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Z.ai Reframes Scaling Beyond Parameter Counts

Z.ai Reframes Scaling Beyond Parameter Counts

Z.ai argues that model scaling should account for data, compute allocation, inference cost, sparsity, effective depth, and post-training—not parameters alone. The post presents GLM-5.3 as a controlled experiment using the same total and activated parameters as GLM-5.2 while scaling long-horizon environments and reinforcement learning for one month.

Reddit r/LocalLLaMACommunity1d ago#scaling-laws#mixture-of-experts#post-training
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Why AI Office Agents Struggle to Charge

The article argues that consumer-paid subscriptions for AI office agents such as Workboddy, 千問辦公, and TraeWork are unlikely to succeed because productivity gains primarily benefit employers. It suggests these products must either deliver reliable, high-frequency workflow automation, compete on foundation-model capability, or monetize through enterprise delivery and ecosystem access.

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