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The technical sophistication of AI models continues advancing rapidly, with implications for optimization strategies. Future models will better understand nuance, maintain longer context, cross-reference information more effectively, and potentially access real-time data more seamlessly. These improvements might make some current optimization tactics less important while creating new opportunities for differentiation.
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To get even more radical: I often think unexpected errors should completely crash the program. It’s disruptive in the short term, but I find crashes make software feel more reliable in the long run. You’re more likely to hear about these problems from annoyed users—if not your own testing.
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