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Summary:

Everyone's suddenly an expert on open source AI, and almost nobody's using the term correctly. I break down what open weights actually means, why it's not the same as open source and definitely not the same as free, and why your laptop has zero chance of running a 753 billion parameter model no matter how many browser tabs you close first.

I walk through Hugging Face, show you what's actually inside these model files, and explain the real difference between downloading a model and being able to run one. If you've heard people throw around GLM, DeepSeek or Qwen and nodded along without having a clue what any of it means, this sorts it out in non tech speak.

—— Time Stamps ——

0:00 Open Source vs Open Weight AI: Why It Matters 0:40 The Open Source Price War: Why Anthropic & OpenAI Are Scared 1:49 Claude Sonnet 5 vs GLM 5.2: Anthropic's IPO Nightmare 3:15 What Open Source Actually Means (It's Not Free) 4:48 Open Weights vs Open Source: The Taxi, Car & Kit Car Analogy 7:08 How to Download AI Models: Hugging Face Walkthrough 8:06 Inside GLM 5.2: Licenses, Safetensors & 753 Billion Parameters 10:32 Running AI Models Locally: Hardware Requirements & Quantization 11:55 American vs Chinese Open Source Models: Who's Left in the Game 13:34 Open Source AI Rankings: The Top Models Are All Chinese 14:57 Trillion-Dollar IPOs vs China's Undercut Strategy 17:58 Cost Per Intelligence: $2.75 vs 50 Cents Per Task 20:39 The AI Triage System: Which Model for Which Task 23:06 Newsletter, Webinar & How to Get Paid Teaching AI

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