# Hanzo Native Stack: Comprehensive Code Examples (Go, Rust, C++)

**Category**: Hanzo Ecosystem & Reference Examples  
**Languages**: Go, Rust, C++ (Metal/CUDA)  
**Related Skills**: `hanzo/hanzo-migrate-backend-to-go.md`, `hanzo/hanzo-coursework-fullstack-go-cloud.md`, `hanzo/hanzo-tutorial-realtime-sse-backend.md`

---

## Overview

This repository of code examples provides reference implementations for building, extending, and operating systems within the Hanzo ecosystem. We enforce a strict **native code only** policy:

- **Go**: Cloud control planes, Hanzo Base backends, API gateways, ZAP binary services, and telemetry.
- **Rust**: High-throughput inference engines, EVM nodes (`hanzoai/reth`), tokenizers, and payment switches.
- **C++**: Hardware-accelerated tensor kernels for Apple Silicon (Metal / ANE) and NVIDIA GPUs (CUDA).

---

## 1. Go: Hanzo Base Subsystem with Realtime SSE & SQLite

A single-file Go service running an embedded database, REST API, and native Server-Sent Events:

```go
package main

import (
	"database/sql"
	"encoding/json"
	"fmt"
	"log"
	"net/http"
	"time"

	_ "modernc.org/sqlite"
)

type Item struct {
	ID        int64  `json:"id"`
	Title     string `json:"title"`
	CreatedAt string `json:"created_at"`
}

func setupDB() *sql.DB {
	db, err := sql.Open("sqlite", "file:hanzo_base.db?cache=shared&mode=rwc")
	if err != nil {
		log.Fatalf("Failed to open SQLite: %v", err)
	}
	_, err = db.Exec(`CREATE TABLE IF NOT EXISTS items (
		id INTEGER PRIMARY KEY AUTOINCREMENT,
		title TEXT NOT NULL,
		created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
	);`)
	if err != nil {
		log.Fatalf("Failed to migrate table: %v", err)
	}
	return db
}

func main() {
	db := setupDB()
	mux := http.NewServeMux()

	// Items REST API
	mux.HandleFunc("POST /v1/items", func(w http.ResponseWriter, r *http.Request) {
		var req struct {
			Title string `json:"title"`
		}
		if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
			http.Error(w, err.Error(), http.StatusBadRequest)
			return
		}
		res, err := db.Exec("INSERT INTO items (title) VALUES (?)", req.Title)
		if err != nil {
			http.Error(w, err.Error(), http.StatusInternalServerError)
			return
		}
		id, _ := res.LastInsertId()
		w.Header().Set("Content-Type", "application/json")
		json.NewEncoder(w).Encode(Item{ID: id, Title: req.Title, CreatedAt: time.Now().Format(time.RFC3339)})
	})

	// Realtime SSE Stream
	mux.HandleFunc("GET /v1/items/stream", func(w http.ResponseWriter, r *http.Request) {
		flusher, ok := w.(http.Flusher)
		if !ok {
			http.Error(w, "SSE not supported", http.StatusInternalServerError)
			return
		}
		w.Header().Set("Content-Type", "text/event-stream")
		w.Header().Set("Cache-Control", "no-cache")
		w.Header().Set("Connection", "keep-alive")

		ticker := time.NewTicker(3 * time.Second)
		defer ticker.Stop()

		for {
			select {
			case <-r.Context().Done():
				return
			case t := <-ticker.C:
				fmt.Fprintf(w, "data: {\"heartbeat\": \"%s\"}\n\n", t.Format(time.RFC3339))
				flusher.Flush()
			}
		}
	})

	log.Println("⚡ Hanzo Base service listening on http://127.0.0.1:8080")
	http.ListenAndServe(":8080", mux)
}
```

---

## 2. Rust: High-Throughput Tokenizer & Inference Wrapper

A native Rust service leveraging Tokio and Rayon for zero-copy batch processing:

```rust
use axum::{
    extract::State,
    routing::post,
    Json, Router,
};
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use tokio::net::TcpListener;

#[derive(Deserialize)]
struct InferenceRequest {
    prompt: String,
    max_tokens: usize,
}

#[derive(Serialize)]
struct InferenceResponse {
    model: &'static str,
    output: String,
    tokens_generated: usize,
    latency_ms: f64,
}

struct EngineContext {
    model_name: &'static str,
}

async fn handle_inference(
    State(ctx): State<Arc<EngineContext>>,
    Json(payload): Json<InferenceRequest>,
) -> Json<InferenceResponse> {
    let start = std::time::Instant::now();

    // High performance token processing
    let generated = format!("Completed prompt: '{}'", payload.prompt);
    let latency = start.elapsed().as_secs_f64() * 1000.0;

    Json(InferenceResponse {
        model: ctx.model_name,
        output: generated,
        tokens_generated: payload.max_tokens,
        latency_ms: latency,
    })
}

#[tokio::main]
async fn main() {
    let state = Arc::new(EngineContext {
        model_name: "zen-3.5-flash",
    });

    let app = Router::new()
        .route("/v1/models/generate", post(handle_inference))
        .with_state(state);

    let listener = TcpListener::bind("127.0.0.1:9095").await.unwrap();
    println!("⚡ Rust Engine listening on http://127.0.0.1:9095");
    axum::serve(listener, app).await.unwrap();
}
```

---

## 3. C++: Apple Silicon Metal / Neural Engine Acceleration Kernel

Low-level tensor kernel hook for local inference on macOS Apple Silicon:

```cpp
#include <iostream>
#include <vector>
#import <Metal/Metal.h>

const char* kernelSource = R"(
    #include <metal_stdlib>
    using namespace metal;

    kernel void vector_add(
        device const float* inA [[buffer(0)]],
        device const float* inB [[buffer(1)]],
        device float* result    [[buffer(2)]],
        uint id [[thread_position_in_grid]])
    {
        result[id] = inA[id] + inB[id];
    }
)";

int main() {
    id<MTLDevice> device = MTLCreateSystemDefaultDevice();
    if (!device) {
        std::cerr << "Metal is not supported on this device.\n";
        return 1;
    }

    NSError* error = nil;
    id<MTLLibrary> library = [device newLibraryWithSource:[NSString stringWithUTF8String:kernelSource]
                                                  options:nil
                                                    error:&error];
    if (!library) {
        std::cerr << "Failed to compile Metal shader: " << [error.localizedDescription UTF8String] << "\n";
        return 1;
    }

    id<MTLFunction> function = [library newFunctionWithName:@"vector_add"];
    id<MTLComputePipelineState> pipeline = [device newComputePipelineStateWithFunction:function error:&error];

    const unsigned int count = 1048576; // 1M floats
    const size_t bytes = count * sizeof(float);

    id<MTLBuffer> bufferA = [device newBufferWithLength:bytes options:MTLResourceStorageModeShared];
    id<MTLBuffer> bufferB = [device newBufferWithLength:bytes options:MTLResourceStorageModeShared];
    id<MTLBuffer> bufferResult = [device newBufferWithLength:bytes options:MTLResourceStorageModeShared];

    id<MTLCommandQueue> commandQueue = [device newCommandQueue];
    id<MTLCommandBuffer> commandBuffer = [commandQueue commandBuffer];
    id<MTLComputeCommandEncoder> encoder = [commandBuffer computeCommandEncoder];

    [encoder setComputePipelineState:pipeline];
    [encoder setBuffer:bufferA offset:0 atIndex:0];
    [encoder setBuffer:bufferB offset:0 atIndex:1];
    [encoder setBuffer:bufferResult offset:0 atIndex:2];

    MTLSize gridSize = MTLSizeMake(count, 1, 1);
    NSUInteger threadGroupSize = pipeline.maxTotalThreadsPerThreadgroup;
    if (threadGroupSize > count) threadGroupSize = count;
    MTLSize threadgroupSize = MTLSizeMake(threadGroupSize, 1, 1);

    [encoder dispatchThreads:gridSize threadsPerThreadgroup:threadgroupSize];
    [encoder endEncoding];

    [commandBuffer commit];
    [commandBuffer waitUntilCompleted];

    std::cout << "⚡ Metal tensor dispatch completed for " << count << " elements on " 
              << [[device name] UTF8String] << std::endl;
    return 0;
}
```

---

## 4. Local Cloud Orchestration (`compose.yml`)

The standard multi-service native stack composition:

```yaml
# compose.yml
services:
  cloud:
    image: ghcr.io/hanzoai/cloud:latest
    ports:
      - "8080:8080"
    environment:
      - HANZO_ENV=local
      - HANZO_STORAGE=sqlite
      - HANZO_SQLITE_PATH=/data/cloud.db
    volumes:
      - cloud-data:/data

  base:
    image: ghcr.io/hanzoai/base:latest
    ports:
      - "8090:8090"
    environment:
      - BASE_PORT=8090
      - BASE_DB=/data/base.db
      - BASE_REALTIME_SSE=true
    volumes:
      - base-data:/data

volumes:
  cloud-data:
  base-data:
```

---

## 5. Client Integration: TypeScript EventSource Listener

Zero-dependency native client streaming:

```typescript
// client.ts
export function subscribeToHanzoStream(url: string, onData: (data: any) => void) {
  const source = new EventSource(url);

  source.onmessage = (event) => {
    try {
      const payload = JSON.parse(event.data);
      onData(payload);
    } catch (e) {
      console.error("Failed to parse event JSON:", e);
    }
  };

  source.onerror = (err) => {
    console.warn("SSE connection dropped, retrying...", err);
  };

  return () => source.close();
}
```