Founders today are conditioned to believe venture capital is the ultimate milestone — a rite of passage into “real” entrepreneurship.
Watch: Moment Iran's state TV announces Supreme Leader has been killed
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ОАЭ задумались об атаке на Иран20:55
Given the complexity of home scenarios and their long-tail distribution, today’s mainstream technical approaches are still evolving. On the data side, training data often relies on lab demonstrations, limited real-world trajectories, and publicly available videos, leaving significant room to improve generalization to unknown environments and novel task combinations. On the objective and representation side, traditional VLA systems are typically optimized around aligning vision–language–action and reproducing behaviors; deeper modeling of the semantic structure behind actions and a composable skill space is still needed. As a result, models behave more like they are “matching/reusing” existing action fragments rather than generating feasible new strategies based on goals and constraints, making it difficult to handle the highly long-tailed and constantly changing task demands found in real homes.
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It was just core devs.
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