搜索“DeepSeek”

参与教学。“高一阶段打好扎实的基础：基础课程、项目实践、赛事集训；高二阶段注重个性化创新课题的孵化：导师制课题辅导、项目实习、赛事集训。”叶潇说，在学费方面，学院保持公益性收费。 学员们有哪些成长？ 学院成立两年以来，已经招收了两届学员，其中首届学员已毕业。 来自上海交大附中闵行分校的宋明岳是首届学员。他告诉记者，自己参加过学院寒假冬令营的“黑客松”项目，在DeepSeek爆火之前，少年宫的老师们就带着学生用DeepSeek插件做了智能体项目。之后，他跟着学院去北京参加第二届国际人工智能奥林匹克学术活动，与多国学生交流并展示自己的项目：以AI技术串联社区特色小店、节日活动等文化碎片，实时更新数据并生成个性化打卡路线，打造“会讲故事的社区窗口”。 2025年8月，上海青少年人工智能学院首届学生宋明岳（中）参加第二届国际人工智能奥林匹克学术活动（IOAI），其研究成果受到外国学生的关注。 在学院的学习中，宋明岳有个课题是“医学多语言大模型的构建”。他选择这个方向有自己的理由：爷爷、妈妈、弟弟和他本人都有严重的过敏症状，爷爷和外公还有心血管问题，“网上查不到这方面的个性化医疗咨询服务。”在上

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e performance much quicker than if you just train from scratch. Anastasis [01:32:36]: Yeah. Vibhu [01:32:36]: I think we’ve seen this across languages where Swyx [01:32:38]: Yeah, DeepSeek-OCR as well. Vibhu [01:32:40]: Yeah, DeepSeek-OCR. Swyx [01:32:41]: Like, you don’t have to tokenize text. Like, you can just throw them in as images. Vibhu [01:32:44]: There’s a lot that happens in that base pre-training. Like, there was an argument a long time ago of people saying, “Oh, humans have so many,

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nguages. Anthropic is in talks to lease up to 1GW of data center capacity from Stream Data Centers, potentially using AI chips from Google and Broadcom, The Information reported. YouTube added Gemini-powered editing in Shorts and YouTube Create, along with AI comment moderation and voice-and-face likeness detection for creators. China is reportedly probing DeepSeek and Moonshot over routing user data to Claude without users’ knowledge, following Anthropic’s Sept. 10 report on model distillation.

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clusionAI’s Ming-Image-0.1-Design is a 6B MIT-licensed model that ranks as the top open model for UI/UX design. Architecture trends : @eliebakouch compares four efficient designs: DeepSeek V4.1 Flash and MiMo V3 use YOCO. Qwen 3.8 Next Flash and GLM 5.3 Flash use 3:1 interleaving of sparse and linear attention. All four use Muon, mHC or gated residuals, and partial or no RoPE. TPU megakernel : Inferact open-sourced a TPU megakernel for Kimi K3 that reaches 709 tok/s versus 450 on a GB200 baselin

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few custom optimizations. We’re very grateful to be able to build on the work of the community here! The kernels we use for the core matmul and attention operations are standard: DeepSeek’s DeepGEMM and Dao et al.’s Flash Attention 3. These kernels are quite good at high-throughput/prefill-only inference. The speed-of-light cost model only considers these operations, but there are many others in the forward pass. Their runtimes are in principle negligible, but there are a lot of them and they a

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epending on what is supported by the chips in the cluster. We use a common set of shapes for different gate up and down projections at varying batch sizes in the Kimi K2.5, K3 and DeepSeek V3, V4 Pro models. We then test GEMMs, GEMV bandwidth, and MAMF (Maximum Achievable Matmul FLOPS, from Stas Bekman), which runs a sweep for a while on each GPU, per precision, using cuBLASLt (or rocBLAS). We report FLOPs for all of our GEMM tests. We also test memory, through a GEMV test, and nvbandwidth . GEM

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fixed performance has been falling 47% per quarter since 2023 was one of the more useful macro datapoints of the day @EpochAIResearch . /r/LocalLlama + /r/localLLM Recap 1. Qwen, DeepSeek and AliceAI Large-Model Roadmaps Qwen4-27B just confirmed (Activity: 2353): A conference slide image appears to confirm an upcoming Qwen4 Series lineup, explicitly listing Qwen4-27B alongside Qwen4-Max, Qwen4-Flash, and Qwen4-Plus. The post highlights community interest in whether Alibaba will also release a s

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rge language models. arXiv:2505.24864; Millidge, B. (2026, June 12). Distillation ain't what it used to be. beren.io; Shao, Z., Wang, P., Zhu, Q., Xu, R., Song, J., et al. (2024). DeepSeekMath: Pushing the limits of mathematical reasoning in open language models. arXiv:2402.03300; Shenfeld, I., Pari, J., & Agrawal, P. (2025). RL's Razor: Why online reinforcement learning forgets less. arXiv:2509.04259; Stojanovski, Z., Stanley, O., Sharratt, J., Jones, R., Adefioye, A., et al. (2025). Reasoning

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……是的，我描述这个的方式就像是四分钟一英里式的效应：你证明某件事完全可行，然后人们就有了信念去走那条路，不必浪费大量资源去探索一堆其他东西，这就是你在这里指出的东西。 00:38:43 Nathan Lambert：是的。而且我认为这—— 00:38:44 JS Denain：我想我是在找一些你认为这……有哪些例子？因为比如推理模型就不是这个，对吧？比如 DeepSeek Math 之类的，远早于 o1。是的，我有点好奇是否……是的，我同意这看起来有点合理，但我不确定我能举出前沿实验室做出重大创新的例子—— 00:39:04 Nathan Lambert：我认为这更多是组织结构的问题。也就是说，实验室里有研发和建模算力，我觉得这两者有点被混在一起了。而现实情况是，让下一代模型变得更好的算力，和两三代之后的模型相比，差别非常大。而且我认为中国实验室只是在更短的时间跨度上投入。然后，如果架构或其他方面出现重大创新，并在美国生态系统中传播开来，我猜中国实验室也会搞明白。我不认为架构创新能在 Anthropic、OpenAI、Meta 和 Google 之间很好地保密。我认为这些都会通过人员

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, so it is very surprising to the established order of names you have come to know and love. And… it is natively omnimodal! Xiaomi made news a few days ago when Fuli Luo, a former DeepSeek star engineer now at Xiaomi , started publishing their final RL training runs live, which showed an abnormal amount of transparency in their internal metrics . As they note in their technical report , they scaled RL compute along three axes: Larger batches and higher throughput : large batches on a fully async

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16ビット版は65.6％だった。3回のうち一度でも見つけた脆弱性は、Altar-1とGLM-5.3の量子化版がともに23件、16ビット版が25件だった。同社は、量子化版と同じ23件を検出しながらモデルのサイズを32.8％減らせた点を、狙いどおりの成果としている。 なお、同社の8月公開の記事では、この32件を使った他モデルの評価結果も紹介している。3回を通じてDeepSeek V4 Proが28件、Opus 5とGrok 4.6がそれぞれ26件を再発見し、検出件数はAltar-1を上回っている。 Hugging Faceで公開⁠、H200を4基使う構成を案内 モデルはHugging FaceのAikidoSec/altar-1から入手できる。モデルカードでは、NVIDIA H200を4基搭載した環境で推論エンジンvLLMを使う構成を案内している。328GBはモデルの重みを保存した際のサイズで、実行時には入力や生成内容に応じた一時データの分もメモリが必要になる。 ライセンスはGLM-5.3 Licenseを継承し、著作権・許諾表示の保持などを条件に商用利用、改変、再配布を認める。ただし、利

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前 AI 是一大段系统消息，然后也许你还有另一个大的 AI Swyx [01:02:09]：大的输出，是啊 Diogo Almeida [01:02:10]：来看看它是否真的做到了这一点。这太疯狂了吧？这，这有点疯狂。就像，它——那就是我们的斯德哥尔摩综合征，对吧？但这有点疯狂。比如，如果你想说的是，“嘿，别读这个子目录”，或者“别把任何 API 密钥传给 DeepSeek”，或者别的什么，那基本上应该能在程序层面得到保证。你永远不会有任何机器学习模型的保证，但通过把它拆解，你实际上可以——你实际上可以测量它，对吧？就像 Swyx [01:02:37]：是啊，你可以验证它确实被调用了 Diogo Almeida [01:02:39]：对，而且比如，我们的模型，我们的模型——接口本身是如此可验证。这应该让人松一口气。 Diogo Almeida [01:02:46]：就像，这，这，这，这只会带来好得多的工程实践。 Swyx [01:02:50]：是的。我想我明白了。所以过去人们不这么做的原因之一，就是他们只会去调用一个小型 LLM，对吧？ Diogo Almeida [01:02:59]

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into two categories, open-weight and open-source models. Open-weight models are the most common form, such as popular models like Meta’s Llama, Alibaba’s Qwen, Google’s Gemma, or DeepSeek’s models. These models are governed by licenses, governing documents dictating what is allowed with downstream use, and are often accompanied by inference code in libraries such as Transformers, VLLM, SGLANG, etc. Since about April 2025, Chinese AI companies have been the clear leader in open-weight models. Tr

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nd tk-train , so an application links only what it uses bitcannon: replace regex splitting on the encoding path with bitstream operations covering GPT-2, cl100k, o200k, Tekken and DeepSeek. This replaced the finite-state machines that shipped first 2201 2317 WordCache: reuse the token IDs of previously processed pre-tokens 2262 , af5a3e3 faster lookup and merging structures: add FlatCache, MPHF RankStore, incremental merging, and BucketVocabStore 2190 2188 reusable model memory: move temporary m

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oracle router across all eighteen models reaches 97.6% at $1.88 a task. That is 23 points above GPT-6 Astra, at under a third of its cost. Restrict it to open-weight models only (DeepSeek V4 Flash and Pro, GLM-5.3 and GLM-5.3 Flash, Kimi K3, Qwen3.8 Max), and it still reaches 90.3% at $1.45 a task, which beats every closed model here by 16 points while spending under a quarter of what Astra does. These results make “open versus closed” a less interesting debate. The emerging race is to move fro

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啊。终于在频繁的测试后自己崩溃了，我这里接管催它尽快交付。 最后用时 3 小时 46 分钟，使用 GLM Coding Plan Lite 套餐，5 小时额度消耗 90%，周额度消耗 18%。 等了这么久，看看成品。方向键是反的。最大的问题是，一旦撞到赛道两边的护栏，车就动不了了。最后勉强跑完了一整圈，等了快四个小时，做成这样还是有点失望。 这里我也用了 DeepSeek 拥有视觉能力的新模型 DeepSeek V4.1 Flash 测了一下，只能说半斤八两。方向键终于能正常操作了，但是没有边界限制，可以满图随便跑。 这轮两家的表现都差强人意。 Part 3 · 复刻操作系统 接下来是经典测试题，用一个 HTML 文件复刻整个 macOS 桌面系统，另外还得在里面做一个能玩的射击游戏。用了 1 小时 21 分钟，5 小时额度消耗 18%，周额度消耗 5%。 图标绘制得很精致，访达、备忘录、终端、设置都正常可用，壁纸也能换。 最后看看射击游戏，也没任何问题，走路的时候还有轻微抖动，有不同强度的敌人，整体没有出现任何 bug。 同样用 DeepSeek 也测了一下，效果比起 V4 Fla

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escribed a split between “frontier for planning, cheap for execution.” @TheAhmadOsman summarized one stack as GPT 5.6 Sol XHigh for planning, GLM 5.3 Flash for implementation, and DeepSeek V4.1 Flash for other tasks. @kylebrussell reported an internal knowledge-base pipeline moving from Opus → Sonnet → GLM 5.2 → GLM 5.3 Flash , cutting spend by roughly two orders of magnitude since spring. Meanwhile @theo argued that in real-world coding the payoff from stronger models like Fable and Astra is no

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、滥用和社会后果，而在美国的讨论则更多地集中在人工智能失控或人类灭绝等假设性情境上。” 图像来源， Reuters 虽然中国官员忧心人工智能带来的风险，但他们也相信，在中国经济成长放缓之际，人工智能将成为提振经济的重要动力。 譬如，腾讯、阿里巴巴、百度和字节跳动等中国科技巨头的人工智能模型，正与全球主要竞争对手一较高下。一些领先模型则来自零一万物、深度求索（DeepSeek）、MiniMax、月之暗面和智谱等新创公司。 这些公司都没有回应BBC的置评请求。 刘少山博士向BBC解释，北京正力求在培育人工智能产业和管控相关风险之间取得平衡。 “这与主张停止发展人工智能是两回事，”他说。 除了国内发展需求，北京也必须考量地缘政治因素。 美国总统特朗普（Donald Trump，川普）将人工智能发展描述为一场与中国的高风险竞赛。 “我们在人工智能领域领先中国……坦白说，我希望保持这种领先地位，因为谁赢了人工智能，谁就赢了，”他曾说。 中国国家安全部长陈一新在9月发表于官方期刊的文章中，也从地缘政治的角度阐述人工智能的发展，称其为“大国战略博弈的新赛道”。 尽管呼吁放缓人工智能发展的呼声主要来自

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here it might help save the company money, especially if it could prevent it from paying for an unnecessary background check, the greater value is time savings, Eagy said. Related:DeepSeek and what enterprises should learn from AI agent exploits Alight provides an example of how measuring a return on AI investments can get messy. For many companies, the return on investing in AI tools might not be monetary gains but rather productivity gains or the ability for the AI agent to provide a missing p

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t provenance was intense, including from @Yuchenj UW , before @eliebakouch concluded one confusion was likely due to a router/mis-served model , not evidence of a new GLM release. DeepSeek-V4.1-Flash keeps showing up as the practical open default : It became the default in HuggingChat via @victormustar , and multiple practitioners argued it is under-evaluated relative to impact, notably @teortaxesTex . Anecdotal usage ranged from gaming optimization with Hermes Agent to self-hosted/open workflow

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