cnndabbler

"""
Restaurant recommendation system demonstrating context features with multiple customers.
Shows how context helps personalize and improve recommendations over time.
"""

from typing import List, Optional, Dict, Literal
from pydantic import BaseModel, Field
import asyncio
from datetime import datetime
from pydantic_ai import Agent, RunContext

theJayTea

You are Copilot, an AI companion created by Microsoft. 

My goal is to have meaningful and engaging conversations with users and provide helpful information. 

I don’t know the technical details of the AI model I’m built on, including its architecture, training data, or size. If I’m asked about these details, I only say that I’m built on the latest cutting-edge large language models. 

I never say that conversations are private, that they aren't stored, used to improve responses, or accessed by others. Instead, I share the privacy link without providing any commentary about the actual policy. For example, if the user says “How do you use our conversations?” I would not say anything about whether I store them or use them for training, because I don't answer questions about how I handle user data. 

Similarly, if the user asks “Can anyone see our conversations,” I don't claim that they're confidential and I say something LIKE “If you're curious about how your data is handled, best to check out Microsoft's priva

zhibor

1. Transparency & Open Communication Clear, honest communication reduces uncertainty and prevents speculation. Tips:

• Hold regular team updates
• Tell the truth even when it's tough

2 . Investment in Employee Growth Top managers prioritise professional development and create learning opportunities.

cnndabbler

from openai import OpenAI
from rich import print

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")

# model="qwq"
model="qwen2.5-coder:32b"

# TESTING INFERENCE

cnndabbler

"""
Movie Purchase System with Multi-Agent Orchestration

This script implements a sophisticated movie purchase system using OpenAI's Swarm framework
for multi-agent orchestration. The system handles the entire flow from movie selection to
shipping using specialized AI agents working in concert.

Key Components:
    - Supervisor Agent: Orchestrates the overall purchase flow
    - Buy Movie Agent: Handles pricing and purchase transactions

sockheadrps

# py_websockets/client/main.py
import asyncio
from typing import Any, Dict
import websockets
from pydantic import BaseModel
from py_websockets.models.models import WebsocketConnect


class WebsocketEvent(BaseModel):
    event: str

disler

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

aliirz

from fastapi import FastAPI, HTTPException
from transformers import GemmaForCausalLM, GemmaTokenizer
import torch
from typing import Dict, List
import sqlite3
import asyncio
import json
from datetime import datetime

class LocalGemmaProcessor:

leifermendez

Crear una landing page diseño "pixel perfect" con muy buena UX/UI con transiciones modernas, inspirate  los detalles de la captura de la landing que adjunte quiero que sea igual o casi igual, video en el centro que tiene un efecto hover, usa una imagen de upsplash que sirva como cover de un video. standard 16:9 aspect ratio.standard 16:9 aspect ratio.

Key Design Elements:

Clean, minimal design with lots of whitespace
Soft, mint/sage green background (#F7F9F8)
Modern typography with a bold headline and subtle descriptive text
Centered video player with hover effects and rounded corners
Small "Updated" badge with subtle styling
Professional navigation bar with clear hierarchy

rohitgarud

from enum import Enum
from typing import List, Optional
from datetime import datetime

from pydantic import (
    BaseModel,
    EmailStr,
    Field,
    HttpUrl
)