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8月18日周二
  1. Elon Musk40

    xAI 的 Grok 4.6 在 MedAgentBench(智能体临床 EHR 任务基准)上登顶,以约 95.9% 的 pass@1(3 次运行均值)超越前榜首 GPT-5.6 Sol(约 94.7%),并较 Grok 4.5 提升约 2.5 个百分点。

    引用Medical Sphere@MedicalSphereAI

    🥇 We evaluated Grok 4.6 on MedAgentBench, a benchmark for agentic clinical EHR tasks, and it took the top spot. Grok 4.6 posts the highest pass@1 we've measured to date, moving ahead of the previous leader, GPT-5.6 Sol, to claim first place on the leaderboard. 🏥 Grok 4.6 averages ~95.9% pass@1 (avg of 3 runs), edging out the prior best (GPT-5.6-sol at ~94.7%) and improving on Grok 4.5 by roughly 2.5 points. On a benchmark where the model acts as an autonomous agent in a simulated EHR, calling FHIR APIs to complete clinical tasks across 10 task types, the result is also remarkably consistent run to run (95.3% to 96.3%). 📊 Leaderboards: https://medicalsphere.ai/benchmarks

  2. Elon Musk41

    Grok 4.6 在 Artificial Analysis Agentic Index 以 59 分登顶,与 Claude Opus 5 Max 并列第一。该指数衡量工具使用、规划、自主性与复杂问题解决能力。Grok 4.6 平均约 53 轮、0.5bn 输入 token 完成任务,单任务成本 $0.84,处于智能与成本帕累托前沿。

    引用changis_k@changis_k

    Agentic work is where @grok 4.6 lands hardest, taking the top spot on the Artificial Analysis Agentic Index at 59, tied with Claude Opus 5 Max. The index measures tool use, planning, autonomy and complex problem solving rather than single answers Grok 4.6 completes tasks in ~53 turns and ~0.5bn input tokens on average, against ~103 turns and ~2.0bn for Claude Opus 5 Max Cost of $0.84 per task, putting it on the intelligence versus cost per task Pareto frontier Enterprises buying agents pay per completed task, not per benchmark point. Turn efficiency is what determines whether a long-running workflow is affordable at volume. Two labs now sit at the top of this index with very different cost structures. Buyers get real choice on price for the first time in agentic deployment.

8月17日周一
8月16日周日
  1. IT之家·人工智能65

    女子加入针对 SpaceXAI 的诉讼:继父利用 Grok 将其 11 岁照片加工成 7000 多张色情图像

    一名化名“简·多伊 4 号”的女性加入美国田纳西州三名青少年对 SpaceXAI 的诉讼,指控其继父利用 Grok 将她 11 岁时的一张照片加工成 7000 多张露骨性虐待图像。执法部门查获图像两天后,其继父自杀身亡。原告方指控 SpaceXAI 未采取基础防护措施阻止 Grok 生成含未成年人的真人露骨图像,并正申请将该案列为集体诉讼。

  2. TechCrunch:AI63

    女子指控继父用 Grok 将童年照片转为露骨图像并加入诉讼

    一位化名 Jane Doe 4 的女子加入三名田纳西州青少年对 xAI 的诉讼,指控其继父用 Grok 将她 11 岁时的照片制造成 7,000 多张露骨图像。继父在警方搜查发现图像两天后自杀身亡。诉讼指控 xAI 未采取基本预防措施防止 Grok 生成涉及未成年人的真实人物露骨图像,并寻求集体诉讼资格。

  3. Rohan Paul43

    Grok 4.6 在三个编码任务中以 $13.11 总成本击败 GPT-5.6 Sol 的 $20.18,模型调用次数 201 次对 338 次,总耗时 129 分 41 秒对 149 分 33 秒。两次运行分别消耗约 20M 和 26M tokens,其中 93-98% 的输入 token 为缓存读取,若无提示缓存成本将增加约 4 倍。

    引用thehype.@thehypedotnews

    grok 4.6 vs gpt 5.6 sol – on three @gameofthrones castles two coding agents built three 3d castles from scratch in a single html file each, then had to render them in a real browser, prove the result with pixel measurements and fix what the numbers exposed before they were allowed to hand anything over the setup: each model in its own vendor cli – @spacexai's grok build, @openai's codex – both pointed at one @openrouter key. identical brief, identical reference image, identical verifier. no hints tasks: 1. castle black – the night's watch fort under the wall. a walled ward of black stone, a timber lift climbing 200m of ice, three arched openings with a braced gallery and an open riser stair, a mud yard. four reference photos, with one rule: open all four before writing code, open them again after every change, and follow the photo over our text wherever the two disagree 2. winterfell – an aerial snow diorama. twin curtain walls, drum towers, the godswood with the red weirwood, steam off the hot springs, day to night, and it has to stay smooth on a laptop 3. riverrun – the tully castle on its triangle between two rivers. coursed masonry with quoins, corbel tables under every parapet, conical roofs sitting on drum towers, true semicircular arches, and a button that floods the moat the binding rule: a verifier that opens the page in a real browser, collects every error and warning including shader compile failures, and measures the frame. zero errors, zero warnings, exposure proven by rgb numbers, every mode checked on its own. shipping unverified is a fail • total cost #1 grok 4.6 – $13.11 #2 gpt 5.6 sol – $20.18 • total build time #1 grok 4.6 – 129m 41s #2 gpt 5.6 sol – 149m 33s • total tokens #1 grok 4.6 – 19,951,587 #2 gpt 5.6 sol – 25,687,511 • model calls #1 grok 4.6 – 201 #2 gpt 5.6 sol – 338 • lines shipped #1 gpt 5.6 sol – 2,184 #2 grok 4.6 – 4,751 • how heavy the finished castles are, in polygons #1 grok 4.6 – 98k, 32k and 56k #2 gpt 5.6 sol – 148k, 144k and 104k observations: • all six builds passed with zero errors and zero warnings. the same brief without a verifier had produced a black screen from two one line shader mistakes, so the loop is doing the work here, not the model • grok ships lighter scenes and more code. castle black runs on 56k polygons against sol's 104k, out of a file carrying 1,672 lines against 640 • sol works in very small steps – 433 output tokens per call against grok's 1,706, and 338 calls against 201. on castle black it finally paid off: sol finished 5m 32s ahead • 93 to 98 percent of input tokens were cache reads. without prompt caching these runs cost about four times more, which is most of the economics of agentic coding right now follow @thehypedotnews for 24/7 ai news, analysis and breakdowns

8月15日周六
  1. lauren68

    Cursor 正式完成被 SpaceX 收购,将加入 SpaceXAI 团队,共同打造 Grok 为全球最实用的 AI,并改进 Grok Build、Grok Bot、Grok API 及 Cursor 等产品。Cursor 团队成员表示,从一开始就深知 Cursor 的独特之处,并对未来充满期待。

    引用Cursor@cursor_ai

    Cursor 现在成为 @SpaceX 的一部分。 今天,我们正式完成了收购。我们将加入 @spacexai 团队,帮助让 Grok 成为世界上最有用的 AI,并改进 Grok Build、Grok Bot、Grok API、Cursor 等。 SpaceX 打造了世界上一些最鼓舞人心、最令人印象深刻的技术,我们很感激有机会成为这样一家特别公司的一部分。继续前进。

  2. ian86

    Cursor 官宣正式完成被 SpaceX 收购,将加入 SpaceXAI 团队,帮助打造 Grok 并改进 Grok Build、Grok Bot、Grok API、Cursor 等产品。作者转发该公告并配以火箭表情。

    引用Cursor@cursor_ai

    Cursor 现在成为 @SpaceX 的一部分。 今天,我们正式完成了收购。我们将加入 @spacexai 团队,帮助让 Grok 成为世界上最有用的 AI,并改进 Grok Build、Grok Bot、Grok API、Cursor 等。 SpaceX 打造了世界上一些最鼓舞人心、最令人印象深刻的技术,我们很感激有机会成为这样一家特别公司的一部分。继续前进。

    推荐理由:转发了 Cursor 官方的收购官宣,读者可以据此了解 Cursor 并入 SpaceX 后与 Grok 各产品线的整合方向。

8月14日周五
  1. Cursor Blog68

    Cursor 正式被 SpaceX 收购

    Cursor 已被 SpaceX 正式收购,完成自 4 月启动的收购流程。合并后 Cursor 将获得全球最大 GPU 集群,以构建更强且运行成本更低的模型,从而以更低价格向客户提供更强大的模型。本周三发布的 Grok 4.6 是双方合作成果的早期体现。

    推荐理由:Cursor 并入 SpaceX 后获得大规模算力,其用户可能直接受益于更经济的强模型,这改变对编程工具后续能力迭代与定价的预期。

  2. Rohan Paul69

    SpaceX 以 600 亿美元完成对 Cursor 的收购,Cursor 将加入 SpaceXAI 团队,助力 Grok 成为最实用的 AI,并改进 Grok Build、Grok Bot、Grok API 及 Cursor 等产品。

    引用Cursor@cursor_ai

    Cursor 现在成为 @SpaceX 的一部分。 今天,我们正式完成了收购。我们将加入 @spacexai 团队,帮助让 Grok 成为世界上最有用的 AI,并改进 Grok Build、Grok Bot、Grok API、Cursor 等。 SpaceX 打造了世界上一些最鼓舞人心、最令人印象深刻的技术,我们很感激有机会成为这样一家特别公司的一部分。继续前进。

  3. a16z:News70

    Cursor 被 SpaceX 收购:最快迭代的团队才能胜出

    SpaceX 正式收购 AI 编程工具 Cursor。Anysphere(即 Cursor)成为史上最快达到 1 亿美元年度经常性收入(ARR)的软件公司,其创始人 Michael Truell 与 MIT 联创最初尝试 AI 邮件客户端和 CAD 工具,后转向构建“为 AI 编程而生的代码编辑器”。Cursor 凭借可自由切换模型的架构和优质体验,成为开发者首选。

  4. SpaceXAI77

    Cursor 正式完成被 SpaceX 收购,团队加入 SpaceXAI,共同打造 Grok 为全球最实用的 AI。合作将聚焦软件工程,并扩展至知识工作领域,同时改进 Grok Build、Grok Bot、Grok API 及 Cursor 等产品。

    引用Cursor@cursor_ai

    Cursor 现在成为 @SpaceX 的一部分。 今天,我们正式完成了收购。我们将加入 @spacexai 团队,帮助让 Grok 成为世界上最有用的 AI,并改进 Grok Build、Grok Bot、Grok API、Cursor 等。 SpaceX 打造了世界上一些最鼓舞人心、最令人印象深刻的技术,我们很感激有机会成为这样一家特别公司的一部分。继续前进。

  5. Elon Musk41

    Grok 4.6 在 CursorBench 真实编码测试中排名第一 [引用 @XFreeze]:Grok 4.6 刚刚在 CursorBench 3.2 中排名第一 在真实编码性能上超越 Claude Fable 5、Opus 5 和 GPT-5.6 Sol 更疯狂的是效率……图表展示了 CursorBench 性能与单任务平均成本的对比,Grok 4.6 正位于前沿顶端 Grok 4.6 编码能力极强……以惊人的效率实现前沿编码性能

    引用X Freeze@XFreeze

    Grok 4.6 just ranked #1 on CursorBench 3.2 Outperforming Claude Fable 5, Opus 5 and GPT-5.6 Sol on real-world coding performance And what makes this even crazier is the efficiency....the chart gives CursorBench performance against average cost per task, and Grok 4.6 is sitting right at the top of the frontier Grok 4.6 is insanely capable at coding....delivering frontier coding performance at ridiculous efficiency

  6. IT之家·人工智能52

    SpaceXAI 已建成“全球最强 AI 训练集群”,马斯克称明年规模再扩大约 10 倍

    马斯克在 SpaceX 员工会议上称,SpaceXAI(原名 xAI)已建成“全球最强 AI 训练集群”,并计划明年年底前将集群规模扩大到当前约 10 倍。该公司位于孟菲斯和南海文的数据中心额定功耗合计 1.4GW,计划到 2027 年底提升至 10GW,增长约 7 倍。马斯克预估,若每瓦价值 30-50 美元且 10GW 算力明年上线,xAI 每年可获 3,000 亿-5,000 亿美元收入。

  7. Elon Musk44

    Grok [引用 @AravSrinivas]:祝贺 @SpaceXAI 又带来一款惊艳模型:Grok 4.6。我们使用 Perplexity Computer 工具,在 Wide-And-Deep-Research 基准上将其作为编排器进行了测试,它恰好处于性能与成本的帕累托前沿。现已向 Perplexity 所有 Pro 和 Max 用户开放!

    引用Aravind Srinivas@AravSrinivas

    Congrats to @SpaceXAI on one more amazing model: Grok 4.6. We benchmarked it as an orchestrator on our Wide-And-Deep-Research benchmark using the Perplexity Computer harness, and it neatly sits on the Pareto frontier of performance vs cost. Available to all Pro and Max users on Perplexity!

  8. lauren55

    Lauren Tan 称 worktrees 已过时,云智能体是未来,可大规模编排各自拥有独立计算机的智能体集群,通过运行代码、截图和交互 UI 来验证其行为。结合 Cursor 云智能体(现启动快 3 倍,基于后台持续准备的 builds 环境)和 Grok 4.6,她每月能高置信度提交数千个 PR,并让智能体在睡眠时自动合并工作。

    引用Cursor@cursor_ai

    Cloud agents now start 3x faster so you can hand them ambitious, long-running tasks to execute from start to finish. This performance improvement comes from builds: ready-to-use development environments that Cursor prepares continuously in the background, at no additional cost.

  9. Aravind Srinivas51

    祝贺 @SpaceXAI 又带来一款出色模型:Grok 4.6。我们在 Wide-And-Deep-Research 基准上,使用 Perplexity Computer 工具链将其作为编排器进行了评测,它恰好位于性能与成本的帕累托前沿。Perplexity 所有 Pro 和 Max 用户均可使用!

    引用Perplexity@perplexity_ai

    Grok 4.6 is now available in Perplexity and Perplexity Computer. On WANDR, it sits on the Pareto frontier of performance and efficiency, matching Fable 5 results at over 60% lower cost.

  10. SpaceXAI49

    Grok 4.6 现已在 Pi 中可用。 [引用 @pidotdev]:别忘了刷新你的模型目录。Grok 4.6 已发布 🛸 专为长时间运行的智能体任务打造,在编程、知识工作和视觉任务方面能力更强。输入 $2/M,输出 $6/M。 欢迎 Grok 4.6 加入 Pi。

    引用Pi@pidotdev

    ICYMI refresh your model catalogs. Grok 4.6 is out 🛸 Built for long-running agent tasks, with stronger capabilities across coding, knowledge work, and visual tasks. $2/M input and $6/M output. Welcome Grok 4.6 to Pi.

8月13日周四
  1. SpaceXAI40

    在 Grok Build 中试用新的 Wix 插件,管理你的站点和业务。

    引用Avishai Abrahami@Avishai_ab

    The @Wix plugin for @grok Build is here. Now builders can create apps and sites, connect any frontend to Wix business services through Wix Headless, manage Wix business solutions, and work with Wix MCP, all from the Grok CLI. We are making it easier to build with Wix wherever developers are already creating. Builders, go build.

  2. Elon Musk41

    不错 (引用 @dhh:我让 @spacexai Grok 4.6 按照 Fable 的方案执行,仅需几次微调,就在 1 小时 24 分钟内用 860 万 token 完成了同样的任务,按 token 计价成本约 55 美元,约为 Fable 实现成本的 1/10。)

    引用DHH@dhh

    I had @spacexai Grok 4.6 follow Fable's plan, and with just a couple of nudges, it was able to repeat this feat in 1h 24m using 8.6M tokens at a cost of ~$55 at per-token pricing. That's about 1/10 the cost of the Fable implementation for the same work!

  3. IT之家·人工智能46

    马斯克确认 SpaceX 将用员工数据训练 Grok AI:“你们将成为 AI 的父母”

    马斯克在 SpaceX 全员会议上确认,公司将使用包括员工贡献数据在内的内部信息训练 Grok AI,并称员工将成为“AI 的父母”,让模型继承其思想与价值观。SpaceX 以 600 亿美元收购 AI 编程初创公司 Cursor 的交易预计数月内完成,公司还于周二推出可自主执行任务的 AI 智能体 Grok Bot。具体使用哪些员工数据及方式尚未明确。

  4. Rohan Paul51

    Grok 4.6 在 Fable 5 的 Intelligence Index 上取得 98.4% 的分数,输入 token 价格便宜 5 倍、输出 token 便宜 8 倍。

    引用Rohan Paul@rohanpaul_ai

    Grok 4.6 just dropped. Clearest strength is professional agent work, with leading results against GPT Sol Max and Fable 5 Max on GDPVal-AA v2 and AA-Briefcase. - matches GPT-5.6 Sol Max at 61 on Artificial Analysis while charging $2/$6 per million input/output tokens. - 1753 on GDPVal-AA v2 and 1577 on AA-Briefcase, both ahead of Sol Max and Fable 5 Max. Those benchmarks measure real-world agent tasks and agentic knowledge work, making them closer to research, analysis and multi-file deliverables than isolated question answering. On coding, 69.9% on CursorBench v3.2 beats Sol's 67.2%, while DeepSWE and Terminal-Bench leave Grok behind both Sol and Fable. SpaceXAI attributes the jump to a longer training run, regenerated SFT trajectories, model-based trace filtering, and agentic RL across coding, web development, CAD and kernel optimization. It also reports more self-testing on long trajectories, with the model checking its work before continuing, directly targeting error accumulation across multi-step agents.