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关注 AI 研究者、开发者与机构的动态
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@rohanpaul_ai@rohanpaul_aiAI 评分55 @opencode@opencodeAI 评分4444 @rohanpaul_ai@rohanpaul_aiAI 评分44 抱歉,您提供的主推文内容仅包含一个链接(https://t.co/YOouY1la59),没有可翻译的正文文字。请提供推文的实际文字内容,我将为您翻译。
@rohanpaul_ai@rohanpaul_aiAI 评分5656 
@alexandr_wang@alexandr_wangAI 评分44 @alexandr_wang@alexandr_wangAI 评分44 @alexandr_wang@alexandr_wangAI 评分22 @alexandr_wang@alexandr_wangAI 评分77 @alexandr_wang@alexandr_wangAI 评分33 @alexandr_wang@alexandr_wangAI 评分77 @openclaw@openclawAI 评分1515 @AYi_AInotes@AYi_AInotesAI 评分3737 @AYi_AInotes@AYi_AInotesAI 评分4646 斯坦福最火的 AI 编程课讲师用 9 个月把去年 85% 的教材扔进垃圾桶,原因是底层模型跨过门槛后编程 Agent 出现断层式能力跃迁,教学逻辑从"用对话框辅助写代码"转向"开一家全自动软件工厂"。
@fchollet@fcholletAI 评分66 @alexandr_wang@alexandr_wangAI 评分5454 引用@ArtificialAnlys@ArtificialAnlysMeta's Muse Spark 1.3 (max), which is in limited preview for Meta's partners, scores 68 on the Artificial Analysis Coding Agent Index in the Muse Code harness, #2 behind only Claude Opus 5 (xhigh) in Claude Code. The variant available now, Muse Spark 1.3 (xhigh), scores 64 and costs the least per task of any agent above a 60 index score Muse Spark 1.3 (xhigh) enters the Artificial Analysis Coding Agent Index at 64 in Muse Code, up 2 points from Muse Spark 1.2 (62, August). It enters level with Grok 4.5 (high) in Grok Build (64) and behind GPT-5.6 Sol (max) in Codex (65). At $1.72 per task, it costs the least of any agent above a 60 index score, around a fifth of the cost of Claude Opus 5 (xhigh) in Claude Code ($8.17) Muse Spark 1.3 (max), which is in a limited preview stage, lands at 68 in Muse Code. It enters behind only Claude Opus 5 (xhigh) in Claude Code (68), and ahead of Claude Fable 5 (max) in Claude Code (67) and GPT-5.6 Sol (max) in Codex (65). Muse Spark 1.3 (max) is excluded from cost comparisons as Meta has not announced pricing for the limited release Claude Fable 5.1 results are in progress and will be added when complete. Congratulations @AIatMeta, @finkd, and @alexandr_wang on this result!
@alexandr_wang@alexandr_wangAI 评分77 @alexandr_wang@alexandr_wangAI 评分55 @alexandr_wang@alexandr_wangAI 评分2222 @ArtificialAnlys@ArtificialAnlysAI 评分66 @ArtificialAnlys@ArtificialAnlysAI 评分4444 


@ArtificialAnlys@ArtificialAnlysAI 评分5757 
@alexandr_wang@alexandr_wangAI 评分1313 @alexandr_wang@alexandr_wangAI 评分1717 @SemiAnalysis_@SemiAnalysis_AI 评分3737 
@Replit@ReplitAI 评分3636 
@rohanpaul_ai@rohanpaul_aiAI 评分4545 
@rohanpaul_ai@rohanpaul_aiAI 评分44 @alexandr_wang@alexandr_wangAI 评分1010 @alexandr_wang@alexandr_wangAI 评分22 @ArtificialAnlys@ArtificialAnlysAI 评分4747 @ArtificialAnlys@ArtificialAnlysAI 评分2828 
@ArtificialAnlys@ArtificialAnlysAI 评分3636 
@ArtificialAnlys@ArtificialAnlysAI 评分3535 
@ArtificialAnlys@ArtificialAnlysAI 评分1616 
@ArtificialAnlys@ArtificialAnlysAI 评分1212 
@ArtificialAnlys@ArtificialAnlysAI 评分5656 
@thexpin@thexpinAI 评分00 @thexpin@thexpinAI 评分1010 抱歉,主推文内容仅包含一个链接(https://t.co/Kumrji0G6u),没有可翻译的正文文字。请提供推文的实际文字内容,我再为你翻译并拟标题。
@omarsar0@omarsar0AI 评分4444 一篇论文提出将智能体拆分为持久本体与可替换基础设施两半:身份、私有记忆和带版本历史的代码属于智能体本身,推理模型、运行框架、托管服务器和交互入口(chat、API、UI)则可替换。

@cb_doge@cb_dogeAI 评分1717 Cybercab 停在 Tesla 工程总部前,帕洛阿尔托 https://t.co/AC34Dyfa0j
