Kat Morgan usrbinkat

ContainerCraft Dockerfile Style Guide

Version: 1.0.0
Last Updated: December 2024
Maintainer: ContainerCraft

AI CLI Installer v3.2.0

Bash script for installing AI-powered command-line development tools.

Quick Start

curl -fsSL https://gist.githubusercontent.com/usrbinkat/e36f12cd0d8c0f98decc80b092c447f4/raw/ai-tools.sh | bash

Technical Content Pipeline Architecture Design Document

Overview

This document outlines the technical architecture for processing a livestream multi-host & multi-guest podcast transcript into various marketing outputs for both social media and technical marketing purposes.

Input Source

Format: Raw text transcript from livestream podcast
Components: Speaker identifiers, timestamps, full dialogue content
Additional metadata: Episode title, participant names/titles, recording date, episode number

obelisk on  main [?] is 📦 v0.1.0 via 🐍 v3.9.6
❯ docker model run ai/qwen3
Interactive chat mode started. Type '/bye' to exit.
> tell me about your training corpus, knowledge scope, and strongest logic and skill qualities.
<think>
Okay, the user is asking about my training corpus, knowledge scope, and the strongest logic and skill qualities. Let me start by recalling the information I have.

First, the training corpus. details due to privacy. So I should mention that it's a diverse set of texts, including books, articles, etc., up to 2024, but not the exact sources.de exact

Mathematical Foundations of the UOR-Prime Framework: A Technical Primer

Introduction:
This primer presents a comprehensive, textbook-style exploration of the mathematical foundations underlying the Universal Object Reference (UOR) and Prime Framework as described in the attached paper. Our goal is to equip the reader with deep technical mastery of all prerequisite disciplines, from fundamental definitions to advanced concepts, in a self-contained manner. We cover the following major areas, each chosen for its relevance to the UOR-Prime Template:

Category Theory Fundamentals – including the language of objects, morphisms, functors, and terminal objects, which form the abstract backbone of the framework.
Universal Properties – general constructions (like terminal objects) that guarantee uniqueness and canonicality in mathematical structures.
Algebraic Structures – formal definitions and examples of groups, rings, fields, and algebras, including t

Model-Context-Protocol (MCP) for Agentic AI Workflows

The Model-Context-Protocol (MCP) is an open standard introduced by Anthropic in late 2024 to enable AI systems (like large language model agents) to seamlessly connect with external data sources and tools. This report provides a deep dive into MCP’s architecture and its role in agentic workflows – multi-step, tool-using AI “agents” that coordinate tasks. We will cover MCP’s core concepts, how to develop MCP-compliant agents (both client and server sides), strategies for orchestrating multiple MCP-based agents (coordination, conversation state management, and tool chaining), ensuring interoperability and schema compliance, and finally compare MCP’s approach to other leading agent frameworks (LangGraph, CrewAI, OpenDevin, AutoGen, etc.), evaluating compatibility, strengths, and limitations.

MCP Architecture and Core Concepts in Agentic Systems

MCP Overview: MCP is not a programming framework or a single toolchain – it is a protocol (a

Title: Toward a Deterministic, Semantic, and Dynamically Coherent LLM: Integrating Infomorphic Neurons, UOR Digest Encoding, and Hamiltonian Mechanics

Abstract

This paper introduces a unified theoretical and implementation framework for constructing advanced language learning models (LLMs) that transcend the limitations of token-based architectures. Integrating three frontier paradigms—(1) Infomorphic Neurons via Partial Information Decomposition (PID), (2) Universal Object Reference (UOR) with 512-bit Prime Digest Encoding, and (3) Hamiltonian Mechanics as a governing model of semantic trajectory dynamics—we propose a deterministic, reversible, and fully interpretable semantic engine. This triadic approach enables the construction of dynamic, on-the-fly evolving neural knowledge graphs with canonical semantic addressability, physically grounded coherence, and intrinsically lossless transformation.

Introduction

Language models have traditionally relied on probabilistic token prediction, which fragments

BasedOn:

	#!/usr/bin/env python3
	"""
	Pure UOR — execution + integrity + NTT spectral (lossless full-complex)
	=====================================================================
	This script implements:
	- A dynamic prime cache
	- Data and exec opcodes with per-chunk checksum (exp⁶)
	- Block framing via prime⁷ headers
	- Forward & inverse Number-Theoretic Transform (NTT) mod 13 as a spectral operator
	- Automatic inversion ensuring lossless round-trip

	{
	"mcpServers": {
	"puppeteer": {
	"command": "npx",
	"args": ["-y", "@modelcontextprotocol/server-puppeteer"],
	"env": {
	"PUPPETEER_LAUNCH_OPTIONS": "{\"headless\": \"new\", \"args\": [\"--no-sandbox\", \"--disable-setuid-sandbox\", \"--disable-dev-shm-usage\", \"--window-size=1280,720\"]}",
	"ALLOW_DANGEROUS": "true"
	}
	},