import anthropic, base64

client = anthropic.Anthropic()
with open("image.png", "rb") as f:
    b64 = base64.standard_b64encode(f.read()).decode("utf-8")

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": [
        {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": b64}},
        {"type": "text", "text": "Describe this image"}
    ]}]
)

# 3-step pipeline adds 2-5x latency vs native speech-to-speech
text = transcribe(audio)          # STT: ~500ms
response = llm.generate(text)     # LLM: ~1000ms
audio = text_to_speech(response)  # TTS: ~500ms
# Total: ~2000ms minimum

# Grok Voice Agent: <1s time-to-first-audio (5x faster)
async with websockets.connect("wss://api.x.ai/v1/realtime", extra_headers=headers) as ws:
    await ws.send(json.dumps({
        "type": "session.update",
        "session": {
            "model": "grok-4-voice",
            "voice": "Aria",
            "input_audio_format": "pcm16",
            "output_audio_format": "pcm16",
            "turn_detection": {"type": "server_vad"}
        }
    }))
    # Direct audio in -> audio out, no intermediary transcription

async with model.connect(config=config) as session:
    # Supports barge-in (user can interrupt anytime)
    # Affective dialog (understands and responds to emotions)
    # Proactive audio (responds only when relevant)
    async for response in session.receive():
        if response.data:
            yield response.data  # Audio bytes

# Whisper-1 is deprecated — use GPT-4o-Transcribe for better accuracy
response = client.audio.transcriptions.create(model="whisper-1", file=audio_file)

from openai import OpenAI
client = OpenAI()

with open(audio_path, "rb") as audio_file:
    response = client.audio.transcriptions.create(
        model="gpt-4o-transcribe",
        file=audio_file,
        language="en",  # Optional: improves accuracy for known language
        response_format="verbose_json",
        timestamp_granularities=["word", "segment"]
    )

result = {
    "text": response.text,
    "words": response.words,        # Word-level timestamps
    "segments": response.segments,  # Segment-level with speaker info
    "duration": response.duration
}

import google.generativeai as genai
model = genai.GenerativeModel("gemini-2.5-pro")

audio_file = genai.upload_file(audio_path)
response = model.generate_content([
    audio_file,
    """Transcribe with:
    1. Speaker labels (Speaker 1, Speaker 2)
    2. Timestamps: [HH:MM:SS]
    3. Punctuation and formatting"""
])

import assemblyai as aai
aai.settings.api_key = "YOUR_API_KEY"

config = aai.TranscriptionConfig(
    speaker_labels=True,
    sentiment_analysis=True,
    entity_detection=True,
    auto_highlights=True,
    language_detection=True
)
transcript = aai.Transcriber().transcribe(audio_url, config=config)

# Default voice with no style control — sounds robotic
response = model.generate_content(contents=text)

import google.generativeai as genai

def text_to_speech(text: str, voice: str = "Kore") -> bytes:
    """Gemini 2.5 Flash TTS with voice selection.

    Available voices: Puck, Charon, Kore, Fenrir, Aoede (30 total)
    """
    model = genai.GenerativeModel("gemini-2.5-flash-tts")

    response = model.generate_content(
        contents=text,
        generation_config=genai.GenerationConfig(
            response_mime_type="audio/mp3",
            speech_config=genai.SpeechConfig(
                voice_config=genai.VoiceConfig(
                    prebuilt_voice_config=genai.PrebuiltVoiceConfig(
                        voice_name=voice
                    )
                )
            )
        )
    )
    return response.audio

# Supports: [whisper], [sigh], [laugh], [pause]
await ws.send(json.dumps({
    "type": "response.create",
    "response": {
        "instructions": "[sigh] Let me think about that... [pause] Here's what I found."
    }
}))

config = live.LiveConnectConfig(
    response_modalities=["AUDIO"],
    speech_config=live.SpeechConfig(
        voice_config=live.VoiceConfig(
            prebuilt_voice_config=live.PrebuiltVoiceConfig(
                voice_name="Puck"  # 30 HD voices in 24 languages
            )
        )
    ),
    system_instruction="Speak warmly and naturally."
)

# Wrong: Sora is expensive and slow for volume work
# $1+ per video, 300-600s generation time
result = openai.videos.generate(
    model="sora-2",
    prompt="Product showcase with character",
    duration=10,
)
# Better: Use Kling 3.0 Standard — $0.20/video, 60-90s, character elements

# Social/ads volume → Kling 3.0 (cheap, fast, character consistent)
# Narrative film → Sora 2 (realism, cause-and-effect, 60s duration)
# Cinematic B-roll → Veo 3.1 (camera control, polished motion)
# Professional VFX → Runway Gen-4.5 (Act-Two motion transfer)
# Self-hosted/private → Wan 2.6 or LTX-2 (open-source)

# WRONG: Video generation takes 60-300s, this will timeout
response = requests.post(url, json={"prompt": "..."}, timeout=30)
video_url = response.json()["video_url"]  # Not available yet!

import requests
import time

API_KEY = "your-api-key"
BASE_URL = "https://api.klingai.com/v1"

# 1. Submit generation task
response = requests.post(
    f"{BASE_URL}/videos/text2video",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "model": "kling-v3.0",
        "prompt": "A chef plating a dish in a modern kitchen, warm lighting",
        "duration": "5",
        "aspect_ratio": "16:9",
        "mode": "std",  # std | pro
    }
)
task_id = response.json()["data"]["task_id"]

# 2. Poll for completion (60-90s typical for Kling)
while True:
    status = requests.get(
        f"{BASE_URL}/videos/text2video/{task_id}",
        headers={"Authorization": f"Bearer {API_KEY}"},
    ).json()
    if status["data"]["status"] == "completed":
        video_url = status["data"]["video_url"]
        break
    elif status["data"]["status"] == "failed":
        raise RuntimeError(f"Generation failed: {status['data']['error']}")
    time.sleep(10)

import fal_client

# Kling V3 text-to-video via fal.ai
result = fal_client.run(
    "fal-ai/kling-video/v3/pro/text-to-video",
    arguments={
        "prompt": "A knight in weathered armor, cinematic lighting",
        "duration": "10",
        "aspect_ratio": "16:9",
    }
)
video_url = result["video"]["url"]

import { klingai, type KlingAIVideoModelOptions } from '@ai-sdk/klingai';
import { experimental_generateVideo as generateVideo } from 'ai';

// Text-to-video
const { videos } = await generateVideo({
  model: klingai.video('kling-v3.0-t2v'),
  prompt: 'A cat playing in autumn leaves, warm afternoon light',
  aspectRatio: '16:9',
  duration: 5,
  providerOptions: {
    klingai: { mode: 'std' } satisfies KlingAIVideoModelOptions,
  },
});

// Image-to-video with start + end frame
const { videos: i2v } = await generateVideo({
  model: klingai.video('kling-v3.0-i2v'),
  prompt: { image: startFrameUrl, text: 'The subject turns and smiles' },
  duration: 5,
  providerOptions: {
    klingai: {
      mode: 'pro',        // Pro required for end-frame
      imageTail: endFrameUrl,
    } satisfies KlingAIVideoModelOptions,
  },
});

# Register a webhook URL when creating the task
response = requests.post(
    f"{BASE_URL}/videos/text2video",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "prompt": "...",
        "callback_url": "https://your-app.com/api/video-webhook",
    }
)
# Your webhook receives POST with task_id + video_url on completion

# WRONG: Each generation creates a different-looking character
clip1 = generate_video("A woman walks into a café")
clip2 = generate_video("A woman orders coffee at a counter")
clip3 = generate_video("A woman sits down and opens a laptop")
# Result: 3 different women, inconsistent clothing, hair, face

import { klingai, type KlingAIVideoModelOptions } from '@ai-sdk/klingai';
import { experimental_generateVideo as generateVideo } from 'ai';

const { videos } = await generateVideo({
  model: klingai.video('kling-v3.0-t2v'),
  prompt: 'A woman walks into a café, orders coffee, and sits down',
  providerOptions: {
    klingai: {
      shot_type: 'customize',
      // Multi-shot: each shot gets its own prompt and duration
      multi_prompt: [
        { prompt: '@Element1 walks into a bright café', duration: 5 },
        { prompt: '@Element1 orders coffee at the counter, smiling', duration: 5 },
        { prompt: '@Element1 sits by the window and opens a laptop', duration: 5 },
      ],
      // Character element: up to 3 reference images for identity lock
      elements: [{
        images: [
          'https://...frontal-face.jpg',   // Required: frontal view
          'https://...side-profile.jpg',    // Optional: side angle
          'https://...full-body.jpg',       // Optional: full body
        ],
        // Optional: assign a voice to this character
        voice_id: 'voice_abc123',
      }],
    } satisfies KlingAIVideoModelOptions,
  },
});

response = requests.post(
    f"{BASE_URL}/videos/text2video",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "model": "kling-v3.0",
        "mode": "pro",
        "shot_type": "customize",
        "multi_prompt": [
            {"prompt": "@Element1 enters a modern office", "duration": 5},
            {"prompt": "@Element1 presents to a group, gesturing at a screen", "duration": 5},
        ],
        "elements": [{
            "images": ["https://...frontal.jpg", "https://...profile.jpg"]
        }],
        "aspect_ratio": "16:9",
    }
)

# Exceeds context window or times out on 100+ page PDFs
response = model.generate_content([genai.upload_file("large_doc.pdf"), "Summarize this"])

# Claude Code Read tool with page ranges (max 20 pages per request)
Read(file_path="/path/to/document.pdf", pages="1-5")    # TOC/structure scan
Read(file_path="/path/to/document.pdf", pages="45-55")   # Target section
Read(file_path="/path/to/document.pdf", pages="80-90")   # Appendix

response = client.chat.completions.create(
    model="gpt-5.2",
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": (
                "Extract all data from this chart. Return as structured JSON with:\n"
                "1. Chart type (bar, line, pie, etc.)\n"
                "2. Axis labels and units\n"
                "3. All data points with values\n"
                "4. Title and legend entries"
            )},
            {"type": "image_url", "image_url": {
                "url": f"data:{mime_type};base64,{base64_data}",
                "detail": "high"  # Required for chart OCR accuracy
            }}
        ]
    }]
)

# OpenAI — image silently ignored
response = client.chat.completions.create(
    model="gpt-5.2",
    messages=[{"role": "user", "content": f"Describe this image: {base64_data}"}]
)

import base64, mimetypes

def encode_image(path: str) -> tuple[str, str]:
    mime_type = mimetypes.guess_type(path)[0] or "image/png"
    with open(path, "rb") as f:
        return base64.standard_b64encode(f.read()).decode("utf-8"), mime_type

# OpenAI (GPT-5, GPT-4o)
base64_data, mime_type = encode_image(image_path)
response = client.chat.completions.create(
    model="gpt-5.2",
    max_tokens=4096,  # Required for vision — omitting truncates response
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": prompt},
            {"type": "image_url", "image_url": {
                "url": f"data:{mime_type};base64,{base64_data}",
                "detail": "high"  # low=65 tokens, high=129+ tokens/tile
            }}
        ]
    }]
)

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": [
            {"type": "image", "source": {
                "type": "base64",
                "media_type": media_type,
                "data": base64_data
            }},
            {"type": "text", "text": prompt}
        ]
    }]
)

from PIL import Image
model = genai.GenerativeModel("gemini-2.5-pro")
image = Image.open(image_path)
response = model.generate_content([prompt, image])

content = []
for img_path in images:
    b64, mt = encode_image(img_path)
    content.append({"type": "image", "source": {"type": "base64", "media_type": mt, "data": b64}})
content.append({"type": "text", "text": "Compare these images..."})
response = client.messages.create(model="claude-opus-4-6", max_tokens=8192, messages=[{"role": "user", "content": content}])

response = model.generate_content([
    "Detect all objects. Return JSON: {objects: [{label, box: [x1,y1,x2,y2]}]}",
    image
])

# Wastes tokens: high detail + large model for yes/no
response = client.chat.completions.create(
    model="gpt-5.2",
    messages=[{"role": "user", "content": [
        {"type": "text", "text": "Is there a person? Reply: yes/no"},
        {"type": "image_url", "image_url": {"url": img_url, "detail": "high"}}
    ]}]
)

response = client.chat.completions.create(
    model="gpt-5.2-mini",  # Cheaper model
    messages=[{"role": "user", "content": [
        {"type": "text", "text": "Is there a person? Reply: yes/no"},
        {"type": "image_url", "image_url": {"url": img_url, "detail": "low"}}  # 65 tokens
    ]}]
)