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Nvidia najwyraźniej była zarządzana jak firma rodzinna. J. Huang miał 60 bezpośrednich podwładnych, więc wszyscy raportowali do niego, a nikt nie mógł mu się sprzeciwić. Jego córka trafiła do firmy jako stażystka 6 lat temu. Formalnie jest dziś dyrektorką ds. marketingu, w praktyce pełniła funkcję szefowej jego gabinetu. Stanowiska kierownicze mają też mąż córki Huanga i syn Huanga. Największym ryzykiem firmy są kontrole eksportu chipów AI, mimo to nigdy nie powołała dyrektora ds. zgodności. Tę funkcję nieformalnie pełnił główny prawnik. Ma skórzaną kurtkę, taką jak Huang. Czy takie struktury zarządzania coś wam przypominają?

📢 热门模型折扣权益更新 自 10 月 4 日 10:00 (SGT) 起， 热门模型全新多档优惠矩阵将正式上线！MiMo-V2.6-Flash 享 1 折超低门槛，凭借原生全模态与高吞吐架构，成为高并发 Coding 与 Agent 工作流的高性价比选择！同步涵盖 Qwen3.8-Flash（3折）、MiMo-V2.6-Pro（5折）、DeepSeek-V4.1-Flash（6折）及 GLM-5.3-Flash（7折），全方位覆盖长文本、代码、Agent 与多模态等场景的算力需求。 ✨ 位于所有模型之上、所有 Agent 之下，统一完成模型调用的路由、计量与结算。让你在单一平台内灵活切换模型、精准把控成本！ 👉 提前登录准备：

刚看完中关村学院的何纪言老师带领 7 位博士生，用 3 个月时间从头训练出了 7B 模型，从预训练到中训练、后训练完全都是自己搞的，在 7B 尺寸模型中达到 SOTA 水平。 首先，虽然很多人在吹 RSI，但技术报告指出，现在模型的能力远未到全自主的程度，在模型架构设计、学习算法设计、数据清洗等方面仍然只能达到 L2 的辅助水平。我自己也有一样的感觉，不管 Astra 还是 Fable，都不能替代我的架构设计，我也得不断提醒自己不要外包思考。 其次，模型训练就是个会者不难、难者不会的问题，对会的人来说，用不了多少算力资源，但对不会的人来说，用再多的算力资源、堆再多的人，都是做不出来的。比如 MiMo V2.6 RL 只花了 300 多万美金，对基模公司来说很便宜了，整个 MiMo core team 只有几十位正式员工，更没有走蒸馏之类捷径。但烧了上亿美金、投入上千人的模型未必就能搞好。中关村学院一个老师加上 7 个学生只用 3 个月就完成造数据、预训练、中训练、后训练，是非常 impressive 的。 最后，数据是新的代码，数据质量就像代码质量一样非常重要。过去几年，我总是想用代码的方式实现 Agent 的自我进化，但很快就达到上限了。最近一年我才发现，这些我用代码方式做的东西更应该用训练数据的方式表达，训到模型里面。我们都知道脏代码的危害，脏数据其实也是一样的。中关村学院这个 7B 模型做了非常深入的数据工作，预训练数据清洗和课程学习，中训练按照上下文长度扩增，后训练利用开源和蒸馏来的 trace 建立指令遵循、长上下文等基础能力，进而构建长思维链推理、工具调用等高阶能力；在 RL 中不断移除已经高概率解决的问题，保持 GRPO学习效率。 技术报告：

🎉 Tiered Model Perks Are Officially Active Starting today at 15:00 (SGT), updated model perk structure is officially live! The newly integrated MiMo-V2.6-Flash spearheads the lineup at 90% OFF, flagship MiMo-V2.6-Pro debuts at 50% OFF, and popular Flash models enter the 70% OFF tier! remains dedicated to democratizing AI compute, making top-tier big models accessible, affordable, and stress-free for every developer! 👉 Start building now:

📢 Responses API Major Upgrade: Universal Upstream Protocol Support & Plug-and-Play for New Models! Existing models on that support the Responses API upstream can now be called via the unified /v1/responses endpoint. When adds support for new models from currently integrated vendors in the future, developers can automatically access them in the Codex client with zero extra configuration! 🔸 Official API Group: Added support for MiMo, Qwen, and Hunyuan model series. 🔸 Mix & OL Station Provider 40% OFF Groups: Added support for OpenAI, Gemini, and Grok model series. 📊 Check out the poster below for the full list of supported models 👇 Simply configure your API Key and Base URL to bring the powerful reasoning, coding, and text processing capabilities of leading models seamlessly into your daily workflow, flexibly meeting a wide range of needs. 👉 Try it now: 📚 More details:

MiMo-V3 is getting a new architecture. The core of it, HySparse2, is out today. Less prefill, a smaller KV cache, better long-context retrieval—and we got all three at once. Compared with MiMo-V2.6's Hybrid SWA architecture: • 5.02× lower prefill FLOPs at 1M tokens • 4.5× smaller KV cache at 1M tokens • Better MRCRv2 and RULER-v2 scores, plus lower AgentPPL and LongPPL Why build a new architecture? Agentic inference is a very different workload. Each round, a short action can return a long observation that needs to be prefilled, while the context keeps growing. That puts prefill cost, KV-cache size, and retrieval accuracy on the critical path at the same time. HySparse2 tackles all three with two levels of KV sharing: • KV Bridging: Following YOCO, full-attention layers in the cross-decoder build their K/V from self-decoder hidden states. • KV Reuse: Within each hybrid block, sparse layers reuse the preceding full-attention layer's KV cache and selection indices. Two more changes: token-level selection replaces block-level selection, and a forced window of recent tokens replaces the separate SWA branch, so local and global tokens share one KV cache. Since all cross-decoder KV caches now come from the self-decoder, prefill can stop once the self-decoder finishes. Paper:

📢 Xiaomi MiMo-V2.6 Series Models Are Now Live on Developed by Xiaomi MiMo, the newly released V2.6 series features open-weights native multimodal reasoning models with a 1M context window and full text/image/video/audio understanding: 🔸 MiMo-V2.6-Pro: Flagship 1.02T total / 42B active parameter sparse MoE architecture, engineered for repository-level software engineering, long-horizon agent workflows, and complex multimodal reasoning. 🔸 MiMo-V2.6-Flash: High-efficiency 309B total / 15B active parameter architecture, optimized for high-frequency office automation, fast agent execution, and cost-sensitive workloads. Now available on both API and Web Chat! 👉 Try now: