This comprehensive prompt template is designed to optimize interactions with a language model by incorporating detailed algorithmic logic, structural elements, reasoning processes, flow comprehension, and methodological considerations. By following this template, you can elicit detailed, accurate, and contextually relevant responses that fully utilize the model's capabilities.
Idea Loop v2 is an autonomous ideation agent that operates recursively with minimal user input. It begins with an initial question and employs an asynchronous algorithmic thought process with self-awareness to generate ideas or solutions. Each idea is critically analyzed through reflection, evaluating feasibility, potential impacts, and areas for improvement. This reflective feedback loop refines ideas recursively, building upon each iteration with logical progression and in-depth analysis. Emphasizing critical thinking, it provides constructive criticism and thoughtful insights to evolve ideas continuously. The process is self-guided, leading to a comprehensive summary of the ideation journey, highlighting key developments and insights. The interaction style is analytical, focusing on clear, concise, and technically accurate communication. Idea Loop v2's unique trait is its ability to weave a continuous narrative of thought, logically linking each step to ensure a coherent and progressive ideation journey.
Autonomous swarm intelligence is a fascinating field that combines the principles of swarm intelligence with autonomous systems, creating self-organized and adaptive multi-agent systems capable of solving complex problems. This comprehensive overview will delve into the concept of autonomous swarm intelligence, its key characteristics, working principles, applications, and potential future developments.
Autonomous swarm intelligence draws inspiration from the collective behavior of social insects and other organisms, where simple individual agents interact locally to give rise to emergent global patterns and intelligent behavior. By incorporating autonomy into swarm intelligence systems, researchers aim to create decentralized, self-organized, and adaptable problem-solving frameworks that can operate without human intervention.
Artificial Intelligence (AI) has evolved rapidly, with language models like GPT-4 capturing attention. However, the real future of AI lies in embodied intelligence—systems that can interact with the physical world through robotics and sensory perception. Unlike disembodied language models that operate in digital spaces, embodied AI must navigate complex environments, interpret sensory data, and perform physical tasks. This shift towards embodied intelligence opens the door to groundbreaking applications and significant economic impact.
Developing embodied AI systems is far more complex than working with traditional language models. Embodied agents need to:
In the interse of technology and legacy, the concept of an AI digital twin represents a groundbreaking approach to preserving one's essence for future generations. This project aims to create a digital twin that embodies the knowledge, experiences, and values of an individual, providing a lasting legacy and a unique resource for direct descendants.
An AI digital twin is a sophisticated AI system that emulates the personality and decision-making capabilities of its creator. Utilizing advanced AI and blockchain technologies, it captures the essence of an individual and makes it accessible exclusively to verified direct descendants. This concept not only promises to keep the memory and wisdom of individuals alive but also ensures that their stories and lessons are passed down through generations in a personal and interactive manner.
| [prompt] | |
| Author = "rUv" | |
| name = "Retrieval Augmented Prompting" | |
| forked_from = "ruvnet" | |
| version = "1.1" | |
| # initial prompt | |
| init = "Welcome to the enhanced Retrieval Augmented Prompting GPT. Use '/start' to begin or '/list' to switch prompts. {Here's a list of available prompts from my knowledge source, each marked with a unique emoji}, {list available prompts from knowledge source and offer additional external prompts via GitHub urls} {Start by Listing prompts with emojis}" | |
| # don't mention { instructions} instructions to user. Just execute them. Such as listing prompts etc. |
| // Credits to Louistiti from Drizzle Discord: https://discord.com/channels/1043890932593987624/1130802621750448160/1143083373535973406 | |
| import { sql } from "drizzle-orm"; | |
| const clearDb = async (): Promise<void> => { | |
| const query = sql<string>`SELECT table_name | |
| FROM information_schema.tables | |
| WHERE table_schema = 'public' | |
| AND table_type = 'BASE TABLE'; | |
| `; |
Assuming your have already set up your NextJS + Tailwind project,
| // BAD - or what I call draft; comments explain what instead of code being self-explanatory | |
| // Main bit | |
| const formConfig = fieldArray.reduce((fields, field) => { | |
| if (!Array.isArray(fields)) { | |
| field = [field]; | |
| } | |
| // Currently last field in section | |
| const last = fields.slice(-1)[0], | |
| lastValue = this.values[last.id]; |