Rapid AI Prototyping

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import os
from openai import OpenAI
from anthropic import Anthropic

# Initialize clients
openai_client = OpenAI(api_key=os.environ['OPENAI_API_KEY'])
anthropic_client = Anthropic(api_key=os.environ['ANTHROPIC_API_KEY'])

def generate_with_gpt4(prompt: str) -> str:
    """Generate text using GPT-4 Turbo"""
    response = openai_client.chat.completions.create(
        model="gpt-4-turbo-preview",

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// The simplest possible AI prototype structure
import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic();

// Your core prompt - this IS your product
const SYSTEM_PROMPT = `You are a product description expert.
Transform bullet points into compelling marketing copy.
Rules:
- Output exactly 150 words
- Use active voice
- Include one specific benefit

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// Full API route for a prompt-based MVP (Next.js/Vercel)
import { OpenAI } from 'openai';
import { Ratelimit } from '@upstash/ratelimit';
import { Redis } from '@upstash/redis';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const redis = new Redis({ url: process.env.UPSTASH_URL, token: process.env.UPSTASH_TOKEN });
const ratelimit = new Ratelimit({ redis, limiter: Ratelimit.slidingWindow(10, '1 h') });

export async function POST(req: Request) {
  const ip = req.headers.get('x-forwarded-for') ?? 'anonymous';
  const { success } = await ratelimit.limit(ip);

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from openai import OpenAI
from pydantic import BaseModel, Field, validator
from typing import List
import json

client = OpenAI()

class ActionItem(BaseModel):
    task: str = Field(..., min_length=10, max_length=200)
    priority: str = Field(..., pattern='^(high|medium|low)$')
    time_estimate: str = Field(..., pattern='^\d+\s*(min|hour|day)s?$')
    

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import { OpenAI } from 'openai';
import { RateLimitError, APIError, AuthenticationError } from 'openai';

const openai = new OpenAI();

interface AIResponse {
  success: boolean;
  data?: string;
  error?: string;
  retryAfter?: number;
}