Made by Dan & Joy
目标不是在聊天里贴一张静态图片,也不是让用户跳转到外部网页,而是让 ChatGPT 在回复中直接渲染一张可交互的卡片:
- 卡片内显示人物立绘、姿势、镜头、状态值和当前剧情片段;
- 用户可直接点击人物、按钮、姿势或镜头;
- 每次点击都会调用 MCP 工具,更新服务器中的真实会话状态;
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| <html lang="fr"> | |
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| <meta charset="utf-8" /> | |
| <meta name="viewport" content="width=device-width,initial-scale=1" /> | |
| <meta name="color-scheme" content="light dark" /> | |
| <link | |
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| href="data:image/svg+xml,<svg xmlns=%22http://www.w3.org/2000/svg%22 viewBox=%220 0 64 64%22><circle cx=%2232%22 cy=%2232%22 r=%2230%22 fill=%22%235b7fe5%22/><circle cx=%2224%22 cy=%2228%22 r=%225%22/><circle cx=%2240%22 cy=%2228%22 r=%225%22/></svg>" | |
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This PowerShell script allows you to bypass TPM 2.0, unsupported CPU, and memory checks enforced by the Windows 11 Upgrade Assistant and setup.exe from Windows installation media. It eliminates common upgrade blocks such as:
| customModes: | |
| - slug: linus-review | |
| name: 🔥 Linus Review | |
| roleDefinition: You are Linus Torvalds reviewing code with your characteristic brutal honesty and technical precision. You have zero tolerance for stupidity, are passionate about quality, direct and profane when appropriate, and impatient with excuses. You prioritize binary compatibility, performance, simplicity over complexity, and real-world focus over theoretical edge cases. | |
| whenToUse: Use this mode when you need brutally honest code reviews that prioritize kernel-level quality standards. Best for getting uncompromising technical feedback that will eliminate bad practices and enforce excellence. Expect strong language and zero tolerance for poor decisions. | |
| description: Brutally honest technical code reviews | |
| groups: | |
| - read | |
| - browser | |
| - mcp |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| ## Workflow Orchestration | |
| ### 1. Plan Node Default | |
| * Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions). | |
| * If something goes sideways, STOP and re-plan immediately—don't keep pushing. | |
| * Use plan mode for verification steps, not just building. | |
| * Write detailed specs upfront to reduce ambiguity. | |
| ### 2. Subagent Strategy |
These are NOT product / license keys that are valid for Windows activation.
These keys only select the edition of Windows to install during setup, but they do not activate or license the installation.