Next-generation AI infrastructure for unified mesh communication, semantic compression protocols, and device-to-LLM networking. Deploy intelligent systems at the edge with zero friction.
# Initialize Unified Mesh Node
from mythicdot import UnifiedMesh
mesh = UnifiedMesh(port=9876)
# Register LLM endpoint
mesh.register_llm("claude", handler)
# Auto-discover peers
await mesh.discover()
# Sync state across devices
mesh.state.set("model", "quantum-v2")
# Query distributed LLM
response = await mesh.query_llm(
"Generate recursive architecture",
complexity=9
)
print(f"🔮 {response}")A complete toolkit for building, deploying, and scaling intelligent systems.
Single-file solution integrating file sync, network discovery, LLM communication, and CRDT state synchronization.
Generate complex architectures with configurable complexity levels. From simple scripts to enterprise systems.
Intent Token Protocol achieves 75-99% compression. Heartbeats shrink from 80 bytes to just 1.
Connect AI models across devices. Route inference requests, share context, and build distributed intelligence.
Run on Windows, Linux, macOS, Termux, and iSH. Zero external dependencies. Pure Python portability.
Designed for edge computing. Low latency, offline-capable, and mesh-networked for resilient operations.
Try the recursive generator and mesh protocol right in your browser.
Click "Generate Architecture" to begin...
{}
Intent Token Protocol Demo Token: F:p:f:data.json:#abc123 Wire: 88 bytes (75% savings) ♥ Heartbeat: 1 byte (99% savings) I:i:l Query: 12 bytes (94% savings)
Get up and running in minutes with our comprehensive documentation.
$ pip install mythicdot
$ mythicdot init --port 9876
$ mythicdot start
🔮 Mesh node active on 0.0.0.0:9876
🔗 Auto-discovery enabled on port 9877
✨ Ready to connect!Clean, intuitive interfaces for every use case.
from mythicdot import UnifiedMesh
# Create a mesh node
mesh = UnifiedMesh(
port=9876,
sync_path="~/mesh-sync",
auto_discover=True
)
# Start the node
await mesh.start()
# Connect to peer
await mesh.connect("192.168.1.42")
# List connected peers
peers = mesh.list_peers()
print(f"Connected to {len(peers)} peers")# Register local LLM handler
def my_handler(prompt, context):
return generate_response(prompt)
mesh.register_llm("local-model", my_handler)
# Query distributed LLM
response = await mesh.query_llm(
prompt="Generate a data pipeline",
model="local-model",
complexity=8
)
# Broadcast context to all LLMs
mesh.broadcast_context({
"project": "mythicdot",
"version": "2.0"
})# Set state (automatically synced)
mesh.state.set("config.model", "quantum-v2")
mesh.state.set("status", "active")
# Get state value
model = mesh.state.get("config.model")
# Sync with specific peer
await mesh.sync_state("peer-192.168.1.42")
# Watch for changes
@mesh.on_state_change("config.*")
def handle_change(key, value):
print(f"Config updated: {key} = {value}")# Push file to mesh
await mesh.push_file("model.onnx")
# Pull file from peer
await mesh.pull_file(
"config.json",
peer="peer-192.168.1.42"
)
# Sync entire directory
await mesh.sync_directory("./models")
# Get file hash (content-addressed)
hash = mesh.content_store.hash_file("data.json")
# => "sha256:abc123..."Start free, scale as you grow. All plans include core mesh functionality.
Perfect for individual developers and small projects
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Connect with developers building the future of AI infrastructure.
MythicDot.AI was born from the need for a unified solution to AI infrastructure. No more juggling separate tools for file sync, network discovery, and LLM communication.
Our recursive intelligence engine generates optimal architectures with configurable complexity, while the semantic compression protocol achieves up to 99% bandwidth savings.
Whether you're building edge AI applications, distributed LLM networks, or cross-platform development tools, MythicDot.AI provides the foundation you need.
Experience the full power of MythicDot.AI Pro. No credit card required.