Everyone's paying $15/month for Claude to write code. I just found a model that codes BETTER than Opus 4.6, runs 3x faster, and costs $1/hour for unlimited scaling. Self-hosted. Always-on agents. 10B activated parameters. The agent economy just became profitable:
@godofprompt
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AI Models Could Hide Information in Plain Sight
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This paper with Max Tegmark as a co-author just introduced a concept that should make every major AI lab slightly uncomfortable. It formalizes how large language models could hide information in plain sight. Not through obvious jailbreaks. Not through refusal bypasses. But
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Why I Use Claude Frequently
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Here's why I use Claude a lot: 1. It actually reads your entire document before answering, not just the first paragraph
2. Longest context window means I never hit "memory full" mid-project
3. Writes like a human, not like a press release
4. Pushes back when I'm wrong instead of -
Modern Prompting Techniques in 2026
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Most people are still writing prompts like it's 2023. "Be concise." "Think step by step." "You are an expert." The researchers who actually study this moved on. Structured scaffolds are what's in the internal docs now. Copy the format. Use it today. The gap between you and
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Structured Prompting for All LLMs
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Works on GPT, Claude, Gemini, Grok. Any model with strong instruction following. The scaffold doesn't rely on model-specific tricks. It exploits the one thing every frontier LLM has: the ability to follow structured procedures when forced to.
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Why ‘Think Step by Step’ Fails
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First, why "think step by step" fails. It tells the model to think. It doesn't tell the model how to think. You get surface-level reasoning dressed up as depth. Confident-sounding outputs with zero structural logic underneath.
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LLMs Wrongly Advise Walking to Car Wash
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This paper broke my brain Researchers gave Claude a simple question: “I want to wash my car. The car wash is 100 meters away. Should I walk or drive?” Claude said walk. Every major LLM said walk. The correct answer is drive. The car has to be there. Here’s the wild part:
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Creativity through constraint and surprise
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Let me tell why this works… Creativity = constraint + pressure + surprise. Generic prompts remove all three. This structure injects all three simultaneously. Here.. you are not asking the LLM to be creative, you're building a box so specific that the only way out is through
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Why ‘Be Creative’ Fails in AI
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First, understand WHY "be creative" fails. AI creativity is probabilistic. It defaults to the most statistically common answer. "Be creative" has no constraints.
No constraints = no creative pressure.
No pressure = average output. The fix isn't less structure. It's MORE of the -
Framework for AI Creativity
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Asking AI to "be creative" is the laziest prompt you can write. And it produces the laziest output. After 3 years of daily ChatGPT use, I cracked the structure that actually unlocks original, unexpected, usable creative work. Here's the exact framework