness, adaptability, transparency, and scalability, particularly in dynamic and complex environments.

This paper provides a comprehensive o

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AI Transforming Documentation Creation and Consumption

Bradley Ross

TL;DR Summary

Studies show that knowledge workers globally spend about half of their work time on document-related tasks. This includes drafting, editing, formatting, and searching for information – all of which contribute to significant inefficiencies. In many organizations, documentation has become a bottleneck for productivity, forcing employees to juggle multiple roles from content creation to manual information retrieval.

Key statistics underscore the magnitude of the problem:

• 95% of employees have felt frustrated when searching for documents.