LEAKED SYSTEM PROMPTS FOR CHATGPT, GEMINI, GROK, CLAUDE, PERPLEXITY, CURSOR, DEVIN, REPLIT, AND MORE! - AI SYSTEMS TRANSPARENCY FOR ALL!
Trigger.dev is the platform for building AI workflows in TypeScript. Long-running tasks with retries, queues, observability, and elastic scaling.
Build production-ready AI agents with tool calling, automatic retries, and full observability. Use existing Node.js SDKs and code from your repo.
This repository focuses on high-performance prompts sourced from X (Twitter), Reddit, and top prompt engineers. Whether you are looking for Gemini coding prompts, UI/UX design generation, or creative experiments, you will find the most effective inputs here to unlock the full potential of the 1M+ token context window.
L'instruction système suivante est un exemple qui a été évalué par des chercheurs pour améliorer les performances sur les benchmarks agentiques où le modèle doit respecter un ensemble de règles complexes et interagir avec un utilisateur. Il encourage l'agent à agir en tant que planificateur et raisonneur efficace, applique des comportements spécifiques aux dimensions listées ci-dessus et exige du modèle qu'il planifie de manière proactive avant d'entreprendre toute action.
Vous pouvez adapter ce modèle pour qu'il corresponde aux contraintes de votre cas d'utilisation spécifique
Le System Prompt (ce qui donne le contexte d'un agent IA) de Claude 4 a fuité. Si vous voulez voir ce qui se passe sous le capot
FULL v0, Cursor, Manus, Same.dev, Lovable, Devin, Replit Agent, Windsurf Agent & VSCode Agent (And other Open Sourced) System Prompts, Tools & AI Models
A curated list of awesome .cursorrules files for enhancing your Cursor AI experience.
Cursor AI is an AI-powered code editor. .cursorrules files define custom rules for Cursor AI to follow when generating code, allowing you to tailor its behavior to your specific needs and preferences.
This guide shares strategies and tactics for getting better results from large language models (sometimes referred to as GPT models) like GPT-4. The methods described here can sometimes be deployed in combination for greater effect. We encourage experimentation to find the methods that work best for you.
Some of the examples demonstrated here currently work only with our most capable model, gpt-4. In general, if you find that a model fails at a task and a more capable model is available, it's often worth trying again with the more capable model.
You can also explore example prompts which showcase what our models
GPT best practices
This guide shares strategies and tactics for getting better results from GPTs. The methods described here can sometimes be deployed in combination for greater effect. We encourage experimentation to find the methods that work best for you.
Some of the examples demonstrated here currently work only with our most capable model, gpt-4. If you don't yet have access to gpt-4 consider joining the waitlist. In general, if you find that a GPT model fails at a task and a more capable model is available, it's often worth trying again with the more capable model.
Find top prompts, produce better results, save on API costs, sell your own prompts.