Every AI Platform.
One Open Stack.

# Universal AI Client (Python · Apache 2.0) # Works with: Ollama (local), Gemini, OpenAI, Groq, Together, Anthropic from openai import OpenAI # Switch provider by changing base_url only: PROVIDERS = { "local_llama": {"base_url": "http://localhost:11434/v1", "api_key": "ollama"}, "gemini": {"base_url": "https://generativelanguage.googleapis.com/v1beta/openai/", "api_key": "YOUR_GEMINI_KEY"}, "groq": {"base_url": "https://api.groq.com/openai/v1", "api_key": "YOUR_GROQ_KEY"}, "together": {"base_url": "https://api.together.xyz/v1", "api_key": "YOUR_TOGETHER_KEY"}, } def get_ai_client(provider="local_llama"): cfg = PROVIDERS[provider] return OpenAI(base_url=cfg["base_url"], api_key=cfg["api_key"]) # IoT data analysis with any AI provider def analyze_iot_data(sensor_data: dict, provider="local_llama"): client = get_ai_client(provider) response = client.chat.completions.create( model="llama3" if provider == "local_llama" else "gemini-2.0-flash", messages=[{ "role": "user", "content": f"Analyze this IoT sensor data and detect anomalies: {sensor_data}" }] ) return response.choices[0].message.content # Example: Analyze 100M rows/day IoT pipeline result = analyze_iot_data({"temp": 24.5, "humidity": 65, "co2": 412}, provider="local_llama") print(result)

# Neuromorphic Mesh Routing (Norse + PyTorch · Apache 2.0) import torch import norse.torch as snn class MeshRoutingSNN(torch.nn.Module): """Spiking Neural Network for IoT mesh routing decisions. 1000x more energy efficient than traditional neural networks. Runs on Raspberry Pi 5 at < 1W power consumption.""" def __init__(self, n_inputs=64, n_hidden=128, n_outputs=8): super().__init__() # Leaky Integrate-and-Fire neurons (biologically inspired) self.lif1 = snn.LIFRecurrent(n_inputs, n_hidden) self.lif2 = snn.LIFRecurrent(n_hidden, n_outputs) def forward(self, network_state: torch.Tensor): # Encode network state as spike trains spikes1, state1 = self.lif1(network_state) spikes2, state2 = self.lif2(spikes1) # Output: routing decision for 8 mesh directions return spikes2 # [N, E, S, W, UP, DOWN, RELAY, DROP] # Deploy on Network Core node model = MeshRoutingSNN() # Input: [signal_strength, latency, battery, traffic, ...] routing_decision = model(torch.randn(1, 64)) print(f"Route: {routing_decision.argmax().item()}")

# Install Ollama on Network Core Node (Raspberry Pi 5) curl -fsSL https://ollama.ai/install.sh | sh # Pull open-source models (no API key needed) ollama pull mistral # 4GB · Best for RPi5 ollama pull phi3 # 2GB · Ultra-lightweight ollama pull qwen2.5:7b # 4GB · 70+ languages # Start OpenAI-compatible server ollama serve & # Test: Analyze IoT sensor data locally curl http://localhost:11434/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "mistral", "messages": [{ "role": "user", "content": "Sensor: temp=38C, humidity=95%, CO2=2000ppm. Anomaly?" }] }'

# IoT AI Pipeline (Python · Apache 2.0) from kafka import KafkaConsumer from openai import OpenAI import psycopg2, json # Connect to local AI (Ollama) or any OpenAI-compatible API ai = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama") db = psycopg2.connect("postgresql://localhost/networkcore") # Consume IoT data from Kafka consumer = KafkaConsumer('iot-sensors', bootstrap_servers=['localhost:9092']) for message in consumer: sensor_data = json.loads(message.value) # AI anomaly detection on every IoT row response = ai.chat.completions.create( model="mistral", messages=[{"role": "user", "content": f"Anomaly score 0-10 for: {sensor_data}. Reply with JSON only."}] ) anomaly_score = json.loads(response.choices[0].message.content) # Store enriched data in TimescaleDB with db.cursor() as cur: cur.execute( "INSERT INTO iot_ai_enriched VALUES (%s, %s, %s)", (sensor_data['timestamp'], sensor_data, anomaly_score) ) db.commit()

# Multi-Language IoT AI (Qwen2.5 · Apache 2.0) from openai import OpenAI ai = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama") LANGUAGES = { "en": "English", "es": "Español", "zh": "中文", "hi": "हिन्दी", "ar": "العربية", "pt": "Português", "ru": "Русский", "ja": "日本語", "de": "Deutsch", "fr": "Français", "ko": "한국어", "sw": "Swahili", # ... all 70+ languages } def analyze_iot_multilingual(sensor_data: dict, language_code: str = "en"): lang_name = LANGUAGES.get(language_code, "English") response = ai.chat.completions.create( model="qwen2.5:7b", # Best multilingual open model messages=[{ "role": "user", "content": f"Respond in {lang_name}. Analyze IoT data: {sensor_data}" }] ) return response.choices[0].message.content # Works in all 70+ languages, all 195 countries print(analyze_iot_multilingual({"temp": 38}, "sw")) # Swahili print(analyze_iot_multilingual({"temp": 38}, "ar")) # Arabic