Gino onigetoc

Introducing the Model Context Protocol

Today, we're open-sourcing the Model Context Protocol (MCP), a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments. Its aim is to help frontier models produce better, more relevant responses.

As AI assistants gain mainstream adoption, the industry has invested heavily in model capabilities, achieving rapid advances in reasoning and quality. Yet even the most sophisticated models are constrained by their isolation from data—trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale.

MCP addresses this challenge. It provides a universal, open standard for connecting

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

Core Principles

EXPLORATION OVER CONCLUSION

Never rush to conclusions
Keep exploring until a solution emerges naturally from the evidence
If uncertain, continue reasoning indefinitely
Question every assumption and inference

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.

Prompt Chaining with QwQ, Qwen, o1-mini, Ollama, and LLM

Here we explore prompt chaining with local reasoning models in combination with base models. With shockingly powerful local models like QwQ and Qwen, we can build some powerful prompt chains that let us tap into their capabilities in a immediately useful, local, private, AND free way.

Explore the idea of building prompt chains where the first is a powerful reasoning model that generates a response, and then use a base model to extract the response.

Play with the prompts and models to see what works best for your use cases. Use the o1 series to see how qwq compares.

Setup

Bun (to run bun run chain.ts ...)

	# from reddit: https://www.reddit.com/r/ClaudeAI/comments/1hciaxk/solved_installing_mcp_servers_on_windows_with/
	param(
	[Parameter(Mandatory=$true)]
	[string]$PackageName
	)

	Write-Host "Starting installation process for $PackageName..."

	# Check Claude Desktop folder
	Write-Host "Checking Claude Desktop installation..."

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	{
	"AF": {
	"Country Name": "Afghanistan",
	"ISO2": "AF",
	"ISO3": "AFG",
	"Top Level Domain": "af",
	"FIPS": "AF",
	"ISO Numeric": "004",
	"GeoNameID": 1149361,
	"E164": 93,

	[
	{ "code": "aa", "name": "Afar" },
	{ "code": "ab", "name": "Abkhazian" },
	{ "code": "ae", "name": "Avestan" },
	{ "code": "af", "name": "Afrikaans" },
	{ "code": "ak", "name": "Akan" },
	{ "code": "am", "name": "Amharic" },
	{ "code": "an", "name": "Aragonese" },
	{ "code": "ar", "name": "Arabic" },
	{ "code": "as", "name": "Assamese" },

	/*
	* twitter-entities.js
	* This function converts a tweet with "entity" metadata
	* from plain text to linkified HTML.
	*
	* See the documentation here: http://dev.twitter.com/pages/tweet_entities
	* Basically, add ?include_entities=true to your timeline call
	*
	* Copyright 2010, Wade Simmons
	* Licensed under the MIT license

	You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

	## Core Principles

	1. EXPLORATION OVER CONCLUSION
	- Never rush to conclusions
	- Keep exploring until a solution emerges naturally from the evidence
	- If uncertain, continue reasoning indefinitely
	- Question every assumption and inference