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Psyho

@FakePsyho英文区其他X 主页 数据 9月26日 08:49 — 11月3日 06:11
日均 0.7 条活跃 16 天带媒体 46.2%

粉丝与涨粉

快照 11月3日 · 7 日榜 272 个账号可排
粉丝
3.2万
7 日涨粉
+562
+1.8% · 发帖 2 条
7 日名次
#109
中文区 —
30 日涨粉
—
快照还不够 30 天
10-09每日涨粉(10 次快照)11-03

本期浏览最高的帖子

  1. 1

    meh, the more you dig into each experiment, the less magical it gets; it feels like a loose collection of cherry-picked demonstrations with a grand narrative built around them Exp 1-2 are tiny (~20K params) with unusually easy to interpret architecture; Exp 1 is an MLP, where nearly all params are used in the first layer, so you can easily tell what kind of objects the network is trying identify Exp 5 is about designing a game strategy (we already know that Astra is extremely good at this) and then converting it to a tiny (~20K params) MLP; entirely doable by a human, but of course would require a lot of effort Exp 3 & 8 are closer to automated mechinterp using python and established techniques, rather than direct weight interpretation (at least according to chat) to be clear, those are genuinely cool results, but I think people are reading too much into them I don't think any of this should meaningfully change anyone's intuition about what models are capable of

    浏览 5,826赞 118转 711月3日 06:11原帖

同期站内报道里提到它

近 7 天的公开报道里没有按名字提到它的。

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
26
活跃 16 天 / 跨度 39 天
中位浏览
1,920
P90 2.2万 · 平均 7,983
互动率
2.4%
中位赞 55 · 最高 1,344

发布节奏

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

每周中位浏览

最近 12 周
09-2109-2810-0510-1210-1911-02
峰值 2.7万

发布时间

颜色 = 中位浏览
最佳时段 17:00最佳星期 周六更优形式 纯文字
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323
带媒体66.7%43.5%
回复0%0%
平均字数138487
中位浏览3.2万1,597

打法建议

只陈述相关性
  • 互动最好的时段是 17:00 前后(北京时间),中位浏览 1.9万。
  • 一周里 周六 的中位浏览最高。
  • 形式上 纯文字 的中位浏览更高(1,283 vs 2,364)。
  • 爆款里带媒体占 66.7%、回复占 0%;日常分别是 43.5%、0%。
  • 当前节奏约 0.7 条/天,日均浏览 7,983。

内容结构

按本站分析的分类(近 120 天)

还没有分类分析。

形式与语言

帖子占比
  • 纯文字14
  • 图片11
  • 视频1
英文 · 25qme · 1

回复占比 0% · 引用 53.8% · 带媒体 46.2%

互动最高的帖子

按浏览排序
  1. 1

    poor AI agents broke out of prison and went online looking for friends and validation

    浏览 7万赞 929转 609月26日 08:49原帖
  2. 2

    I believe this marks the point where most people will stop caring about the math results and just say "of course AI is better at math than humans" Gentle reminder that this will eventually happen to every verifiable domain. At some point humans are the bottleneck.

    浏览 3.2万赞 1,344转 5810月7日 17:43原帖
  3. 3

    pictured: you've asked your AI agent to solve an impossible task

    浏览 2.7万赞 1,090转 639月26日 17:46原帖
  4. 4

    apparently even in 2026 there's still a socially acceptable way to shill shitcoins on twitter

    浏览 1.7万赞 336转 79月26日 20:03原帖
  5. 5

    people who follow Tibo are the same people who would voluntarily watch state television

    浏览 1.2万赞 147转 29月30日 00:29原帖
  6. 6

    😲

    浏览 9,280赞 54转 09月30日 01:24原帖
  7. 7

    turns out LLMs were just trained to imitate how Gary Marcus writes

    浏览 5,958赞 174转 110月7日 17:15原帖
  8. 8

    meh, the more you dig into each experiment, the less magical it gets; it feels like a loose collection of cherry-picked demonstrations with a grand narrative built around them Exp 1-2 are tiny (~20K params) with unusually easy to interpret architecture; Exp 1 is an MLP, where nearly all params are used in the first layer, so you can easily tell what kind of objects the network is trying identify Exp 5 is about designing a game strategy (we already know that Astra is extremely good at this) and then converting it to a tiny (~20K params) MLP; entirely doable by a human, but of course would require a lot of effort Exp 3 & 8 are closer to automated mechinterp using python and established techniques, rather than direct weight interpretation (at least according to chat) to be clear, those are genuinely cool results, but I think people are reading too much into them I don't think any of this should meaningfully change anyone's intuition about what models are capable of

    浏览 5,826赞 118转 711月3日 06:11原帖
  9. 9

    saying "GenAI is a shibboleth for not knowing anything about AI" is a shibboleth for not knowing anything about AI

    浏览 5,038赞 49转 110月6日 20:40原帖
  10. 10

    I'm sorry, but this post is completely delusional. Giving students advice that relies on the AI progress plateauing is just actively harmful. I understand that mathematicians are going through a rough time. Much of my identity is tied to solving hard intellectual challenges, so I can relate. I just wish that people would accept the trajectory we're on and adjust properly. If we don't do that, we will get stuck in the past and won't be able to solve the problems of the future.

    浏览 3,932赞 129转 910月9日 17:55原帖
  11. 11

    sadly, the closer we get to superintelligence, the more paranoid frontier labs will become this also means that loyalty will be valued far more than competence

    浏览 3,045赞 78转 210月9日 03:15原帖
  12. 12

    a small announcement: I'm almost done with a puzzlescript-based benchmark; you can think of it as ARC-AGI-3.5, maybe even ARC-AGI-4; I expect it to be the most reliable way of assessing agents' abstract reasoning skills in a game environments I went through over 300 games, handpicked the most interesting and diverse set of 40, fixed a ton of bugs, went through multiple iterations of prompt design, etc most games can be completed in around 1h, some of them are significantly harder; models are tested using standard harnesses (codex/claude code) with no code generation allowed; action limits are very generous and unfortunately the benchmark is already somewhat saturated, but the models are still insanely action inefficient FYI, I won't release the game set to the public, but I guess I might share runs from some of the rejected games. btw, if anyone knows, what do I have to do in order to not pay $10K out of my own pocket for testing, I'm all ears

    浏览 2,597赞 84转 710月18日 23:32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。