voxels

Caesar Augustus

Location: New York, NY

Email: caesar.augustus.rodney@proton.me

GitHub: https://github.com/voxels

USPTO US-20230333809-A1.pdf

voxels

US Patent & Trademark Office
Patent Public Search | Text View
United States Patent Application Publication
20230333809
Kind Code
A1
Publication Date
October 19, 2023
Inventor(s)

voxels

SovereignSurvivalKit Architecture

Important

Version Control and State Tracking Rule: This repository exclusively uses lore for version control, state management, and documentation tracking. Do NOT use git.

The SovereignSurvivalKit is a modular Swift ecosystem designed for local intelligence, cryptographic vault management, hardware telemetry, and off-grid communication. The codebase is organized into independent Swift Packages located in src/SovereignSurvivalKit/Modules/, standalone tools and daemons in src/SovereignSurvivalKit/, and target extensions in SovereignSurvivalKit.xcodeproj.

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voxels

SovereignSurvivalKit

Important

Version Control Rule: This repository uses lore for version control, state tracking, and documentation. Do not use git.

---

1. Overview

SovereignSurvivalKit is a modular application platform for macOS and iOS. The platform provides:

voxels

from http.server import BaseHTTPRequestHandler, HTTPServer
from http.client import HTTPConnection
import time
import json
import subprocess
import sys
import threading

UPSTREAM_HOST = "127.0.0.1"
UPSTREAM_PORT = 1976

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from http.server import BaseHTTPRequestHandler, HTTPServer
from http.client import HTTPConnection
import time
import json

UPSTREAM_HOST = "127.0.0.1"
UPSTREAM_PORT = 1976

class Proxy(BaseHTTPRequestHandler):
    def do_GET(self):

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# Local AI Video Production Pipeline — Student Edition (May 2026)

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## Overview

This guide describes a fully offline, modular AI filmmaking pipeline split across two machines:

| Machine | Role |
|---|---|

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conda create -n qwen python=3.11 -y
conda activate qwen
pip install mlx-lm
python -m mlx_lm server --model unsloth/Qwen3.6-35B-A3B-UD-MLX-4bit --port 8000 --max-tokens 16384 --prompt-concurrency 2048 --decode-concurrency 1 --prompt-cache-bytes 45G --trust-remote-code

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        - conda activate qwen
        - python -m mlx_lm server \
        -   --model unsloth/Qwen3.6-35B-A3B-UD-MLX-4bit \
        -   --port 8000 \
        -   --max-tokens 8192 \
        -   --prompt-concurrency 4 \
        -   --decode-concurrency 1 \
        -   --prompt-cache-bytes 32G \
        -   --trust-remote-code

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Good — this is the right moment to reset. Your current env is in “dependency whack-a-mole mode,” so a clean stack will actually save time.

Below is a **known-stable Windows 11 + RTX 3080 Ti + OneTrainer SDXL LoRA setup** that avoids all the version traps you just hit (torch/torchvision/xformers/numpy/diffusers mismatches).

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# 🧨 CLEAN INSTALL SCRIPT (Windows 11 / SDXL / OneTrainer)

## ⚠️ What this does