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Why China Can't Sustain Pure-Play AI Model Companies?

Why China Can't Sustain Pure-Play AI Model Companies?

My first reaction to this passage was that, if we apply the same logic to the domestic market, the handful of foundation-model unicorns in China would also be unable to sustain their massive R&D spending over the long run.

•3min read
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This time, Anthropic is being called out for its double standards.

This time, Anthropic is being called out for its double standards.

In September, it was still calling on the entire industry to slow down AI for the sake of human safety; in October, it became: to save humanity, Anthropic must be No. 1.

•4min read
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Recommend StatusGator, a third-party cloud service status aggregator platform.

Recommend StatusGator, a third-party cloud service status aggregator platform.

StatusGator bypasses official self-reported updates entirely and uses cross-platform data collection and multidimensional signals to alert users early, before there is any official response.

•2min read
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How MiniMax M3.1 Flash Uses 200 Trillion Tokens in Real Combat to Break Through Real-World Workflows

How MiniMax M3.1 Flash Uses 200 Trillion Tokens in Real Combat to Break Through Real-World Workflows

MiniMax has officially released M3.1 Flash. Before the official announcement, the model ran online under the anonymous codename Space Bunny for more than 200 trillion tokens. Real-world feedback was used to thoroughly strengthen its core capabilities in enterprise workflows, coding, computer control, and cybersecurity offense and defense.

•3min read
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When your agent gets dumb mid-run, stop blaming the model.

When your agent gets dumb mid-run, stop blaming the model.

Official numbers put it at a trillion tokens a day, though it is hard to tell whether they burned that many tokens just finding this bug, or getting the whole setup off the ground. Unless you are a quant team, that kind of burn really stings.

•2min read
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DeepSeek V4.1 Flash finally cuts down on the fluff after the update.

DeepSeek V4.1 Flash finally cuts down on the fluff after the update.

This kind of repetitive boilerplate is not a sign that the model lacks capability; it is because the evaluation criteria used during training mistook step-by-step self-justification for high-quality answers. To prove that it has done the work, the model tends to lay out its entire reasoning chain and then casually pile on a large number of just-in-case disclaimer assumptions.

•2min read
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