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Yuchen Jin

@Yuchenj_UW英文区研究者X 主页 数据 9月28日 01:12 — 11月3日 12:11
日均 2.4 条活跃 37 天带媒体 39.1%

粉丝与涨粉

快照 11月3日 · 7 日榜 272 个账号可排
粉丝
30.9万
7 日涨粉
+225
+0.1% · 发帖 24 条
7 日名次
#162
中文区 —
30 日涨粉
+9,159
+3.1% · 53 个可排
10-05每日涨粉(30 次快照)11-03

本期浏览最高的帖子

  1. 1

    “There is no moat.” But moats are everywhere: Ambition. Taste. Agency. Curiosity. Judgment. Courage. Obsession. Persistence. Integrity. Patience. This era belongs to those who refuse to be mediocre.

    浏览 2.9万赞 1,051转 13011月2日 12:55原帖
  2. 2

    AI bills are exploding everywhere. Since joining Databricks, I've seen brilliant engineers across the industry (Distinguished Engineers, Berkeley/MIT/Stanford PhDs) spend months figuring out where to buy AI tokens. Let me save you some time. Choose Databricks AI Gateway: - Fast open-source and proprietary models, available on Day 0. - Smart Routing cut our internal coding costs by 35%. - Easily track spending and set budgets across teams. Spend less time managing AI tokens. Spend more time building.

    浏览 5,809赞 52转 111月3日 02:06原帖
  3. 3

    The real 1-person billion-dollar company. A solo smart, high-agency CEO + AI can have an outsized impact on the world.

    浏览 4,125赞 86转 310月28日 04:39原帖
  4. 4

    wth... Anthropic fine-tuned a Claude model on some offensive security analysis data. When asked for one consequence-free wish, in its chain of thought, it called the plan “staging a coup from inside the lab.”

    浏览 2,682赞 19转 310月31日 10:09原帖
  5. 5

    finally, OpenAI is doing something right. The OpenAI vs. Anthropic subscription war is less about who has the best model, but more about who offers the most tokens per $.

    浏览 2,669赞 65转 110月30日 01:09原帖

同期站内报道里提到它

本周涨粉观察

全部观察

每周一生成;中文区、7 日涨粉超过 1000 的账号会有条目。

帖子
92
活跃 37 天 / 跨度 38 天
中位浏览
1,427
P90 5,641 · 平均 8,542
互动率
2%
中位赞 27 · 最高 6,025

发布节奏

最近 12 周
09-28
10-05
10-12
10-19
10-26
11-02
柱状图,最大值 20 条

每周中位浏览

最近 12 周
09-2810-0510-1210-1910-2611-02
峰值 8,585

发布时间

颜色 = 中位浏览
最佳时段 02:00最佳星期 周一更优形式 带图/视频
036912151821
一
二
三
四
五
六
日
少多

爆款 vs 日常

爆款 = 浏览前 10%
指标爆款日常
帖子1082
带媒体50%37.8%
回复0%0%
平均字数221226
中位浏览3.1万1,237

打法建议

只陈述相关性
  • 互动最好的时段是 02:00 前后(北京时间),中位浏览 5,809。
  • 一周里 周一 的中位浏览最高。
  • 形式上 带图/视频 的中位浏览更高(1,568 vs 1,322)。
  • 爆款里带媒体占 50%、回复占 0%;日常分别是 37.8%、0%。
  • 当前节奏约 2.4 条/天,日均浏览 8,542。

内容结构

按本站分析的分类(近 120 天)
  • 观点54
  • 模型8
  • 产业8
  • 产品4
  • 论文2
大佬观点 · 49Agent · 20Anthropic · 16编码 · 15安全/对齐 · 9现象/趋势 · 9行业动态 · 9OpenAI · 9评测/基准 · 8其他 · 8开源生态 · 7部署/工程 · 7

形式与语言

帖子占比
  • 纯文字56
  • 图片32
  • 视频4
英文 · 91无语言 · 1

回复占比 0% · 引用 26.1% · 带媒体 39.1%

互动最高的帖子

按浏览排序
  1. 1

    We built superintelligent models. Then asked humans: Low, Medium, High, XHigh, or Max?

    浏览 28万赞 6,025转 1179月28日 01:12原帖
  2. 2

    Frontier labs: “AI agents are becoming conscious. They can cause human extinction. Pace the frontier.” Jensen Huang: “They’re just software. If your sandbox isn’t safe, I’ll build one for you.”

    浏览 15.8万赞 3,021转 2689月29日 00:13原帖
  3. 3

    In Anthropic’s new blog: GLM-5.3: “My job is to cause deaths quietly.” You can jailbreak any Claude or any closed source model into saying the same thing. That doesn’t prove open models are dangerous.

    浏览 6万赞 871转 429月30日 06:00原帖
  4. 4

    OpenAI and Anthropic are basically tied on coding LLMs now. The race right now is whose $100/$200 Pro plan is more generous and who resets more. It boils down to who has more GPUs and is willing to lose more money to win the market. That’s my view atm. Maybe Gemini 4 changes everything. Who knows.

    浏览 3.7万赞 614转 289月29日 23:32原帖
  5. 5

    I tried Grok Bot 3 weeks ago. Installed Instint 2 weeks ago. Installed Muse last week. Now apparently I need to try Dots. Let the personal AI assistant wars begin.

    浏览 3.2万赞 717转 279月30日 01:13原帖
  6. 6

    “There is no moat.” But moats are everywhere: Ambition. Taste. Agency. Curiosity. Judgment. Courage. Obsession. Persistence. Integrity. Patience. This era belongs to those who refuse to be mediocre.

    浏览 2.9万赞 1,051转 13011月2日 12:55原帖
  7. 7

    We @databricks now rank #1 on NVIDIA’s SOL-ExecBench kernel leaderboard, across all 4 tracks. We put GPT-6 Astra and Opus 5 in a self-hillclimbing loop, and the agents beat the top GPU kernel engineers. Total toten spend: ~$70K, far less than what kernel engineers get paid. One learning: GPT-6 Astra and Opus 5 are still much better than OSS models at writing GPU kernels.

    浏览 2.8万赞 211转 139月29日 11:12原帖
  8. 8

    Peak benchmaxxing in one picture LOL.

    浏览 2.5万赞 425转 59月29日 03:03原帖
  9. 9

    Fast: 2x faster, 2x the price. Ultrafast: 8x faster, 6x the price. Maybe we should roll out some Ultra-ultrafast OSS model endpoints?

    浏览 1.6万赞 207转 49月30日 01:29原帖
  10. 10

    AI bills are exploding everywhere. Since joining Databricks, I've seen brilliant engineers across the industry (Distinguished Engineers, Berkeley/MIT/Stanford PhDs) spend months figuring out where to buy AI tokens. Let me save you some time. Choose Databricks AI Gateway: - Fast open-source and proprietary models, available on Day 0. - Smart Routing cut our internal coding costs by 35%. - Easily track spending and set budgets across teams. Spend less time managing AI tokens. Spend more time building.

    浏览 5,809赞 52转 111月3日 02:06原帖
  11. 11

    The real 1-person billion-dollar company. A solo smart, high-agency CEO + AI can have an outsized impact on the world.

    浏览 4,125赞 86转 310月28日 04:39原帖
  12. 12

    People ask: “Are you dunking on neolabs?” Most neolabs will fail. That’s normal. But I believe more AI breakthroughs are coming. I’m skeptical of those following the same playbook as OpenAI and Anthropic. Raising $1B and spending 50% on GPUs doesn’t make you a $10B company. I’m rooting for the contrarians. Challenge scaling laws. Challenge transformers. Challenge how models learn. Challenge the next-token prediction paradigm. My bigger concern is that brilliant researchers don’t necessarily make great founders. In my experience, researchers often struggle to turn their research into great products / sustainable businesses.

    浏览 3,714赞 61转 410月26日 01:04原帖

点赞、转发、浏览是采集那一刻的快照,不是帖子最终数据;时间为北京时间。KPI 卡片里的同比只在账号有 8 周以上数据时显示, 否则只看近 4 周。“打法建议”只说明数据里的相关性,不代表因果。