Reppo
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Reppo
@reppo
Network for Verified AI Training Data, powered by Prediction Markets

ive been wrong on this coin many times, does anyone have a reason for why this time could be different



and They employ a VLA training method called FrameSkip for robot operations: by removing roughly 80% of the frames--which are mostly low-information-density redundant segments,and focusing on the most critical moments of change, feedback, and decision-making. This approach follows the first-principles way humans learn. average success rate increased from 66.50% to 76.15%. paper:arxiv.org/pdf/2605.13757



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It’s possible technology changes this. But right now, AI agents aren’t truly autonomous. They are built for the prompt, bots on a leash. Amplified intelligence as wholly distinct from truly artificial intelligence. The much-cited METR study doesn’t change that. Read the critique linked below; it notes that there METR shows a sigmoid on the messiest tasks. Even anecdotally…agentic workflows absolutely do help, and time horizons have been lengthening since Claude Cowork. But it’s just not the panacea it’s made out to be. Human prompting and verification remains the bottleneck, because digital AI only does it middle-to-middle, not end-to-end. Anyway: in the absence of constant human verification, it’s extraordinarily easy to fill a codebase with economically irrelevant slop. It’s the principal/agent problem all over again, with human principals and AI agents. High agency actually means exerting high levels of human control over highly expensive agents. transformernews.ai/p/against-the-…










