Nathan's book is incredible. Every chapter has something I missed the first time around too.
@whats_ai
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Current AI Models: Far From Cognitive Task Saturation
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Depends a lot on what "most cognitive tasks" means. We're far from maxing out current models on existing tasks.
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Game Theory AI Job Creation Economic Impact Analysis
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The game theory framing is interesting. Wonder how it accounts for the jobs AI creates that didn't exist before though.
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Optimize costs: use cheaper models for routine tasks
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Let the cheaper model handle 90% and escalate the hard stuff.
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AI Tier Gap: Free ChatGPT vs Claude Code Capabilities
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The tier gap is massive. I had someone tell me last month that "AI can't write code" because they tried free ChatGPT once. Meanwhile Claude Code is building entire apps.
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Pleasure of Working Together with Shared Passion
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Tough to beat the pleasure of working with someone as passionate about it as I am! Glad to have done this with you Paul!
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AI Scaling Bad Targeting: Context Matters More Than Prompts
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The hardest part of AI is not about writing the perfect prompt.
The hardest part is knowing who you’re actually talking to. AI won’t fix bad targeting.
It just helps you scale it faster. Automated “AI-generated outreach” fails for the same reason this text did: → No context -
Drop-in Solution Could Revolutionize Agent Training Approach
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If this actually works as a drop-in it changes the agent training story completely. We'll see!
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Comparing LangGraph, AutoGen, and CrewAI for Full Stack AI
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One person running this full stack is ambitious. Curious how you pick between options like LangGraph, autogen and CrewAI for a given use case George 🙂