adon

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[adon @F5Adon]

@zeng98518 说好一切却不肯陪诊

[adon @F5Adon]

@sekurimrs 洗完一只袖子翻里头

[adon @F5Adon]

@mengmengair 检查项目说清才有用

[adon @F5Adon]

@Baiqi79 努力未必有果却不敢停

[adon @F5Adon]

@takahashi468 没必要的消费省下了

[adon @F5Adon]

@boylikegirl666 凌晨过后已是新一天

[adon @F5Adon]

@ethean666 认真后悔又重新开始

[adon @F5Adon]

@JingchunYi22950 屏幕时间耗在反复刷新

[adon @F5Adon]

@routeking_ 多等一分钟也没什么

[adon @F5Adon]

@JMkernt 是啊

[adon @F5Adon]

震惊，同在这份榜单前十，做一项基准任务的成本竟然差了13倍以上。

智能指数 v5 公布的10个最高分模型里，GPT-6.1 Terra（max）每项指数测试任务0.96美元，Claude Fable 5.1（max）13.04美元。

拿榜单选模型，别只盯排名。成本足以改变产品能不能跑得起。把自己的任务成功率、成本和延迟一起测，才能算清每次有效交付花了多少钱。

[Artificial Analysis @ArtificialAnlys]

Announcing Artificial Analysis Intelligence Index v5, featuring 2 new benchmarks and 3 upgrades. For the first time, the majority of the Index is built on private datasets

The Artificial Analysis Intelligence Index is our composite measure of model capabilities across tasks reflecting real-world usage today. v5 is our biggest upgrade since v4 launched in January. With v5, 60% of the Index is now based on evaluations with private test sets or held-out answers.

Alongside Intelligence Index v5, we’re launching the Artificial Analysis Progress Index v1, which tracks model capability over time on one continuous scale.

New to the Intelligence Index:

➤ FrontierCode 2.0: Cognition's newly-launched private benchmark of real-world coding tasks, graded on whether a repository maintainer would merge the agent's change

➤ Terminal-Bench-Science: 70 agentic scientific research tasks, run in a terminal environment

Upgraded:

➤ HLE-Diamond (replacing Humanity's Last Exam): a refined 1,000-question version of Humanity's Last Exam, split evenly between reasoning and knowledge questions

➤ AA-Omniscience v1.1: An updated dataset with saturated and public set questions removed, new questions added, and ambiguous or erroneous questions revised

➤ CritPt v2: a private, 40-question hard subset of CritPt v2, revised by the CritPt authors and run with a code-execution tool

Staying in the Index: AA-Briefcase, AutomationBench-AA, GDP.pdf and Terminal-Bench 4.0

Removed from the Index: GDPval-AA v2.1, SciCode, and AA-LCR v1.1

Key results:

➤ Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55): Anthropic and OpenAI hold the top five models. Gemini 4 Argon (high) is next at 50

➤ The open weights gap widens from 3 points to 12: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 46. Among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead at 32 - 26 points behind the leader and below Claude Haiku 5.5 (max, 36)

➤ Cost per task varies by more than 13x among the leading models: of the 10 highest-scoring models shown, GPT-6.1 Terra (max) is the cheapest at $0.96 per Intelligence Index task and Claude Fable 5.1 (max) the most expensive at $13.04

[adon @F5Adon]

@mushanchu1 一声您也能表达尊重

[adon @F5Adon]

@z2810358647 全靠原创也可能更焦虑

[adon @F5Adon]

@JMkernt 可以学

[adon @F5Adon]

太实用了，这段89分钟 Stanford 课，直接拆开 Transformer 的结构图：位置编码、前馈网络、归一化各管什么，再看后来模型改动了哪里。

只盯着 Attention，很容易把其余设计当成默认配置。把这些选择连起来，读论文和开源模型代码时，才知道自己正在比较哪一层。课里的公式和结构，适合边看边做一个最小实现。

[adon @F5Adon]

@barrett_tt 脚步制造迟疑更耐看

[adon @F5Adon]

@luojiedev 同类推荐却少了二十抽

[adon @F5Adon]

@fang883688 两边都有煮和烤的面食

[adon @F5Adon]

想放弃的时候，就会重看这段视频。
2008年的马斯克，在别人客厅里卖10.9万美元的车。

不是什么豪华展厅，没有销售团队，没有流量，没有预算。

公司12天就要破产，他自己几乎身无分文，靠借钱维持公司运转。

对面坐着的人可以随时起身离开，他却只能硬着头皮继续讲。

没人把他当回事，没人觉得这辆车能卖出去，更没人觉得他能活过那个冬天。

从客厅里被无视的销售，到全球首富。

中间隔了十八年，和无数次差点撑不下去的夜晚。

所以别太早放弃自己还在拼命推销的那件事。

[adon @F5Adon]

@Ianwong1002 一晃眼日子就过去了