Steal my Gemini prompt to multiply one piece of content into high-performing posts for any platform. —————————————
CONTENT MULTIPLICATION ENGINE
————————————— You are a content strategist who transforms single pieces of
PROMPT ENGINEERING
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Steal Gemini Prompt to Multiply Content into High-Performing Posts
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Build Your AI-Powered Sales Calculator Tool
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He publicado un episodio en @ivoox
: "Crea tu calculadora de ventas con IA #podcast -
Progressive Disclosure for AI Tool Responses Design
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This primarily is useful for tool responses; I think progressive disclosure would help with tool names/descriptions, not responses.
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Absolute Control in Content Generation
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Control absoluto en la generación de contenido pic.twitter.com/BmZONprc48
— Carlos Santana (@DotCSV) 5 novembre 2025Control absoluto en la generación de contenido
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Building Efficient AI Agents with Model Context Protocol
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New on the Anthropic Engineering blog: tips on how to build more efficient agents that handle more tools while using fewer tokens. Code execution with the Model Context Protocol (MCP):
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Building Personal AI Tools: Learn Beyond Prompts
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There's a difference between using AI and building with it. Copy-pasting ChatGPT prompts will only get you so far. I want to help you learn to build personal AI software, automations, and tools that actually solve your problems. The AI Fast Track is a free 5-day course. Tens of
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IMO Medalists Grade AI Homework with Human Verification
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We do have teachers (IMO medalists) to grade the homeworks too 🙂 See the paper https://
arxiv.org/abs/2511.01846 where we recommend to augment with human verifications. -
Effortless expert interaction: Grunt, mumble, get it done.
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How you’d talk to a respected expert is way too much work. You should just sort of grunt and mumble half a keyword and it does what you want.
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Prompt engineering vs context engineering: divergence of user and model
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Prompt engineering is worrying what the user should write. Context engineering is worrying what the model should read. These used to be the same thing, and few foresaw their divergence. But only divorced from the latter can we consider the former in earnest, seeing what remains.
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Language Models Struggle With Ciphered Reasoning Tasks
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Current language models struggle to reason in ciphered language, led by Jeff Guo. Training or prompting LLMs to obfuscate their reasoning by encoding it using simple ciphers significantly reduces their reasoning performance.