machard

OpenStreaming: A Manifesto for Autonomous Value-Aware Mesh Networks

Table of Contents

1. Vision
2. Architecture Overview
3. Core Concepts
4. Network Node Architecture
5. Economic Model
6. EigenLayer Integration

machard

Exploit Disclosure: Q-STATE 
────────────────────────────────────────────────────────────────────
Name:     Q-STATE: Non-Symmetric Cost Collapse of Nakamoto Consensus 
Severity: A-tier / Critical 
Type:     Quantum-Narrative Exploit 
Status:   Unpatchable 
Disclosed: May 2025 
Author:   machard Facebook LinkedIn ChatGPT SnoopDog
────────────────────────────────────────────────────────────────────
→ Description:

machard

The Resonance Protocol — Implementation Overview

The Resonance Protocol is a decentralized, semantic communication system where each message — called a push — is filtered, relayed, and economically settled based on its capacity to resonate through intentional human interaction.

Core Components

1. Client Application (Browser-Native)

The frontend is a lightweight browser interface. It renders one push at a time — image and text — fetched from the user’s connected OpenStreaming node. Navigation is blocked until the user explicitly chooses to relay or block the push.

machard

import matplotlib.pyplot as plt
import numpy as np

# Sigmoid and its derivative
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def sigmoid_derivative(x):
    s = sigmoid(x)
    return s * (1 - s)

machard

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# Date range: centered around 2025
dates = pd.date_range(start="2016-01-01", end="2032-01-01", freq="M")
n = len(dates)
center_index = n // 2  # Peak at mid-2025

# Time variable: peak at center

machard

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d

# --- Parameters ---
k = 0.48
peak_value = 115000
t0 = 2025
A = peak_value * 4 / k  # Ensures derivative sigmoid peaks at 115K

machard

import numpy as np
import matplotlib.pyplot as plt

# Time vector
t = np.linspace(2000, 2045, 1000)

# Logistic growth parameters
L = 90  # asymptotic max (%)
k = 0.15  # growth rate
t0 = 2023  # inflection point

machard

import numpy as np
import matplotlib.pyplot as plt

# Time vector
t = np.linspace(2000, 2045, 1000)

# --- Total Population (in billions) ---
def total_population(t):
    return 6 + 2.2 / (1 + np.exp(-0.05 * (t - 2025)))  # asymptotes toward ~8.2B