use gemini / claude / qwen. the only three real ai's.
@godofprompt
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Recursive prompting beats larger models
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I just read the Samsung TRM paper and it broke how I think about prompting. A 7M parameter model beat DeepSeek-R1, Gemini 2.5 Pro, and o3-mini at reasoning. Not by being bigger. By thinking recursively. Here's the meta-prompt that applies the same logic to any LLM:
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AI Resume + Portfolio Builder Tool
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And because I couldn’t stop there…
— God of Prompt (@godofprompt) 7 octobre 2025
I asked it for an AI Resume + Portfolio Builder.
It generated a professional, multi-page tool complete with parsing, design layouts, and exportable HTML. pic.twitter.com/INBLtUjlWUAnd because I couldn’t stop there… I asked it for an AI Resume + Portfolio Builder. It generated a professional, multi-page tool complete with parsing, design layouts, and exportable HTML.
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CREAO Builds Dynamic Couple Compatibility Quiz
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I asked CREAO to build a Couple Compatibility Quiz it generated the entire app: – witty results
– dynamic scoring
– personality logic All from one prompt. Apps aren’t static anymore they talk back now. -
OpenAI Dev Day: ChatGPT Apps SDK Launch
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Today’s @OpenAI Dev Day announcements – quick rundown:
— God of Prompt (@godofprompt) 6 octobre 2025
→ ChatGPT now supports apps inside conversations via a new Apps SDK—users and devs can invoke apps contextually mid-chat.
→ Launch partners include https://t.co/ru8cKKIkm2, Canva, Coursera, Figma, Spotify, Zillow. More… https://t.co/I5kjajei7mToday’s @OpenAI Dev Day announcements – quick rundown: → ChatGPT now supports apps inside conversations via a new Apps SDK—users and devs can invoke apps contextually mid-chat. → Launch partners include http://
Booking.com, Canva, Coursera, Figma, Spotify, Zillow. More -
Transformers Shape Language Patterns Like Marbles
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We say “Transformers predict the next word,” but really what’s happening is: They’re reshaping a whole field of relationships until the pattern feels stable – like a marble rolling down until it rests in the lowest valley. That “valley” is the lowest-entropy shape of language –
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Mastering Prompt Engineering Fundamentals
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The meta-lesson from reverse-engineering Anthropic's library:
Prompt engineering isn't about clever tricks. It's about clear communication of: WHO should respond (role)
WHAT they should do (task)
HOW they should do it (process)
WHAT format to use (structure)
WHAT to avoid -
Prefilled responses: underrated prompt technique
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Tenth pattern: they use prefilled assistant responses. This is the most underrated technique in the entire library. Instead of just sending a prompt, Anthropic starts Claude's response.
API structure: { "messages": [ {"role": "user", "content": "Analyze this contract"}, -
Error Handling in AI Prompts
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Ninth discovery: error handling is built into prompts. Anthropic anticipates edge cases and tells Claude how to handle them. If the input data is:
– Incomplete: State what's missing and make reasonable assumptions
– Contradictory: Identify the contradiction and -
Negative examples in prompt engineering
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Sixth technique: constraint specification using negative examples.
Don't just say what you want. Say what you don't want. Standard approach: Write a professional email. Anthropic's method: Write a professional email that:
– Is concise (under 150 words)
– Has a