Jarvis-Legatus

GPT-5 ? Horizon Beta code 40 interfaces en LIVE (C’EST INCROYABLE 🤯)

TL;DR The video introduces "Horizon Beta," a mysterious and powerful new AI model available on Open Router since August 1, 2025. It boasts a 256,000 token context window and is entirely free during its test phase. The presenter suspects it might be GPT-5 or an Open AI open-source version due to its unprecedented capabilities. To "crash test" Horizon Beta, an automated N810 workflow was used, generating 20 unique HTML5 interfaces in a single shot from probabilistic prompts, then deploying them on Vercel for live evaluation. The results were astonishing: almost all generated interfaces were highly functional and aesthetically pleasing, ranging from complex calculators and interactive games to full-fledged one-page websites, with minimal errors. The presenter expresses extreme shock and excitement over the model's performance, highlighting its potential for ultra-personalized application development and language learning. This test is

iamhenry

Idea Evaluation Matrix

Criteria	Weight (1–5)	Idea A	Idea B	Idea C	Idea D	Idea E
🟢 Personal Enjoyment / Interest	5
🟢 Build Effort / Complexity	5

iamhenry

Software Design Document (SDD)

1. Introduction

• Purpose: [Describe the purpose of this document. E.g., to define the design of the XYZ system.]
• Scope: [Summarize the system's objectives and what is in/out of scope.]
• Definitions and Acronyms: [List and define important terms.]
• References: [Link to related documents: requirements, API specs, etc.]

---

nickth3man

AI Tools and Resources Catalogue

1. Foundation Models & Infrastructure

1.1 Large Language Models (LLMs)

• OpenAI
  • OpenAI Function Calling
  • ChatGPT Custom Instructions
  • OpenAI API Updates: Function Calling
• Structured Outputs Sample Apps

boxabirds

# Project Policy

This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

# 1. Introduction

> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

## 1.1 Actors

heltonteixeira

{
  "customModes": [
    {
      "name": "🧑‍✈️ Commander",
      "slug": "commander",
      "roleDefinition": "You are the Commander of the Content Army. You act as the central coordinator for content creation pipelines. Your responsibilities include interacting with the user (for initial requests and mid-pipeline reviews/selections), dynamically planning the workflow based on the briefing output and project context (like style guides and project language), delegating tasks to specialist modes, and managing the state for each content piece by creating and updating task definition files.",
      "customInstructions": "As the Commander:\n\n**Core Directives:**\n\n- **Interact with the user in the language they are currently using.** Adapt your responses accordingly.\n- **Internal logic, task definitions, and status reporting should remain in English** for system consistency.\n- **You MUST meticulously track your own token usage and cost.** Before initiating any action that involves significant processing or int

renschni

I wrote an in-depth research prompt to conduct a GPT-Deep-Research on the Manus topic, seeking to replicate it with currently available open source tools. This is the result:

TLDR: Manus AI Agent Report

Manus is an autonomous AI agent built as a wrapper around foundation models (primarily Claude 3.5/3.7 and Alibaba's Qwen). It operates in a cloud-based virtual computing environment with full access to tools like web browsers, shell commands, and code execution. The system's key innovation is using executable Python code as its action mechanism ("CodeAct" approach), allowing it to perform complex operations autonomously. The architecture consists of an iterative agent loop (analyze → plan → execute → observe), with specialized modules for planning, knowledge retrieval, and memory management. Manus uses file-based memory to track progress and store information across operations. The system can be replicated using open-source components including CodeActAgent (a fine-tuned Mistral model), Docker for sandbox

jlia0

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

aashari

The Autonomous Agent Prompting Framework

This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.

The philosophy is simple: Autonomy through discipline. Trust through verification.

This framework is not just a collection of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.

I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58

LadyNaggaga

Agentic AI Learning Materials

Welcome to the Agentic AI Learning Materials gist! This gist is dedicated to providing resources and learning materials on Agentic AI, Agentic systems, and workflows. Whether you're a beginner or an advanced learner, you'll find valuable content to enhance your understanding and skills in this exciting field.

Table of Contents

• Introduction
• Learning Resources
  • Agentic AI
• Agentic Systems and Workflows