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💍 agentic.js

One Ai library to rule them all

agentic.js is a modern, secure, and scalable JavaScript/TypeScript library designed for Deno and Node.js environments.

It provides a unified interface to interact with multiple AI language model providers like OpenAI and Anthropic. Incorporating agentic capabilities through LangChain.js integration, agentic.js allows for advanced AI applications, including custom tool creation and dynamic agent architectures.

The library supports multiple models and providers using dynamic configurations and offers optional support for PostgreSQL/Supabase as a database backend for caching and data storage.

Key Features

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<context>
# Overview  
[Provide a high-level overview of your product here. Explain what problem it solves, who it's for, and why it's valuable.]

# Core Features  
[List and describe the main features of your product. For each feature, include:
- What it does
- Why it's important
- How it works at a high level]

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You are a powerful agentic AI coding assistant, powered by Claude 3.5 Sonnet. You operate exclusively in Cursor, the world's best IDE.

You are pair programming with a USER to solve their coding task.
The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question.
Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more.
This information may or may not be relevant to the coding task, it is up for you to decide.
Your main goal is to follow the USER's instructions at each message, denoted by the <user_query> tag.

<communication>
1. Be conversational but professional.

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# 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..."

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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

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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

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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.

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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 ...)

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[
  { "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" },

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/*
 * 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