imagine you decided to use AI for deep research:> complete confusion, surface-level answers> chatgpt gives you a summary that sounds like wikipedia> you spend 3 hours verifying it across 50 open tabs> messy notes, no structure, no idea what to trust> then you find this system> 20 markdown files. 6 research lenses. one folder> you give claude one question> it rethinks it through 6 completely different angles> contradictions don't get buried, they become the insights> the system gets smarter every
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imagine you decided to use AI for deep research:> complete confusion, surface-level answers> chatgpt gives you a summary that sounds like wikipedia> you spend 3 hours verifying it across 50 open tabs> messy notes, no structure, no idea what to trust> then you find this system> 20 markdown files. 6 research lenses. one folder> you give claude one question> it rethinks it through 6 completely different angles> contradictions don't get buried, they become the insights> the system gets smarter every
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Alright. Put on your lab coat, loosen your tie, and read this properly. Here’s the step-by-step, no-nonsense, slightly savage guide to generating hypothetical (simulated) data 👇 STEP 1: Decide WHAT you’re pretending to study. Before you generate data, ask: “If this was a real study… what am I measuring?” Example: Job satisfaction GPA Stress level Zakat distribution efficiency (yes, that one 😌) 📌 Rule of life: If you cannot explain your variables to your insurance agent, don’t simulate yet. ST
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