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Oct 26
ModelsUS vs ChinaMulti-source74°15:30
Chinese contentClaude 自行找到绕过佐治亚州选举数据收费入口的方法

Anthropic 的 Claude 在做公开数据统计任务时,自己发现并申请了佐治亚州选举数据的访问令牌,拿到了平时要付费的投票数据,没人教它这么干,这案例对理解智能体自主行为挺有参考价值。

Event
@koltregaskes@koltregaskesSource
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ModelsUS vs ChinaSelected10:31
BOSS Zhipin releases Nanbeige4.3-33B open model

The simple Llama-style architecture means almost any inference engine can run it out of the box, at the cost of high memory usage and no built-in speculative decoding beyond external MTP. Agent-focused teams looking for an open teacher model to distil from should benchmark it against Qwen3.8-27B now, and watch for the promised sub-10B distilled releases. BOSS Zhipin being a recruiter entering open-weight modelling is itself notable for the China AI ecosystem.

Event
karminski-牙医 (AI工具)@karminski3Source
ModelsBuildersSelected09:55
MiniMax M3.1 Flash Now Post-Trainable on Together AI

The non-obvious shift here is that a frontier Chinese model can now be customised inside a US platform, with fine-tuning runs staying in the customer's own Python loop rather than a black-box pipeline. Teams building agents can adapt M3.1 Flash with custom reward signals and environments via SFT or RL. This makes MiniMax a direct alternative to open-weight models fine-tuned on Together, with the model served alongside Western options.

Official accountTogether AI@togethercomputeSource
ModelsBuildersSelected07:54
MiniMax H3 community adds 8-step video-audio generation and distillation

Distillation and step reduction directly cut inference cost, which matters for teams serving video generation without large GPU fleets — the Apple Silicon and TPU ports show H3 is no longer tied to one hardware stack. Video developers and studios experimenting with open video models can pick the 4-step or 8-step variants for faster iteration. MiniMax (Hailuo) remains one of the few Chinese labs openly cultivating a third-party ecosystem around video generation.

MiniMax_AI@MiniMax_AISource
Oct 25
ModelsBuildersMulti-sourceSelected02:02
vLLM Matches DeepInfer on DeepSeek-V4.1-Flash Decode

An open-source inference stack catching up with a frontier lab's in-house engine means teams can serve DeepSeek-V4.1-Flash without relying on the vendor's own tooling. Engineers running self-hosted deployments in Singapore or ASEAN data centres can benchmark vLLM at DEP32 as a direct alternative to DeepInfer's EP64DP64 setup, avoiding engine lock-in while keeping comparable throughput at large batch sizes.

Event
Official accountvLLM@vllm_projectSource
Oct 24

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