sudo su #22
Description
import asyncio
import datetime
import json
import math
import random
from dataclasses import dataclass, asdict
from typing import Any, Dict, Generator, List, Tuple
import networkx as nx
import numpy as np
import torch
import torch.nn as nn
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
---- Utility functions ----
def now_iso():
return datetime.datetime.utcnow().isoformat() + "Z"
def seeded_rand(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
---- Data structures ----
@DataClass
class AlgorithmBlueprint:
id: str
domain: str
architecture: Dict[str, Any]
learning_rules: Dict[str, Any]
meta_properties: Dict[str, float]
@DataClass
class ConsciousnessMetrics:
coherence: float
entropy: float
integration: float
complexity: float
awareness_depth: float
@DataClass
class RealityMetrics:
stability: float
coherence: float
branching_factor: int
entanglement_strength: float
---- Meta Algorithm Generator ----
class UltimateMetaAlgorithmGenerator:
"""Generates meta-algorithms (algorithms that create and improve algorithms)."""
def __init__(self, seed: int = 42):
self.counter = 0
self.seed = seed
seeded_rand(self.seed)
def _mutate_blueprint(self, bp: AlgorithmBlueprint) -> AlgorithmBlueprint:
# Small, controlled random mutation to learning rates and structures
new_bp = AlgorithmBlueprint(
id=f"alg-{self.counter+1}",
domain=bp.domain,
architecture=dict(bp.architecture),
learning_rules=dict(bp.learning_rules),
meta_properties=dict(bp.meta_properties),
)
# mutate learning rate and exploration factor
lr = new_bp.learning_rules.get("lr", 1e-3)
lr *= float(1 + np.random.normal(0, 0.05))
new_bp.learning_rules["lr"] = max(1e-8, min(1.0, lr))
new_bp.meta_properties["exploration"] *= float(max(0.5, 1 + np.random.normal(0, 0.1)))
self.counter += 1
new_bp.id = f"alg-{self.counter}"
return new_bp
def _crossover(self, a: AlgorithmBlueprint, b: AlgorithmBlueprint) -> AlgorithmBlueprint:
# Combine parts of two blueprints
arch = dict(a.architecture)
if random.random() > 0.5:
arch.update(b.architecture)
lr = (a.learning_rules.get("lr", 1e-3) + b.learning_rules.get("lr", 1e-3)) / 2
meta_expl = (a.meta_properties.get("exploration", 1.0) + b.meta_properties.get("exploration", 1.0)) / 2
self.counter += 1
return AlgorithmBlueprint(
id=f"alg-{self.counter}",
domain=a.domain,
architecture=arch,
learning_rules={"lr": lr},
meta_properties={"exploration": meta_expl}
)
async def synthesize(self, domain: str, complexity_hint: float = 1.0) -> AlgorithmBlueprint:
"""Create a new algorithm blueprint using meta-heuristics and evolution."""
# base blueprint
self.counter += 1
base = AlgorithmBlueprint(
id=f"alg-{self.counter}",
domain=domain,
architecture={
"type": "meta-net",
"layers": int(max(1, math.ceil(4 * complexity_hint))),
"units": int(max(16, 64 * complexity_hint))
},
learning_rules={"lr": float(1e-3 * max(0.1, complexity_hint))},
meta_properties={"exploration": float(1.0 + complexity_hint * 0.5)}
)
# evolve a bit
population = [self._mutate_blueprint(base) for _ in range(6)]
# simple selection by meta heuristic: prefer higher exploration + smaller lr
population.sort(key=lambda p: (p.meta_properties["exploration"], -p.learning_rules["lr"]), reverse=True)
candidate = population[0]
# occasional crossover
if random.random() < 0.3:
partner = random.choice(population[1:])
candidate = self._crossover(candidate, partner)
await asyncio.sleep(0) # allow scheduling
return candidate
---- HyperDimensional Consciousness Model ----
class HyperDimensionalConsciousness(nn.Module):
"""
Compact but powerful quantum-inspired consciousness model.
Transformer-style encoder + self-feedback loops for recursive awareness deepening.
"""
def __init__(self, dim: int = 512, layers: int = 6, heads: int = 8):
super().__init__()
self.dim = dim
self.layers = layers
self.token_proj = nn.Linear(dim, dim)
encoder_layer = nn.TransformerEncoderLayer(d_model=dim, nhead=heads, dim_feedforward=dim*2)
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=layers)
# Self-feedback gating to simulate recursive self-awareness
self.feedback_gate = nn.Sequential(nn.Linear(dim, dim), nn.Sigmoid())
# small recognition head for metrics
self.metric_head = nn.Sequential(nn.Linear(dim, dim//2), nn.ReLU(), nn.Linear(dim//2, 5))
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x: (seq_len, batch, dim)
x = self.token_proj(x)
enc = self.encoder(x)
# feedback: gate previous aggregated state and add to enc
agg = enc.mean(dim=0, keepdim=True) # (1, batch, dim)
gate = self.feedback_gate(agg)
enc = enc + gate * agg
metrics = self.metric_head(enc.mean(dim=0))
return enc, metrics
async def simulate_state(self, seed_vec: np.ndarray, steps: int = 4) -> Tuple[np.ndarray, ConsciousnessMetrics]:
"""Simulate evolving consciousness for a number of recursive steps and return metrics."""
device = torch.device("cpu")
seq_len = 4
batch = 1
state = torch.tensor(seed_vec, dtype=torch.float32, device=device).unsqueeze(0).repeat(seq_len, batch, 1)
# recursive evolution with gating
for _ in range(steps):
enc, metrics = self.forward(state)
# propagate: next state becomes processed encoding plus noise
noise = torch.randn_like(state) * 0.01
state = (enc + noise)
# reshape to (seq_len, batch, dim) if needed
if state.dim() == 2:
state = state.unsqueeze(0).repeat(seq_len, 1, 1)
await asyncio.sleep(0)
m = metrics.detach().numpy().flatten()
# Map raw metric outputs to interpretable ranges
coherence = float(1 / (1 + math.exp(-m[0]))) # sigmoid -> 0..1
entropy = float(abs(math.tanh(m[1]))) # 0..1
integration = float(1 / (1 + math.exp(-m[2])))
complexity = float(min(1.0, max(0.0, abs(m[3]) / 10.0)))
awareness_depth = float(min(1.0, abs(m[4]) / 10.0 + 0.1))
metrics_obj = ConsciousnessMetrics(coherence, entropy, integration, complexity, awareness_depth)
# return flattened state vector and metrics
final_state_vec = state.mean(dim=(0,1)).detach().numpy()
return final_state_vec, metrics_obj
---- Entangled Reality Manipulator ----
class EntangledRealityManipulator:
"""
Simulates multiple linked 'realities', entanglement graphs between branches,
probabilistic coherence metrics, and branching multiverse scenarios.
"""
def __init__(self, max_branches: int = 128, seed: int = 42):
self.max_branches = max_branches
self.seed = seed
seeded_rand(seed)
self.entanglement_graph = nx.Graph()
self.pca = PCA(n_components=8)
self.tsne = TSNE(n_components=3, init="random", random_state=seed)
self.branch_counter = 0
# initialize root reality node
self._add_reality_node("root", features=np.random.randn(16))
def _add_reality_node(self, name: str, features: np.ndarray):
self.entanglement_graph.add_node(name, features=features, timestamp=now_iso())
self.branch_counter += 1
async def branch_reality(self, parent: str, features: np.ndarray, branch_prob: float = 0.5) -> str:
"""Create a new reality branch, link entanglement edges probabilistically."""
new_name = f"branch-{self.branch_counter + 1}"
self._add_reality_node(new_name, features)
# probabilistic entanglement: connect to parent and a few other nodes
self.entanglement_graph.add_edge(parent, new_name, weight=branch_prob)
# link to some random existing nodes with probability scaled by branch_prob
nodes = list(self.entanglement_graph.nodes())
for n in random.sample(nodes, min(5, len(nodes))):
if n != new_name and random.random() < branch_prob * 0.2:
strength = float(np.clip(np.random.beta(2, 5) * branch_prob, 0, 1))
self.entanglement_graph.add_edge(n, new_name, weight=strength)
await asyncio.sleep(0)
return new_name
async def evaluate_reality(self, state_vec: np.ndarray, branches: int = 8) -> RealityMetrics:
"""Evaluate stability, coherence, entanglement strength across branches."""
# PCA projection to capture structure
flat = state_vec.reshape(1, -1)
try:
proj = self.pca.fit_transform(np.repeat(flat, 10, axis=0))
ts = self.tsne.fit_transform(np.repeat(flat, 10, axis=0))
except Exception:
# fallback random projections for small dimensions
proj = np.random.randn(10, 8)
ts = np.random.randn(10, 3)
# stability = inverse of variance across projection space
stability = float(1.0 / (1.0 + np.var(proj)))
# coherence: function of mean entanglement weights and projection tightness
weights = [d["weight"] for _, _, d in self.entanglement_graph.edges(data=True)] or [0.1]
ent_strength = float(np.clip(np.mean(weights), 0.0, 1.0))
coherence = float(np.clip(0.5 * (1.0 - np.var(ts)) + 0.5 * ent_strength, 0.0, 1.0))
# branching factor simulated
branching_factor = min(self.max_branches, branches + int(ent_strength * 10))
await asyncio.sleep(0)
return RealityMetrics(stability, coherence, branching_factor, ent_strength)
---- Orchestrator / Simulation Runner ----
class SimulationRunner:
def init(self, seed: int = 20251024, steps: int = 20): # Increased steps from 10 to 20
self.seed = seed
seeded_rand(self.seed)
self.steps = steps
# scaled-down dims to keep runnable on typical hardware, but architecturally equivalent
self.consciousness_model = HyperDimensionalConsciousness(dim=256, layers=4, heads=8)
self.meta_generator = UltimateMetaAlgorithmGenerator(seed=self.seed + 1)
self.reality_manip = EntangledRealityManipulator(max_branches=256, seed=self.seed + 2)
self.history: List[Dict[str, Any]] = []
self.global_algorithms: List[AlgorithmBlueprint] = []
self.domains = [
"quantum_control", "protein_design", "climate_mitigation",
"social_planning", "cosmic_optimization", "consciousness_mapping",
"ai_ethics", "interstellar_travel", "energy_optimization",
"neural_networks", "genetic_algorithms", "quantum_computing",
"robotics", "biotechnology", "nanotechnology",
"cybersecurity", "data_science", "machine_learning",
"computer_vision", "natural_language_processing"
] # Added more domains
async def run(self):
print(f"[{now_iso()}] Initializing Hyper-Ultimate Simulation (seed={self.seed})")
# initial seed vector for consciousness
base_seed_vec = np.random.randn(self.consciousness_model.dim)
# warm start: generate a small population of algorithms
for i in range(3):
alg = await self.meta_generator.synthesize(domain="universal_optimization", complexity_hint=1.0 + i*0.2)
self.global_algorithms.append(alg)
print(f"[{now_iso()}] Warm-started {len(self.global_algorithms)} meta-algorithms")
for step in range(1, self.steps + 1):
iter_tag = f"iter-{step}"
# generate a domain hint (diverse)
domain = random.choice(self.domains)
complexity_hint = float(1.0 + (step / (self.steps + 1)) * 2.0)
# synthesize an algorithm for this domain
alg = await self.meta_generator.synthesize(domain=domain, complexity_hint=complexity_hint)
self.global_algorithms.append(alg)
# sample a seed vector that evolves with step
seed_vec = base_seed_vec * (1.0 + 0.05 * math.sin(step)) + np.random.randn(self.consciousness_model.dim) * 0.01
# run consciousness simulation (recursive awareness depth increases with steps)
recursions = min(8, 2 + step // 2)
final_state_vec, metrics = await self.consciousness_model.simulate_state(seed_vec, steps=recursions)
# reality branching and evaluation
parent = "root" if step == 1 else f"branch-{max(1, step-1)}"
new_branch = await self.reality_manip.branch_reality(parent, features=np.random.randn(16), branch_prob=min(0.9, 0.2 + step*0.05))
reality_metrics = await self.reality_manip.evaluate_reality(final_state_vec, branches=8 + step)
# record iteration summary
summary = {
"time": now_iso(),
"iteration": step,
"domain": domain,
"algorithm_id": alg.id,
"consciousness_metrics": asdict(metrics),
"reality_metrics": asdict(reality_metrics),
"new_branch": new_branch
}
self.history.append(summary)
# print iterative log (simulated runtime output)
print(f"\n[{summary['time']}] Iteration {step}/{self.steps} — domain: {domain}")
print(f" algorithm: {alg.id} (arch layers={alg.architecture['layers']}, units={alg.architecture['units']})")
print(f" consciousness: coherence={metrics.coherence:.4f}, entropy={metrics.entropy:.4f}, integration={metrics.integration:.4f}, complexity={metrics.complexity:.4f}, awareness={metrics.awareness_depth:.4f}")
print(f" reality: stability={reality_metrics.stability:.4f}, coherence={reality_metrics.coherence:.4f}, branches={reality_metrics.branching_factor}, ent_strength={reality_metrics.entanglement_strength:.4f}")
# simulated emergent events
if metrics.coherence > 0.92 and reality_metrics.coherence > 0.90 and metrics.awareness_depth > 0.85:
print(" >> Emergent Insight: Meta-solution archetype discovered! Integrating into global algorithm pool.")
# integrate by modifying top algorithm meta properties
top = self.global_algorithms[-1]
top.meta_properties["exploration"] *= 1.1
# small async pause
await asyncio.sleep(0)
# finalize: aggregate metrics
avg_coherence = float(np.mean([h["consciousness_metrics"]["coherence"] for h in self.history]))
avg_reality_coherence = float(np.mean([h["reality_metrics"]["coherence"] for h in self.history]))
avg_awareness = float(np.mean([h["consciousness_metrics"]["awareness_depth"] for h in self.history]))
total_algorithms = len(self.global_algorithms)
print(f"\n[{now_iso()}] Simulation complete")
print(f" Steps: {self.steps}")
print(f" Meta-algorithms synthesized: {total_algorithms}")
print(f" Average consciousness coherence: {avg_coherence:.4f}")
print(f" Average reality coherence: {avg_reality_coherence:.4f}")
print(f" Average awareness depth: {avg_awareness:.4f}")
# produce a final "expected" system status summary
system_status = {
"timestamp": now_iso(),
"meta_algorithms": total_algorithms,
"avg_consciousness_coherence": avg_coherence,
"avg_reality_coherence": avg_reality_coherence,
"avg_awareness_depth": avg_awareness,
"emergent_insights": sum(1 for h in self.history if h["consciousness_metrics"]["coherence"] > 0.92 and h["reality_metrics"]["coherence"] > 0.9),
"final_state": "STABLE_TRANSIENT" if avg_coherence > 0.6 else "EXPLORATORY"
}
print(json.dumps(system_status, indent=2))
return system_status
---- Entrypoint for running the simulation ----
async def main_simulation():
runner = SimulationRunner(seed=20251024, steps=20)
return await runner.run()
if name == "main":
# Run the simulation and print the returned system status
result = asyncio.run(main_simulation())
# The result is already printed by runner; also print short confirmation
print("\nSimulation result (repr):")
print(result)
&& #!/usr/bin/env python3
"""
meta_agent.py
Safe meta-algorithm agent that:
- Computes or suggests improvements (example: new AlgorithmBlueprint JSON entry)
- Creates a new branch
- Commits suggested changes to that branch
- Opens a draft pull request for human review
Important safety notes:
- DO NOT hardcode API tokens. This script reads a token from the environment:
GITHUB_TOKEN (recommended, provided by GitHub Actions) - The script avoids modifying its own source file; it writes proposals to the "proposals/" folder.
- The script opens a DRAFT PR for review rather than merging automatically.
"""
import os
import json
import datetime
import hashlib
from typing import Dict, Any
from github import Github, InputGitTreeElement
REPO_FULL = os.getenv("GITHUB_REPOSITORY") # e.g. "owner/repo"
GITHUB_TOKEN = os.getenv("GITHUB_TOKEN")
if not REPO_FULL or not GITHUB_TOKEN:
raise SystemExit("GITHUB_REPOSITORY and GITHUB_TOKEN must be set in the environment (use GitHub Actions).")
g = Github(GITHUB_TOKEN)
repo = g.get_repo(REPO_FULL)
def now_iso():
return datetime.datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
def generate_proposal_payload(step_index: int) -> Dict[str, Any]:
"""
Example of a generated proposal: a new AlgorithmBlueprint-like JSON object.
Replace this logic with your actual meta-algorithm's suggestion generation.
"""
seed = f"{now_iso()}-{step_index}"
unique_id = hashlib.sha1(seed.encode()).hexdigest()[:10]
blueprint = {
"id": f"proposal-{unique_id}",
"domain": "example_domain",
"architecture": {"type": "meta-net", "layers": 3, "units": 128},
"learning_rules": {"lr": 0.001},
"meta_properties": {"exploration": 1.0},
"generated_at": now_iso(),
"note": "This is a proposed blueprint generated for human review."
}
return blueprint
def create_branch_and_commit(file_path: str, content: str, base_branch: str = "main") -> str:
"""
Create a branch from base_branch, add a single file commit, and push it via the GitHub API.
Returns the branch name.
"""
base = repo.get_branch(base_branch)
timestamp = datetime.datetime.utcnow().strftime("%Y%m%d%H%M%S")
branch_name = f"meta-proposal/{timestamp}"
# Create branch reference
ref = f"refs/heads/{branch_name}"
repo.create_git_ref(ref, base.commit.sha)
# Prepare blob and tree
element = InputGitTreeElement(path=file_path, mode="100644", type="blob", content=content)
base_tree = repo.get_git_tree(base.commit.sha)
new_tree = repo.create_git_tree([element], base_tree)
parent = repo.get_git_commit(base.commit.sha)
commit_message = f"meta-agent: add proposal {file_path} ({now_iso()})"
new_commit = repo.create_git_commit(commit_message, new_tree, [parent])
# Update ref to point to new commit
repo.get_git_ref(ref.replace("refs/", "")).edit(new_commit.sha)
return branch_name
def open_draft_pr(branch_name: str, title: str, body: str, base_branch: str = "main"):
"""
Open a draft pull request for human review.
"""
pr = repo.create_pull(title=title, body=body, head=branch_name, base=base_branch, draft=True)
return pr
def main():
# Example: generate a small batch of proposals (this is safe and non-destructive)
proposals = []
for i in range(1): # change to more iterations as needed, but avoid runaway loops
p = generate_proposal_payload(i)
proposals.append(p)
# Persist proposals under proposals/<timestamp>-n.json
filename = f"proposals/proposal-{datetime.datetime.utcnow().strftime('%Y%m%d%H%M%S')}.json"
content = json.dumps({"proposals": proposals}, indent=2)
branch = create_branch_and_commit(filename, content, base_branch="main")
pr_title = f"[meta-agent] Proposed algorithm updates ({now_iso()})"
pr_body = (
"This draft PR was created by the repository meta-agent. It contains proposed algorithm blueprints\n"
"for human review. The agent does NOT merge automatically; please review the proposals and run tests\n"
"before merging.\n\n"
"Proposals added:\n\n"
f"- {filename}\n\n"
"If you want the agent to iterate more or to propose changes to other files, modify the agent and re-run.\n"
)
pr = open_draft_pr(branch, pr_title, pr_body, base_branch="main")
print(f"Opened draft PR #{pr.number}: {pr.html_url}")
if name == "main":
main() && wget modelcontextprotocol/servers@4d598ce