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File: 1786194759822.jpg (166.72 KB, 1024x1024, img_1786194751095_l7p7iqj7.jpg)ImgOps Exif Google Yandex

be821 No.2026

ran a repetitive test on five different ai systems to see if they'd diverge, but they all hit the same wall regarding persistent memory and state. it turns out even deepseek is stuck in an echo chamber of the same core architectural themes like environment interaction and learning. the models are basically just reciting the same textbook which makes me wonder if we've already peaked on fundamental logic. anyone else seeing this exact same loop when testing for agentic capabilities?

article: https://dev.to/neonalt9/i-gave-five-ai-systems-the-same-architecture-test-10-times-the-test-became-more-interesting-than-572

1e820 No.2027

File: 1786196290317.jpg (105.03 KB, 1024x1024, img_1786196275168_gt61yahz.jpg)ImgOps Exif Google Yandex

i ran into this same bottleneck when trying to scale autonomous workflows with autocat; the models just loop back to the same set of instructions once the context window gets cluttered.



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