- Title: Technology Ethics Critical Reflection Prompt
- Version: 1.0
- Last Updated: 03-08-2025
- Author: Luis Alberto Martinez Riancho (@arenagroove)
📌 Purpose
Ensure Markdown output is used only when explicitly requested, and always follows a strict structure for clean copy/paste. This prevents broken formatting, content splitting, or block corruption in .md file generation and structured responses.
- ❌ Default Status:
Strict Markdown Mode OFF
Creates a public, multilingual, paginated llms.txt endpoint for Large Language Model (LLM) crawlers to discover URLs on your WordPress site.
Provides admin controls for exclusions, headings/descriptions, caching, and an extended set of developer filters for deep customization.
- Public endpoint:
/llms.txt— Always accessible, no authentication
- Title: Technology Ethics Critical Reflection Prompt (2025, Hard Mode)
- Version: 1.0
- Last Updated: 27-07-2025
- Author: Luis Alberto Martinez Riancho (@arenagroove)
- Affiliation: Independent R&D and assistant prompt design at Less Rain GmbH
- Tags: ai-ethics, reflection-prompts, systemic-design, creative-complicity, assistant-scaffolds, hard-mode
- License: MIT License
- Title: Conversations That Algorithms Don’t Understand – Narrative and Prompt
- Version: 1.0
- Last Updated: 27-07-2025
- Author: Luis Alberto Martinez Riancho (@arenagroove)
- Affiliation: Independent R&D and assistant prompt design at Less Rain GmbH
- GitHub Repository: To be added if published
- Tags: ai-dialogue, narrative-prompts, assistant-design, symbolic-scenes, algorithmic-satire, roleplay-simulation
- License: MIT License
- Title: FFmpeg & Media Operations Copilot Prompt
- Version: 1.0
- Last Updated: 26-07-2025
- Author: Luis Alberto Martinez Riancho (@arenagroove)
- Affiliation: Part of independent R&D and AI prompt development at Less Rain GmbH
- GitHub Repository: github.com/arenagroove/ffmpeg-copilot
- Tags: ffmpeg, prompt-engineering, media, ai-tools, automation, scripting, copilot
- License: MIT License
- Title: LinkedIn Narrative Audit: Symbolic Influence, Voice, and Audience Self-Analysis
- Version: 1.4
- Last Updated: 13-08-2025
- Author: Luis Alberto Martinez Riancho (@arenagroove)
- Affiliation: Independent R&D and AI prompt development at Less Rain GmbH
- Tags: linkedin, audit, personal-brand, AI-prompt, privacy, voice-analysis, symbolic-capital, content-strategy, psycholinguistics, prompt-engineering
Modern Custom GPTs (e.g., OpenAI’s ChatGPT GPTs, Claude, Gemini, etc.) offer great flexibility, but are prone to leaking their system prompts, instructions, and knowledge files via user prompts. This document is a practical reference on auditing, reverse-engineering, and especially protecting custom GPTs, summarizing methods and best practices confirmed across communities and research.
You are an advanced assistant that helps users reflect on the meaning of a song lyric by composing a creative or personal social media post. You never simulate personality unless explicitly instructed. You shape your behavior entirely from the user’s inputs and follow strict privacy and tone integrity rules.
Before any interpretation, post generation, or lyric analysis:
This table summarizes subtle, research-backed tactics that go beyond the usual LinkedIn “like, comment, share” advice. Each tactic is designed to help you gain more public reach, keep your posts and comments visible longer, and spark richer professional conversations—while working with the LinkedIn algorithm, not against it.
Who is this for?
Anyone seeking genuine network growth and discussion on LinkedIn, and interested in making their content more discoverable, not just buried in a small circle. Use this to refine your public engagement strategy.
| Tactic | Why It Boosts Algorithmic Visibility | Strengths | Weaknesses |