搜索“DeepSeek”

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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liability does the job, and what an eighteen-month pause costs in cancer cures and vibe-coding projects; Send in Demis! plus Lu Xun as the antidote to Chongqing TikToks, and what DeepSeek’s Chinese name owes to Qu Yuan Jordan Schneider: Ladies and gentlemen, ModelTalk is back. Where do we start this timeline? Nathan Lambert: Hugging Face, OpenAI. July. Jasmine Sun: There’s the pacing letter at the end of July, then Hugging Face shortly after that, and everything has been escalating since. Then

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otal traffic within the same power budget. The initial wait for the first token and overall response time remain separate considerations in the preceding latency analysis. For the DeepSeek V4 Pro agentic workload, Vera Rubin delivers substantially more total token throughput per megawatt than GB300 and MI355X at the matched targets compared below. The size of that advantage depends on the interactivity target and the serving engine used for comparison. At 100 TPS, Rubin delivers approximately 59

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Some social media users, including former CNBC anchor Deirdre Bosa, turned toward Chinese AI stars like Tangjie from Zhipu AI, Yang Zhilin from Moonshot AI, and Liang Wenfeng from DeepSeek to see how they would respond. While they are usually vocal about model achievements and benchmarks, weighing in on a global debate about slowing down progress is a different topic. Chinese AI leadership has to be careful here. That said, China’s domestic focus on security is tightening recently. Minister of S

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tion. When the first chips came back from the foundry in May, the team pointed its internal AI models at designing software to run benchmarks such as SemiAnalysis’s InferenceX. On DeepSeek’s multi-head latent attention kernel benchmark, performance climbed from 0.31 percent of the theoretical ceiling (set by the chip’s compute and memory bandwidth) to 88.94 percent in roughly 40 hours. Ho says this result is repeatable, so the time between when foundries deliver the first chips and when producti

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rts of the country.” See Thy’s full workflow Visit The Rundown University . How do you use AI? Tell us for a chance to be featured. 🧠 Smaug Flash - Abacus’ open-weight, 3x cheaper DeepSeek Flash fine-tune ⚙️ SWE-2 - Cognition’s powerful new coding model inside of Devin 🎵 Music v2.5 - ElevenLabs’ latest music AI for richer melodies, long-form songs 🤖 Muse - Meta’s new always-working personal AI agent Amazon Connect Customer - design, govern, and deploy agentic conversational AI across every custo

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eve HBM capacity pressures very effectively. This is for Kimi K3, which doesn’t take into account some of the even more extreme cache reduction techniques introduced in the latest DeepSeek v4.1 Flash model, which reduce active KVCache sizes by around 75% compared to v4 Flash. At these reduced KVCache levels, increasing concurrency to utilize all the HBM capacity is likely to mean that the workload becomes compute and network bound rather than capacity bound, therefore also diminishing the value

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would get if you train an actual sensible size model on it. Charlie O’Neill It’s hard as well now because so many of the architecture changes — you look at Kimi, for instance, or DeepSeek — they’re doing these architectural modifications not just with dropping the pre-training loss in mind, but with how the models are going to be used in the real world. The inference efficiency, having some form of compressed attention in the DeepSeek models, is not necessarily geared around a fundamental trade

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rs might have a performance edge. But the test for Mistral is whether enterprises will see that added control as valuable enough to influence how they choose AI providers. Related:DeepSeek and what enterprises should learn from AI agent exploits Mistral's open-weight approach has been central to its pitch to customers that want more flexibility in how they deploy and customize their AI systems. Open-weight models are especially important for companies that want to keep sensitive workloads under

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or the subagents, you can create a file. I don’t usually mess around so much mixing this many models but I do want to get into using open-source models (I have since started using Deepseek v4 Flash as a daily driver and really enjoying it - as well as GLM 5.3). One of the ideas from my previous critiques and ‘10x’ prompts was a shuffle button. You click shuffle and it generates a site for you in those styles - this felt great, you could see what your choices would look like in place. Many more b

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mpanies have very publicly shifted the models they use from American, closed labs to Chinese open models to save costs. Examples include Perplexity prominently and rapidly adopted DeepSeek R1 ( Forbes , Jan. 28 2025) and Thomson Reuters building on Qwen to move off Claude ( Business Insider , Aug. 24 2026) Leave a comment What is distillation and how much does it help Chinese labs, how do open models impact frontier AI risks like cybersecurity, and how far are open models behind the closed front

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efeating the Training-Inference Mismatch via FP16 (Qi et al., 2025). The reproduction code is in sail-sg/Precision-RL . The authors generated 40 answers for each MATH problem with DeepSeek-R1-Distill-Qwen-1.5B . They kept problems with a success rate between 20% and 80%, yielding 1,460 questions. This dataset is really good for RL validation because the questions are neither already solved nor completely hopeless for that model, meaning the model can get a good early signal to train on and impro

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shown in Figure 1. Figure 1: There’s a strong correlation between NLS depth and the number of layers in open-source models. Note: Models like Kimi-K3 that use Gated DeltaNets and DeepSeek-V4-Pro have significantly higher NLS depth per layer than the other more standard transformers listed in Figure 1. We think that the architectural changes that give Kimi-K3 higher depth is indeed somewhat concerning from a monitorability perspective. On the other hand, we consider DeepSeek-V4-Pro’s high depth

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ranks slightly lower overall might be excellent at the kind of coding work you do every day. For reference, the most popular open models in the space right now are GLM 5.3 Flash, DeepSeek V4 Flash , Kimi K3, and MiniMax M3 . These change very rapidly though. After you’ve selected a few models to try out, the next step is to find a provider that hosts them. The way these providers work is that you send them an API request and they run the model on their GPUs. You pay for the input you send to th

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27 2 分享 Avid Artifacts 的读者知道，我们不仅一直在报道模型，也报道它们的许可证，而且已经持续了相当长一段时间。曾经有一段时间，自定义许可证风靡一时，例如自定义的 Qwen2.5 72B-Instruct 许可证或 Llama 许可证。DeepSeek 曾为 DeepSeek V3 使用自定义许可证，后来 R1 将其改为 MIT，这导致许多（中国）模型厂商在 2025 年采用 MIT 或 Apache 2.0 许可证。 2026 年，开放模型比以往任何时候都更具竞争力，这带来了两个有趣的发展：西方模型厂商采用开放许可证，Google 和 Meta 都转向了 Apache 2.0。然而，处于前沿的中国模型厂商正变得越来越严格：Kimi K3 附带的许可证要求提供推理或微调服务者签订商业协议，MiniMax M3 则要求超过收入门槛的协议，并规定了禁止使用的场景。 最新的例子是智谱的 GLM-5.3，它从 MIT（GLM-5.2 及更早版本）改为自定义许可证，对推理和微调服务提供商有如下条款： 如果被许可方或其任何关联方经营模型即服务（Model as a Servic

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into one all-gather, removing an extra collective whose latency can dominate the transfer time for such small arrays. The PR reports roughly 80 microseconds per layer saved in its DeepSeek-V3 measurements. While 80 µs may sound small, DeepSeek-V3/R1 has 58 layers that require this AllGather. Across those layers, the savings add up to around 4.64 ms per forward pass in isolation (80 µs × 58), reducing TPOT and increasing interactivity. Google also implemented the ReduceScatter collective on Spars

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