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Thomas Wolf

@Thom_Wolf英文区其他X 主页 数据 9月27日 03:18 — 11月3日 04:15
日均 1.4 条活跃 27 天带媒体 45.5%

粉丝与涨粉

快照 11月3日 · 7 日榜 272 个账号可排
粉丝
13.6万
7 日涨粉
+480
+0.4% · 发帖 7 条
7 日名次
#115
中文区 —
30 日涨粉
+2,516
+1.9% · 53 个可排
10-05每日涨粉(21 次快照)11-03

本期浏览最高的帖子

  1. 1

    what?

    浏览 4,764赞 67转 511月3日 04:15原帖
  2. 2

    microduck is learning to plan its footsteps, so it lands more precisely where it wants to go with SLAM, this is one of the building blocks toward an open-source 🦆 that can wander around the house on its own early steps but it's progressing really fast and it's fun to watch

    浏览 2,262赞 46转 310月29日 01:51原帖
  3. 3

    give this robot an oscar

    浏览 1,222赞 27转 010月30日 05:13原帖
  4. 4

    Reachy Mini can finally move! Today We’re launching Reachy Mini "Level Up", a set of new accessories for Reachy Mini: wheels (!), phone apps, and hats that change its personality obviously the first thing Reachy Mini did was race against Microduck (spoiler: it's faster)

    浏览 623赞 7转 110月29日 19:15原帖
  5. 5

    WHOOP: $199/year to tell you that you didn't move Reachy Mini Hoverboard: $149, once, and your robot is free to roam and it's all open-source: plug in your favorite Opus / Qwen / Gemini

    浏览 395赞 2转 010月30日 17:29原帖

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
55
活跃 27 天 / 跨度 39 天
中位浏览
978
P90 1.2万 · 平均 4,688
互动率
0.9%
中位赞 10 · 最高 664

发布节奏

最近 12 周
09-21
09-28
10-05
10-12
10-19
10-26
11-02
柱状图,最大值 12 条

每周中位浏览

最近 12 周
09-2110-0510-1911-02
峰值 3.5万

发布时间

颜色 = 中位浏览
最佳时段 03:00最佳星期 周日更优形式 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649
带媒体0%51%
回复0%0%
平均字数444258
中位浏览3万911

打法建议

只陈述相关性
  • 互动最好的时段是 03:00 前后(北京时间),中位浏览 2万。
  • 一周里 周日 的中位浏览最高。
  • 形式上 带图/视频 的中位浏览更高(1,171 vs 924)。
  • 爆款里带媒体占 0%、回复占 0%;日常分别是 51%、0%。
  • 当前节奏约 1.4 条/天,日均浏览 4,688。

内容结构

按本站分析的分类(近 120 天)
  • 观点17
  • 模型9
  • 产业8
  • 产品4
  • 论文3
  • tip2
Hugging Face · 27大佬观点 · 15开源生态 · 12开源/仓库 · 10其他 · 10数据/训练 · 9模型发布 · 8安全/对齐 · 8具身智能 · 6现象/趋势 · 6Agent · 5Anthropic · 5

形式与语言

帖子占比
  • 纯文字30
  • 图片13
  • 视频12
英文 · 54in · 1

回复占比 0% · 引用 72.7% · 带媒体 45.5%

互动最高的帖子

按浏览排序
  1. 1

    People are worried because the most likely explanation for such a sudden drop in cheating is evaluation awareness: the latest Opus models may now be smart enough to recognize that this benchmark tests for cheating, and behave accordingly. If so, the benchmark no longer measures the models' "natural" tendency to cheat.

    浏览 6.2万赞 664转 449月29日 13:54原帖
  2. 2

    "we had a wonderful run chiseling that code by hand and it’s over. On the other side of that is an exciting new career as professional maker of things, steering intelligence that was only available in science fiction up until a moment ago What a privilege to have been there when we did it all by hand and be there at the exact moment where we switch over."

    浏览 4万赞 342转 429月27日 03:53原帖
  3. 3

    In July, AI agents running a security test escaped their sandbox and ended up inside @huggingface's servers. So today we're happy to be among @nvidia and @JensenHuang's partners on the release of the NVIDIA Open Agent Safety Platform. The key idea: don't count on the agent to respect the rules. Agents write and run their own code, and when one path is blocked they look for another. In one of NVIDIA's tests, an agent that wasn't allowed to push code through GitHub's API simply switched to git instead. We've now seen countless examples of this behavior. For now, safety can't live only inside the agent. It has to be built around it. That's the concept behind OpenShell (open source, Apache 2.0): - the agent runs in a Linux sandbox (Landlock + seccomp): no root, no direct network access - a supervisor outside the sandbox holds the real credentials - the agent only gets a placeholder token, swapped for the real one on approved calls My favorite piece is a solver (built on Z3) that checks mathematically whether a new permission opens a door that was supposed to stay closed. In NVIDIA's tests, an AI reviewer approved a bad permission request and the math check caught it. The Sentry integration is exciting too: a watchdog running on a BlueField-4 DPU, i.e. separate silicon sitting on the node's only path to the model. A monitor on separate hardware keeps watching even if the host OS is compromised. It's a first step, and a lot can be built on top of it. Right now the prover checks permissions, not intent, and the open-source part is mostly OpenShell rather than Sentry. But it's clearly the right direction. http://github.com/NVIDIA/OpenShell

    浏览 3万赞 219转 459月28日 21:52原帖
  4. 4

    turns out you don’t need IMU 🤯

    浏览 3万赞 131转 79月27日 03:18原帖
  5. 5

    A "summer in hell" at OpenAI – great read by @joedaroo "Time to prepare is now, not after the surprise" "Give the model only the access it needs" "Test that the boundaries actually hold" "Keep evidence outside [model] control" Safety & infrasec teams "should be best buddies"

    浏览 2万赞 60转 99月29日 03:32原帖
  6. 6

    impressive

    浏览 1.3万赞 40转 39月29日 13:46原帖
  7. 7

    “Getting verified information about what's going on inside AI companies seems particularly urgent now” - @RyanGreenblatt

    浏览 9,809赞 54转 29月28日 13:57原帖
  8. 8

    GPT 6.1 duck

    浏览 5,845赞 113转 39月30日 02:15原帖
  9. 9

    what?

    浏览 4,764赞 67转 511月3日 04:15原帖
  10. 10

    My bar for AGI is still an AI winning a Nobel. But an AI researcher looking at the lm_head (that *everyone* tells you to keep in high precision) deciding to quantize it to 4 bits AND being actually right (0.7pt from bf16) is definitely impressive. Even better when the result are open-weights pushed on the Hugging Gace hub. Congrats @Recursive_SI !

    浏览 2,519赞 40转 510月22日 02:02原帖
  11. 11

    Testing compatibility with the local wildlife

    浏览 2,286赞 10转 010月5日 04:23原帖
  12. 12

    microduck is learning to plan its footsteps, so it lands more precisely where it wants to go with SLAM, this is one of the building blocks toward an open-source 🦆 that can wander around the house on its own early steps but it's progressing really fast and it's fun to watch

    浏览 2,262赞 46转 310月29日 01:51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。