
全部 AI 动态
Google@GoogleAI 评分4242
Sundar Pichai@sundarpichaiAI 评分4242引用Google DeepMind@GoogleDeepMindGemini 4 Argon built a 3D periodic table synced to Tom Lehrer’s song, “The Elements.” Created in @Antigravity, it animates orbiting electrons, atomic data, and light spectra.
Higgsfield AI 🧩@higgsfieldAI 评分5252
Rohan Paul@rohanpaul_aiAI 评分5454
引用Anusha Ramesh@anurahkI launched the first image and video generation models at Google, and kept asking why all AI designs look the same. AI has brought us the WORST era of design. It made content creation effortless, but it also made 90% of it look painfully generic. My co-founder and I spent 2 years building in stealth to change that. Meet Vyb, the first AI design model that understands your taste like a designer would. RT + comment your website URL and we’ll make your first design.
MiniMax (official)@MiniMax_AIAI 评分4040感谢 Parallel Web System 提供如此有意义的数据点~ 是的,MiniMax M3.1 Flash 现已在搜索能力排行榜的效率维度上位列第一。而且它很快~
引用Parallel Web Systems@p0MiniMax M3.1 Flash is now #1 on our search capabilities leaderboard for efficiency. It delivers web research performance with Parallel Search similar to DeepSeek V4.1 Flash (1017) at ~3x lower cost.
Arena.ai@arenaAI 评分6060引用Arena.ai@arenaExciting news: Muse Wonder Full (Max) by @AIatMeta has just debuted in the Code Arena: WebDev at #6 overall with 1726 pts, putting Meta in the top 3 labs! This is +75 pts and 11 places above Muse Wonder (Max) at #17 (1651 pts), and +67 pts and 8 places above Muse Spark 1.3 (Max) at #14 (1659 pts). Muse Wonder Full (#6) also landed +16 pts above Claude Sonnet 5.5 (#17), and +48 pts above Gemini 4 Argon (#10). With this score, Muse took the #3 lab spot (previously held by Google). Across categories, Muse Wonder Full (Max) landed: - #4 in Data & Analytics and in Consumer Product - #5 in Brand & Marketing - #6 in Content Creation Tools - #7 in Gaming and in Simulations - #8 in Reference-Based Design Congrats to the @AIatMeta team and @alexandr_wang on this release!
Rohan Paul@rohanpaul_aiAI 评分5454SoMark 团队发布开源文档解析模型 OXR(1.3B 参数),将 PDF 和文档图像转为含布局坐标的 Markdown 或 JSON 的 Agent-Friendly 数据。
引用SoMark@somarkaiBeyond OCR. Meet OXR. Only 1.3B. Open source. #1 on olmOCR-Bench, OmniDocBench & DocParsingBench🏆 #1 in parsing speed among open-source models⚡ RSI-powered. A first in document parsing. Turn documents into Agent-Friendly data. If OXR helps, give us a star on GitHub! 🌟
Mark Chen@markchen90AI 评分5454引用OpenAI@OpenAIWe’ve acquired Orchestra Bio, a platform that helps biopharma teams coordinate the complex process of drug development. Together, we’ll build on Rosalind, our AI models and tools for life sciences research, to streamline those operations and help new treatments reach patients sooner. Welcome @ashokaraj_ and team! https://openai.com/index/openai-to-acquire-orchestra-bio/
Rohan Paul@rohanpaul_aiAI 评分4848引用HeyGen@HeyGenProfessional Voice Clone on HeyGen sounds exactly like you. Upload 30 min to 2 hrs of audio and get a voice no one can tell apart from your own. Every breath, pause, laugh, and accent, preserved. Comment "PVC" for a free month. First 100, web subscriptions only, ends 11/19.
ViggleAI@ViggleAIAI 评分4646一个模型即可生成 3D 角色、动作和场景。 JST-2 现已在我们的 API 上线。 今天就开始构建:https://viggle.ai/developers
引用ViggleAI@ViggleAIToday we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.
Rohan Paul@rohanpaul_aiAI 评分6969引用Anthropic@AnthropicAINew on our Science Blog: designing protein binders against scorpion neurotoxins. Scorpions sting about 1.2m people every year, and antivenoms are often costly or in short supply. Claude has made an early step toward finding new treatments. https://www.anthropic.com/research/claude-scorpion-toxin-binder-design
OpenAI Developers@OpenAIDevsAI 评分5656
Elon Musk@elonmuskAI 评分4949“电力不足”的说法过于简化了。要以极快速度上线大规模算力,每个环节都很难。 如果有两倍的时间,这会相对容易,但并没有。
引用Rihard Jarc@RihardJarcFormer $GOOGL employee on why data center capacity and electricity are the real bottlenecks, not GPU production itself, and how unreliable some of the neoclouds are ($AMZN, $GOOGL, $MSFT, $NVDA): 1. According to the expert, open-source models are closing the gap with frontier models faster than expected, with customers increasingly replacing Anthropic and OpenAI models in multi-agent architectures with open-source alternatives starting from Q2 of this year. The shift started with simple tasks like tool calling and MCP, but has moved up the stack to low-level orchestration. 2. The expert identifies capacity as the biggest constraint at the moment, with certain companies' traffic growing 5x while GPU availability, particularly in the U.S. where latency requirements are tightest, simply cannot keep up. Sourcing capacity overseas in Asia or Australia is technically possible but adds latency that most customers will not accept. 3. Hyperscalers offer reliability but are expensive, require multi-year commitments of three or more years, and have limited availability of the latest hardware, with customers already chasing B300s. AI-native companies tend to gravitate toward neoclouds due to hyperscaler pricing and contract rigidity, but neoclouds come with their own reliability issues, with GPUs dropping off unexpectedly and requiring engineering resources to manage fleet stability around the clock. 4. The third tier consists of smaller independent operators who have invested in GPU racks, seeing the opportunity to lease out capacity and recoup costs within a year or two, with the remainder being pure profit. The expert describes these as essentially unusable in practice, with operators who do nothing more than find land, install racks, connect electricity, and set up cooling, with no real cloud management, maintenance, or operational expertise behind them. 5. The expert expects overall GPU availability to increase meaningfully over the next year or two, with $NVDA continuing to ramp production. The real bottleneck is not GPUs themselves but data center capacity and electricity in the U.S. On the supply side, the expert also sees players $SPCX beginning to lease out spare capacity to the market, with more likely to follow. 6. The expert notes that U.S.-based neoclouds typically require 20-30% prepayment upfront, with the remainder paid monthly or quarterly over the contract term, and rebates negotiated at the end for any reliability incidents like traffic loss. APAC-based providers operate differently, with no prepayment required at all. found on @AlphaSenseInc
Rohan Paul@rohanpaul_aiAI 评分5252引用Shadeform@shadeformaiSome of the most in-demand GPUs on Earth are sitting idle right now, and the teams that paid for them are just eating those costs. Today, we’re launching Flex VMs + Shadeform Spot to fix that👇
Aravind Srinivas@AravSrinivasAI 评分5858Perplexity CEO Aravind Srinivas 转引 Engadget 报道称,Perplexity AI 工作流现在可以在 Mac 上完全本地运行,要求配备至少 36GB 内存。
引用Engadget@engadgetA Mac with at least 36GB of RAM can now run Perplexity AI workflows locally. https://www.engadget.com/2291754/perplexity-makes-it-easier-to-run-ai-workflows-entirely-on-your-mac/?utm_term=Autofeed&utm_campaign=Echobox-Engadget&utm_medium=Social-Distribution&utm_source=Twitter#Echobox=1792511300
Arena.ai@arenaAI 评分5757引用Arena.ai@arenaUpdate to the Agent Arena Pareto frontier: GPT-6 Luna (Max) has landed on the Agent Arena Pareto frontier with +1.59% net improvement at a median cost of just $0.05 per task! Luna costs 29% less than DeepSeek-V4.1-Flash and 76% less than Hy4 preview, while trailing their net-improvement scores by just 2.27 and 2.51 percentage points respectively. Compared to other GPT models on the Pareto frontier, GPT-6 Luna (Max) costs 94% less than GPT-6 Sol (Max), and 98% less than GPT-6 Astra (Max). Congrats to the @OpenAI team on this cost-efficient release!
fal@fal精选AI 评分6868引用fal@fal隆重推出 FLUX 3 Max,这是 fal Research 与 @bfl_ai 合作推出的最新后训练视频模型。 在独立第三方基准测试中,FLUX 3 Max 在图生视频质量上并列第一,可在约 12 秒内生成一段 10 秒、720/768p 的视频。 在接下来的三周内,FLUX 3 Max 享受 75% 折扣,使其成为同等质量水平下最实惠的前沿视频模型。
推荐理由:原文给出第三方 Elo 分数和生成速度数据,读者可据此判断该模型在图生视频梯队中的位置。
Tibo@thsottiauxAI 评分5353Codex 团队回应部分用户周末额度消耗异常的反馈,确认约 40,000 个账号实际执行的用量限制与其所选套餐不一致,已修正该问题并逐一联系受影响用户,将为其账户补加额度,并向用户致歉。
Rohan Paul@rohanpaul_aiAI 评分5151
引用General Compute@general_computeOur heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner. We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands. Hardware goes live Q1 2027. Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership. LFG!
Arthur Mensch@arthurmenschAI 评分4949
OpenRouter@OpenRouterAI 评分5858引用Mistral AI@MistralAIIntroducing Voxtral 3: state-of-the-art Transcribe and Speech models powering Mistral’s new voice agents API. 🧵
Elon Musk@elonmuskAI 评分5252引用matt palmer@mattypWhat's new in Grok Bot 00:00 New chat cards 00:31 Speed 00:53 Draft edits 01:08 Email aliases 01:33 X DMs 01:55 Tag @bot 02:31 Connectors 02:48 Voice calls 03:27 Notifications 03:56 Invites and Android Assistant 04:19 Access lists, unread, routines, sign-in This team ships!
Artificial Analysis@ArtificialAnlysAI 评分6464Artificial Analysis 分析 Intelligence Index 各阈值达成的成本变化:达到 30 分如今每任务 0.03 美元(GPT-6 Luna high)。

Artificial Analysis@ArtificialAnlysAI 评分6060Artificial Analysis 评测了 Mistral 发布的 Voxtral 3 语音模型,包括 1 个 TTS 模型和 3 个语音转文本模型。
Aravind Srinivas@AravSrinivas精选AI 评分6565引用Perplexity@perplexity_aiPerplexity Computer 现在可以在你的 Mac 上完全本地运行。 与你的原生 Mac 应用和本地文件协同工作,运行任务时无需将敏感数据发送到云端。模型和智能体都在你的 Mac 上运行。 现已在 Perplexity Mac 应用中提供。
推荐理由:Perplexity CEO 亲自宣布,Perplexity Computer 可完全本地运行,敏感数据无需上云,对关注隐私和离线使用的人有参考价值。
Artificial Analysis@ArtificialAnlysAI 评分5757
Artificial Analysis@ArtificialAnlysAI 评分5959


