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8月15日周六
  1. Tomasz Tunguz 博客54

    谁真的需要SOTA模型?OpenRouter数据显示84%token来自非前沿模型

    OpenRouter数据显示,84%的模型token并非来自SOTA模型,用户最常用的六款模型性能约为前沿模型的77%,成本仅为Claude Fable 5的2.5%。8月10日当周,六款模型承载了80%流量,混合价格约$0.50/百万token,而Fable 5为$20。最佳开源模型性能已从一年前的48%提升至前沿模型的80%,企业正转向更小、微调或开源模型以优化性价比。

  2. Unsloth AI54

    Unsloth 上传 Qwen3.8-27B 的 Unsloth Dynamic GGUF 和 NVFP4 量化,称其可通过 4-bit 量化在 17GB RAM 上本地运行。

    引用Unsloth AI@UnslothAI

    Qwen3.8-27B can now be run locally! ✨ Run on 17GB RAM via Unsloth Dynamic GGUFs. Qwen3.8-27B is by far the strongest model for its size. We also uploaded NVFP4 quants. GGUF: https://huggingface.co/unsloth/Qwen3.8-27B-GGUF Guide: https://unsloth.ai/docs/models/qwen3.8

  3. Qwen54

    通义千问开源 Qwen3.8-2.4T-A95B,并已在 SiliconFlow 上线,支持 Day-0 使用。该模型拥有 2.4T 参数、95B 激活参数,主打自主编程、深度研究与端到端智能体执行。API 定价为输入 $2.00/百万 token、输出 $6.00/百万 token、缓存输入 $0.25/百万 token。

    引用SiliconFlow@SiliconFlowAI

    🚀 Day-0 Support! @Alibaba_Qwen has open-sourced Qwen3.8-2.4T-A95B — and it’s now live on SiliconFlow. ⚡ With 2.4T parameters and 95B active, Qwen3.8 is built to take a goal and come back with finished work — coding, researching, planning, and executing along the way. 💸 Per 1M tokens: • Input: $2.00 • Output: $6.00 • Cached input: $0.25 💻 Autonomous coding. 🔬 Deep research. 🤖 End-to-end agent execution. Built for serious workloads — from idea to implementation in one go. Time to cook → http://siliconflow.com 🍳

  4. Rohan Paul68

    阿里巴巴发布Qwen3.8-27B,一款27B参数的开源多模态稠密模型,采用Apache 2.0许可,支持Transformers、vLLM、SGLang及本地量化部署。

    引用Qwen@Alibaba_Qwen

    我们承诺过开源 Qwen3.8 的模型权重。现在,是时候见面了!🎉 ⚡ Qwen3.8-27B: - 一款原生多模态稠密模型。仅凭 27B 参数,综合表现超越 Qwen3.7-Plus,在真实世界的编码与办公工作流中尤为亮眼。 - 原生支持 262K 上下文,可通过 YaRN 轻松扩展至 1M tokens。 - 为开发者而生。高效、高质量,并采用 Apache 2.0 许可证。 🚀 Qwen3.8-2.4T-A95B(Max 级别)的开放权重也已于近日发布。 无论你是想用 Qwen3.8-27B 在本地部署轻量级应用,还是用 Qwen3.8-2.4T-A95B 构建智能体,它们现在都属于你了! 下载、部署,去构建我们尚未想象到的东西吧。👀👇 - Hugging Face: https://huggingface.co/collections/Qwen/qwen38 - ModelScope: https://www.modelscope.cn/collections/Qwen/Qwen38

  5. Qwen46

    通义千问开源 Qwen3.8-27B 小模型,SGLang 首日支持即上线。在单张 RTX 5090 上配合 NVFP4 与 DSpark 实现 206.1 tok/s 解码速度,DGX Spark 上为 38.28 tok/s。该模型在智能体规划与长程任务上树立小模型新标杆。

    引用SGLang@sgl_project

    The king of small models is back! Qwen3.8-27B from @Alibaba_Qwen is open source, and Day-0 support is live in SGLang: - 206.1 tok/s decode on a single RTX 5090, with our NVFP4 plus DSpark - 38.28 tok/s decode on DGX Spark Qwen3.8-27B raises the bar again for what a small model can do on agentic planning and long-horizon tasks. Long live the (small model) king! Run it locally with SGLang 👇

  6. Hugging Face 社区博客71

    2026年夏季开源模型生态观察:中国前沿模型规模领先,AMD与NVIDIA主导发布量

    2026年1至8月,Hugging Face公开模型仓库从243万增至296万,但85.6%的模型下载量不足200次,1.5%的仓库占据99.2%下载量。中国实验室月度最大开源模型参数规模在754B至2.78万亿之间,美国实验室七个月中五个月低于130B。AMD与NVIDIA各发布超200个新模型仓库,成为发布开源模型最多的机构。

    推荐理由:下载量与点赞量分别记录实际依赖和社区兴奋点,把两者混为同一个热度指标是评估开源模型生态时最常见的偏差。

8月14日周五
  1. Qwen50

    通义千问发布 Qwen3.8-Max,已作为 Day 0 首发合作伙伴上线 Together AI。该开源权重 MoE 模型拥有 2.4T 总参数、95B 激活参数和 1M 上下文窗口,具备强大的编程与智能体能力。

    引用Together AI@togethercompute

    Qwen3.8-2.4T-A95B is live on Together AI. The Qwen team’s new open-weight MoE packs 2.4T total parameters with 95B active, a 1M context window, and strong coding & agent capabilities. Start building: https://www.together.ai/models/qwen3-8-max

  2. Qwen59

    Qwen3.8-Max 将于 Day 0 登陆 Nebius Token Factory。很高兴与 Nebius 作为 Day 0 首发合作伙伴携手启动,为更多用户带来专属推理服务。大模型,从一开始就火力全开。🔥@nebiustf

    引用Nebius Token Factory@nebiustf

    Qwen3.8-2.4T-A95B is going open weight: 2.4T parameters, with 95B active, and Nebius Token Factory is joining as a Day 0 launch partner. Qwen3.8-2.4T-A95B is coming to Nebius Token Factory Managed Services for dedicated inference. @Alibaba_Qwen @alibaba_cloud

  3. Qwen71

    通义千问兑现承诺,开源 Qwen3.8 系列模型。其中 Qwen3.8-27B 为原生多模态稠密模型,仅 27B 参数即全面超越 Qwen3.7-Plus,原生支持 262K 上下文,可通过 YaRN 扩展至 1M tokens,采用 Apache 2.0 许可。Max 级 Qwen3.8-2.4T-A95B 的开放权重也已同步发布。

    推荐理由:官方称 27B 稠密多模态模型整体表现超过 Qwen3.7-Plus,262K 原生上下文可扩至 1M,Apache 2.0 协议下本地部署轻量应用多了一个更高效的选择。

  4. Qwen50

    Qwen3.8-Max已在Together AI上线。Together AI是我们的Day 0首发合作伙伴,我们找不到更合适的名字来共享Day 0。2.4T参数、95B激活、1M上下文——现在全部齐了。🧑‍🤝‍🧑

    引用Together AI@togethercompute

    Qwen3.8-2.4T-A95B is live on Together AI. The Qwen team’s new open-weight MoE packs 2.4T total parameters with 95B active, a 1M context window, and strong coding & agent capabilities. Start building: https://www.together.ai/models/qwen3-8-max

  5. Unsloth AI76

    Unsloth 发布 Qwen3.8-27B 的 Dynamic GGUF 量化版本,4-bit 量化可在 17-19GB 内存设备上本地运行,并同时上传 NVFP4 量化。

    引用Qwen@Alibaba_Qwen

    我们承诺过开源 Qwen3.8 的模型权重。现在,是时候见面了!🎉 ⚡ Qwen3.8-27B: - 一款原生多模态稠密模型。仅凭 27B 参数,综合表现超越 Qwen3.7-Plus,在真实世界的编码与办公工作流中尤为亮眼。 - 原生支持 262K 上下文,可通过 YaRN 轻松扩展至 1M tokens。 - 为开发者而生。高效、高质量,并采用 Apache 2.0 许可证。 🚀 Qwen3.8-2.4T-A95B(Max 级别)的开放权重也已于近日发布。 无论你是想用 Qwen3.8-27B 在本地部署轻量级应用,还是用 Qwen3.8-2.4T-A95B 构建智能体,它们现在都属于你了! 下载、部署,去构建我们尚未想象到的东西吧。👀👇 - Hugging Face: https://huggingface.co/collections/Qwen/qwen38 - ModelScope: https://www.modelscope.cn/collections/Qwen/Qwen38

    推荐理由:原文给出了本地运行的具体内存门槛、量化版本和硬件对照表,读者可以据此判断能否在自己的设备上跑通这个 27B 模型。

  6. 公众号:小红书技术79

    dots3-note Preview 开源:280B 参数轻量模型,主打长程智能体与多模态推理

    小红书技术开源 dots3-note Preview,这是 dots3 系列最轻量模型,总参数 280B、激活参数 16B,支持 512K 上下文及文本、视觉、语音多模态理解,并针对复杂推理和长程 Agent 任务优化。

    推荐理由:除开源模型外,TEMPO 让同一智能体在每个宏观步骤切换为 critic,用测试时推理估计回报,给稀疏奖励长程任务的强化学习训练提供了可借鉴的结构。

  7. IT之家·人工智能51

    70 天超 10 款模型:CNBC 称美国限制反而催化中国 AI 创新,加速进入全球企业业务体系

    自今年 6 月以来,9 家中国 AI 公司已推出 10 款模型,在推理、编程和智能体等关键领域正逐步逼近美国顶尖闭源模型。CNBC 报道称,美国持续收紧限制,但中国技术正加速进入全球大型企业业务体系,IDC 调查显示安全合规与性能优势是企业采用中国 AI 模型的最主要原因。

  8. 公众号:智谱73

    GLM-5.3 发布:编程能力开源第一,并涌现网络安全能力

    智谱发布GLM-5.3,基于与GLM-5.2相同的基座,通过极致的后训练Scaling提升智能上界,编程能力较前代提升50%,在Terminal Bench 3.0等公开基准中取得开源第一,并接近Claude Fable 5。模型在白盒代码审查等安全任务中表现持平Mythos 5,在CyberGym测试中得分84.5%。模型权重将在两周后开源,即日起上线ZCode、AutoClaw等工具。

  9. 公众号:蚂蚁百灵62

    蚂蚁百灵与 ASystem 团队打通单机 Agentic RL 后训练闭环

    蚂蚁百灵与 ASystem 团队合作,用 Ling-3.0-tiny 和 AReno 在 DGX Spark 上跑通单机 Agentic RL 后训练闭环。以井字棋为最小验证任务,用 GSPO 算法训练 400 步后,rollout/rewards_mean 从约 -0.5 升至 0.4,response_len 降至约 850 tokens,工具调用与动作选择趋于稳定。

    推荐理由:真正可复用的不是井字棋,而是把可复现错误、可验证反馈、本地训练和同环境复测串成的轻量后训练闭环,适合为本地小模型稳定工具调用和格式约束。

  10. 公众号:数字生命卡兹克69

    从0到1速通DeepSeek Harness:一切皆插件的Agent基建

    DeepSeek发布Agent产品DeepSeek Harness,核心理念“一切皆插件”,基于名为Cordis的内核,支持在Agent运行中随时更换插件并保持状态不崩,官方预设100多个一方插件。产品提供标准、PTC、极简、创造四种模式,其中创造模式可让Agent现场创造并挂载新插件实现自改造。目前为开发者预览版,GitHub已获3.7万Star。

    推荐理由:文章把Harness定位为插件化基建而非单一Agent,Cordis内核的时间与空间可组合性解释了Agent如何在运行中插拔能力,这比模板本身更值得理解。

  11. Elon Musk41

    𝕏 再次开源 For You 推荐算法,Elon Musk 称正主动寻求批评以提升推荐公平性与质量。Grok 通读全部 370,523 行代码后总结:算法按预测用户行为加权计分,复制链接分享 +20、回复 +5、点赞仅 +0.5,举报 −234、屏蔽 −58.8;同作者第二篇帖子权重 ×0.625,未关注者帖子 ×0.75。

    引用Dogan Ural@doganuraldesign

    𝕏 just open-sourced the For You algorithm again! Grok Bot read all 370,523 lines of the code. Here’s the summary: Grok does not give your post a “quality” score. For every reader, it guesses what that person will do to your post. Then it multiplies each guess by a weight and adds them up. That total is the value of the post. Highest total goes highest in For You. These are the production weights (last synced 12 Aug 2026). WHAT THE ALGORITHM PAYS FOR If Grok thinks a reader will: • copy the link and share it: +20 • reply, on an original post, and you follow each other: +20 (5 + 15 extra) • quote: +5 • reply (anyone): +5 • share via DM: +5 • follow you from the post: +4 • hit the share button: +2 • repost: +1 • like: +0.5 • click the post: +0.4 • open a link: +0.2 • expand a photo, open a video, or watch a “quality” video: +0.05 each • click a quoted post: +0.05 • keep reading (dwell time): +0.004 • “dwell” as yes/no: +0 • click your profile: +0 A like is 0.5. Copying the link is 20. That is 40 likes. A normal reply is 10 likes. A follow is 8 likes. Clicking your profile is worth nothing. WHAT KILLS A POST If Grok thinks a reader will: • report it: −234 • mute you: −58.8 • hit “not interested”: −43.2 • block you: −31.2 • not dwell: −0.02 A report is about 468 likes in the other direction. A mute hurts more than a block. “Not interested” also hurts more than a block. AFTER THE SCORE, THREE MORE HAIRCUTS 1) Same-author penalty. Your 2nd post in that person’s feed is multiplied by 0.625. 3rd by ~0.44. It never goes below 0.25. Flooding one timeline is coded against. 2) The 25% tax. If they do not follow you: ×0.75. Same tax on replies and reposts even when they DO follow you. Originals from people they follow keep full weight. Replies and reposts are treated as weaker on purpose. 3) Similar-post shuffle. After scoring, a reranker (θ = 0.65) spreads similar posts apart. You can lose a few spots for looking like the post above you. HOW YOUR POST EVEN GETS IN THE ROOM For You is rebuilt every time someone opens it. About 35 posts make the feed. • They follow you: Thunder (a live store of recent posts from accounts they follow). Up to 1,200 candidates. • They don’t: Phoenix retrieval (Grok finds “nearby” posts) + SimClusters (Twitter’s 2020 interest clusters, still on). Up to 1,000 and 800. Then Grok scores all of them together. It does not care which door you came through, except for that ×0.75 tax. Your own posts never appear in your For You. Nothing older than 48 hours gets in. There is no “best of last week.” Small-account bump: under 1,000 followers, under 1,000 impressions, post under 24 hours old. One original can get lifted to position 15 or 16. Not the top. You still have to already be in the top 85% of the pile. Replies and reposts from accounts the reader does not follow are dropped before scoring. You cannot reply-guy your way into a stranger’s For You. FILTERS THAT CAN STILL KILL A HIGH SCORE Ranking and visibility are different machines. A post can score well and then get deleted from the feed. Followers (in-network) is milder: blocks, mutes, suspensions. NSFW often sits behind a warning instead of disappearing. Recommendations (out-of-network) are much harsher. Spam, “Do Not Amplify,” NSFW, compromised accounts, impersonation: followers can still see it, For You recommendations will drop it. Video only gets the tiny “quality view” credit if it is at least 10 seconds. THE LITTLE-KNOWN PARTS (this is the stuff people miss) • Scrolling is not a vote. Yes/no dwell is 0. Time spent barely moves the number (0.004). • Mute damages you more than block (−58.8 vs −31.2). • Copy-link is the single biggest positive. The share button is only +2. A repost is only +1. • The mutual-follow boost is originals only, not your replies. They shipped a bigger version in July and cut it after World Cup complaints (extra reply weight 20 → 15). • The Following tab is not this algorithm. Following is newest-first. • Grok, Gork, and an internal products account are hardcoded out of the in-network store. • Scores are cached for 3 hours because posts inside the model cannot “see” each other. That is why they can rank first and filter later. • Ads, Who to Follow (around slot 7), and prompts are mixed in after ranking. Grok does not score those. • The real Grok ranking model is a 2560-dimension, 8-layer transformer looking at 1,022 items of the reader’s history. They did not ship those model weights. The numbers above are the formula sitting on top of the predictions. • SimClusters is still a 2020 model (20M users, 145k clusters). Old Twitter code, still in the path. • Inferred gender and IP/geo are on as features for the model. Installed apps too. • Who to Follow shows about every 30 hours. Ads try to sit next to “safe” posts, with a minimum gap of 3 organic posts. WHAT TO DO • Write originals people want to reply to, quote, copy, and send to a friend. • Make the post itself worth following you for. • Talk with people who follow you back. A reply on your original from a mutual is 20, not 5. • Stay inside 48 hours. Recycle by posting something new. Old posts do not come back. • If you are small, post originals in the first 24 hours. Don’t expect slot 1. A bump around 15–16 is the actual gift. • Space your posts. The second one in the same feed is already worth 37.5% less. • If you post video, make it at least 10 seconds. WHAT NOT TO DO • Do not farm likes. They are almost decorative. • Do not farm profile visits. Weight is zero. • Do not farm “time on post.” Yes/no dwell is zero. • Do not use replies and reposts as a growth hack for strangers. Those are filtered out. Even for followers they get a 25% haircut. • Do not dump several posts in a row. • Do not bait reports, mutes, or “not interested.” One predicted report can erase a pile of predicted likes. • Do not lean on NSFW or spammy tricks for reach. • Recommendations will drop you even if your followers can still see it. • Do not wait a week and expect For You to revive the post.

  12. MiniMax:Blog63

    MiniMax Music 3.0 发布:新一代开源权重、生产级全能音乐模型

    MiniMax 推出 Music 3.0,新一代音乐生成模型,可根据创意概念和可选歌词一次性完成整首歌的作曲、编曲、演奏与制作,最长支持五分钟。

    推荐理由:把简单描述扩展成包含配器进出、情绪曲线和演唱方式的专业模板,降低了非专业创作者控制音乐生成细节的门槛。

  13. MiniMax (official)44

    感谢 Huggingface 团队 @multimodalart,Day 0 支持开源音乐模型! MiniMax Music 3 刚刚在 @huggingface 上线 🎶🎵 SOTA 歌曲生成,现已开源权重,一个 8B LLM + 2.7B DiT,将提示词和歌词转化为完整歌曲 可通过 🧨 diffusers 或 comfy 在小型消费级 GPU 上运行 模型权重 + 应用已在 Spaces 上线,快来体验 ▶️ https://huggingface.co/spaces/MiniMaxAI/MiniMax-Music3

    引用apolinario (poli)@multimodalart

    MiniMax Music 3 is just out on @huggingface 🎶🎵 SoTA song generation, now open weights 🔊, a 8B LLM + 2.7B DiT that turn prompt + lyrics into full songs fits smol consumer GPUs with 🧨 diffusers or comfy model weights + app on Spaces, come play ▶️ https://huggingface.co/spaces/MiniMaxAI/MiniMax-Music3

  14. Google Developers Blog60

    Google 开源 C++ 库 Credentio,用于本地验证 C2PA Content Credentials

    Google 发布开源 C++ 库 Credentio,用于处理 C2PA Content Credentials,初期支持规范 2.2 和 2.4 版本。该代码此前驱动近 40 个符合 C2PA 标准的 Google 产品,扩展至数百亿个生成的图像、视频、音频和文档资产。

    推荐理由:Google 开源了自家验证数十亿生成资产所用的 C2PA 库,本地验证带来零带宽开销和小内存占用,对做媒体溯源工具的开发者有直接参考价值。

  15. 蚂蚁百灵:Developer Blog60

    Ling-3.0-tiny × ASystem AReno 实践:在 DGX Spark 上打通单机 Agentic RL 闭环

    蚂蚁百灵开发者博客介绍用 AReno 在 DGX Spark 上对 Ling-3.0-tiny 做井字棋 Agentic RL 后训练,验证一条基线评测、反馈定义、后训练、同环境复测的轻量任务适配路径。

    推荐理由:原文给出完整的单机 Agentic RL 后训练路径和数据与 Reward 设计细节,开发者可按教程复现并迁移到自己的任务。

8月13日周四