Code snippets.

curl https://api.nan.builders/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer tu-api-key-personal" \
  -d '{
    "model": "qwen3.6",
    "messages": [{"role": "user", "content": "Hola, ¿cómo estás?"}],
    "max_tokens": 500
  }'

from openai import OpenAI

client = OpenAI(
  api_key="sk-tu-key-aqui",
  base_url="https://api.nan.builders/v1"
)

response = client.chat.completions.create(
  model="qwen3.6",
  messages=[{"role": "user", "content": "Escribe un hola mundo en Rust"}],
  max_tokens=500,
  stream=True
)

for chunk in response:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-tu-key-aqui",
  baseURL: "https://api.nan.builders/v1",
});

const stream = await client.chat.completions.create({
  model: "qwen3.6",
  messages: [{ role: "user", content: "Escribe un hola mundo en Zig" }],
  max_tokens: 500,
  stream: true,
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) process.stdout.write(content);
}

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "litellm": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "NaN",
      "options": {
        "baseURL": "https://api.nan.builders/v1",
        "apiKey": "sk-tu-key-aqui"
      },
      "models": {
        "qwen3.6": {
          "name": "Qwen 3.6",
          "modalities": {
            "input": ["text", "image"],
            "output": ["text"]
          }
        }
      }
    }
  }
}

curl https://api.nan.builders/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer tu-api-key-personal" \
  -d '{
    "model": "qwen3-embedding",
    "input": ["Hola mundo", "Hello world"]
  }'
# → 4096-dimensional vectors per input

from openai import OpenAI

client = OpenAI(
  api_key="sk-tu-key-aqui",
  base_url="https://api.nan.builders/v1"
)

response = client.embeddings.create(
  model="qwen3-embedding",
  input=["Kubernetes pod scheduling", "Programación de pods Kubernetes"]
)

embeddings = [d.embedding for d in response.data]
print(len(embeddings[0]))  # 4096

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-tu-key-aqui",
  baseURL: "https://api.nan.builders/v1",
});

const response = await client.embeddings.create({
  model: "qwen3-embedding",
  input: ["Hello world", "Hola mundo"],
});

const embeddings = response.data.map((d) => d.embedding);
console.log(embeddings[0].length);  // 4096

curl https://api.nan.builders/v1/audio/speech \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer tu-api-key-personal" \
  -d '{
    "model": "kokoro",
    "input": "Bienvenido a NaN builders.",
    "voice": "ef_dora"
  }' \
  -o speech.mp3

# Spanish female voice (ef_dora), English (af_heart), etc.
# Ver todas las voces: https://github.com/hexgrad/Kokoro-82M

from openai import OpenAI

client = OpenAI(
  api_key="sk-tu-key-aqui",
  base_url="https://api.nan.builders/v1"
)

response = client.audio.speech.create(
  model="kokoro",
  voice="ef_dora",
  input="Hola, bienvenido a NaN builders.",
  speed=1.0,
  response_format="mp3"
)

response.stream_to_file("output.mp3")

# English voice
response = client.audio.speech.create(
  model="kokoro",
  voice="af_heart",
  input="Welcome to NaN builders.",
  response_format="mp3"
)

import OpenAI from "openai";
import fs from "fs";

const client = new OpenAI({
  apiKey: "sk-tu-key-aqui",
  baseURL: "https://api.nan.builders/v1",
});

const response = await client.audio.speech.create({
  model: "kokoro",
  voice: "ef_dora",
  input: "Hola, bienvenido a NaN builders.",
  speed: 1.0,
  response_format: "mp3",
});

const buffer = Buffer.from(await response.arrayBuffer());
fs.writeFileSync("output.mp3", buffer);

# Transcribe audio file
curl https://api.nan.builders/v1/audio/transcriptions \
  -H "Authorization: Bearer tu-api-key-personal" \
  -F "model=whisper" \
  -F "file=@recording.mp3" \
  -F "language=es"

# → {"text":"Texto transcrito","language":"es","duration":5.2}

# Translate to English
curl https://api.nan.builders/v1/audio/translations \
  -H "Authorization: Bearer tu-api-key-personal" \
  -F "model=whisper" \
  -F "file=@grabacion.mp3"

from openai import OpenAI

client = OpenAI(
  api_key="sk-tu-key-aqui",
  base_url="https://api.nan.builders/v1"
)

# Transcribe Spanish audio
with open("grabacion.mp3", "rb") as f:
    result = client.audio.transcriptions.create(
        model="whisper",
        file=f,
        language="es",
        response_format="verbose_json"
    )

print(result.text)              # Transcribed text
print(result.language)          # "es"
print(result.duration)          # 5.2 (seconds)

# Translate to English
with open("grabacion.mp3", "rb") as f:
    translation = client.audio.translations.create(
        model="whisper",
        file=f
    )
print(translation.text)  # English translation

import OpenAI from "openai";
import fs from "fs";
import FormData from "form-data";

const client = new OpenAI({
  apiKey: "sk-tu-key-aqui",
  baseURL: "https://api.nan.builders/v1",
});

// Transcribe audio
const file = fs.createReadStream("grabacion.mp3");
const form = FormData();
form.append("file", file);

const result = await client.audio.transcriptions.create({
  model: "whisper",
  file,
  language: "es",
  response_format: "verbose_json",
});

console.log(result.text);       // Texto transcrito
console.log(result.language);   // "es"
console.log(result.duration);   // 5.2