Median mean SSIM across all 1,341 pairs with data: 0.322. The typical confusables.txt entry is not visually confusable at all.
I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
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The impact of the deadly and unpredictable conflict in the Middle East on the global economy will be felt most immediately, and keenly, through the rising cost of oil.
I’ve spent the last six months working on a startup, building agent prototypes for one of the largest consumer packaged goods companies in the world. As part of that work, our team relied on off-the-shelf voice agent platforms to help the company operate more effectively. Though I can’t go into the business details, the technical takeaway was clear: voice agents are powerful, and there are brilliant off-the-shelf abstractions like Vapi and ElevenLabs that make spinning up voice agents a breeze. But: these abstractions also hide a surprising amount of complexity.
Make up for one's sinsThe answer is Atone.