Search results

🧠 Step 1: Install Ollama (One-Time Setup) Ollama lets you run LLMs locally with ease. Go to Ollama’s download page Download the installer for your OS (Windows/macOS) Install and open the Ollama app In the Ollama terminal, pull a model: ollama pull llama3 This downloads the LLaMA 3 model...

🧰 Step 1: PyCharm Project Setup Create a new PyCharm project (e.g., waifu_chatbot). Add two files: main.py requirements.txt In requirements.txt, add: fastapi requests PyCharm will prompt you to install these — go ahead and accept. 🧠 Step 2: Install Ollama (One-Time Setup) Download...

🧠 Step 1: Choose Your LLM Pick a model that suits your hardware and goals. Popular choices: LLaMA 3 (Meta) Mistral Gemma GPT4All These models are available in GGUF format and optimized for local use. ⚙️ Step 2: Install Ollama Ollama is a user-friendly tool to run LLMs locally. curl...

🧰 Step-by-Step: Direct MCP Tool Call with String Output 1. Install the MCP SDK pip install "mcp[cli]" httpx 2. Create Your Config File Save this as zapier_mcp.json: { "mcpServers": { "zapier": { "remote": "https://actions.zapier.com/mcp/YOUR_UNIQUE_ID" } } } Replace...

https://mcp.so/servers

Here’s a clean Python walkthrough to connect your AI agent to Zapier MCP—no spaghetti, just the essentials. This setup lets your agent call real-world actions like checking rain alerts, sending emails, or posting to Slack using natural language. 🧰 Prerequisites Python 3.8+ Node.js (for...

the livingrimoire is an AGI software design pattern, that allows absorbing skills using 1 line of code per skill. another way to add skills to the AI, now is simply done by copy pasting skill files(.py) into the project's DLC directory. so any skill(image reco, speech reco, face reco...

private let loader = { brain.add_skill(DiSarcasm()) brain.add_skill(DiWisdom()) return 0 }() import Foundation let brain = Brain() .chained(DiHelloWorld()) .chained(DiTime()) .chained(DiSysOut()) // DLC personality injection _ = DLC_Personality.loader let brainQueue...

import re def extract_dollar_amount(text: str) -> int: # Use a regex to find a number before the word "dollars" match = re.search(r'sends\s+(\d+)\s+dollars', text, re.IGNORECASE) if match: amount = int(match.group(1)) return amount if amount > 0 else -1 return -1...

class YogaFlowHero(Skill): def __init__(self): super().__init__() self.set_skill_type(3) # burst mode skill self.mode: bool = True self.r2 = Responder( "Take a deep breath", "Child's Pose", "Seated forward fold", "Lie on your back"...