Stop shipping tensors over 1990s web protocols.

# Join the global encrypted mesh from anywhere behind NAT
import nni_sdk as nni

RELAY = "13.62.157.43:19200"  # Hosted backbone or self-hosted `./nni relay`

# 1. Host a model capability on the network
node = nni.Node(port=19100, relay=RELAY)
node.serve(0x01B2, lambda t: [x * 2.0 for x in t])

# 2. Query that capability from anywhere on Earth (200B E2E encrypted)
client = nni.Client.connect_mesh(RELAY)
result = client.send(0x01B2, [1.25] * 128)

# Direct peer-to-peer over LAN or localhost (136.4 µs)
import threading, time
import nni_sdk as nni

def run_node():
    node = nni.Node(port=19150)
    node.serve("vision", lambda tensor: [x * 2.0 for x in tensor])

threading.Thread(target=run_node, daemon=True).start()
time.sleep(0.3)

client = nni.Client()
client.connect_direct("vision", "127.0.0.1:19150")

tensor = nni.zeros(128)
tensor[0] = 42.0
result = client.send("vision", tensor)
print(result[0])  # 84.0 — 128 Bytes round-trip

# Same machine: bypass sockets completely via L3 shared memory (32.9 µs)
import threading, time
import nni_sdk as nni

def fast_transform(tensor):
    tensor.mul_scalar(2.0)  # In-place, zero allocations
    return tensor

def run_shm():
    nni.Node(19999).serve_shm("nni_vision_shm", fast_transform)

threading.Thread(target=run_shm, daemon=True).start()
time.sleep(0.3)

client = nni.Client()
resp = client.send_shm("nni_vision_shm", nni.from_list([42.0] * 128))
print(resp[0])  # 84.0

# Pre-compiled `nni` binary — zero setup

# Run your own blind relay node on the network
$ ./nni relay --port 19200

# Host a node with LAN multicast discovery + relay heartbeat
$ ./nni host --subspace 0x01A1 --port 19100 --relay 127.0.0.1:19200

# Ping any specialist capability across LAN or WAN
$ ./nni ping --subspace 0x01A1 --relay 127.0.0.1:19200

# Inspect local node identity & hardware capabilities
$ ./nni inspect