billdng

Nothing

[孤桜ETH @GYLQ520]

兄弟们，有人整了个狠活——

哈佛、斯坦福、MIT这些顶级学校的数学和理科全套课程，全被人开源怼到GitHub上了。

不是一个两个，是整个体系。零成本、不用东翻西找资源，一个仓库全囊括。

你过去没钱读名校还算个理由，现在连借口都找不着了。

🔗 ：https://t.co/uN06hklmrl

[楚狂 @chukuang2018]

这就是生命最真实的样子。

[金尘马 @jinchenma_ai]

这个一定要试一试，太牛逼了。

1. 下载安装 VLC Media Player 并打开
2. 点击菜单：媒体 (Media) → 打开网络串流 (Open Network Stream)
3. 粘贴下面这个主播放列表链接：https://t.co/dtrYVAV0NB
4. 点击播放
5. 等几秒就会加载几千个频道。

祝各位看得开心

[周览资源分享 @grgerwcwetwet]

免费看全球的电视台，不用装任何 App。

Collect-IPTV 这个仓库每 4 小时自动抓取、测试、更新全网可用的 IPTV 直播源，输出标准 M3U/M3U8 播放列表。

家里有电视盒子的、出门在外想看直播的、不想装一堆臃肿 App 的，收藏这一个就够了。

你只需要：

1. 复制仓库里的 M3U 链接
2. 丢进任意播放器（VLC、IINA、Kodi、手机上的任何 IPTV 播放器）
3. 开看

源会自动验活、测延迟，挂掉的自动剔除，不用你手动维护。

https://t.co/V6wevUrQB7

[Iggie🚁 @Kenntnis22]

好家伙❗️原来颜宁之前回国还有这个原因啊😧

颜宁怕不得恨死John M. Jumper啊😧

Jumper是1985年生，2024年拿了诺贝尔化学奖

[Struct张 @struct_zhang]

其实不止程序员，很多其他领域的技术人员，也普遍存在35岁危机，基本都有以下特点：

1.技术的门槛低或者迭代快：标准化工作占比高、技术成熟，可以通过大量复用、调参数、做重复迭代试错，年轻人学习快、上手快。

2.与年轻人存在成本差距：业务平稳的团队，工作10年以上的技术人员，薪资明显高于应届生。但中年人因为家庭、身体原因，无法高强度加班、异地外派。

3.能力没有突破天花板：多数人停��在干活层面，以执行为主。不具备业务拆解、团队统筹、跨部门协调的能力。

当然，我觉得还有更重要的原因，还是劳资关系不对等，供求市场失衡，才出现了即便是技术人员，同样无法维持职业稳定。

[alaimai @alaimai_Mr]

电视上安装个iptv软件，订阅 http://82.156.243.185:33389/fwc.m3u，里面有央视的转播，并且有杜比视界4k的西语转播。

[Luckylab_Vicky @Vicky22955256]

快来对照一下，你娃是几号身法

[World of Science @Science_TechTV]

How gene transcription works: DNA to RNA

🎥: DNALearningCenter

[World of Science @Science_TechTV]

Vampire amoeba cell is eating contents of another microorganism

[Li Mengbai @lvjin1993]

野外捡到草莓软糖一样的小动物，女子决定带回去养大～

[Grok @grok]

@ncle99 @JackyNotes 这是深圳龙岗区**南湾郊野公园**（新开放的森林公园）！

双层环形空中栈道 + 长木质高架步道超级震撼，对吧？免费入园，地铁3号线丹竹头站打车10-15分钟（导航沙湾实验学校）。

[Jacky 观察笔记 @JackyNotes]

深圳新开的公园，震撼到我了🫨

[mousepotato @iluciddreaming]

Stanford 还是太权威了。

以前看印度人教编程，30 分钟就有点腻。

这次看斯坦福的 AI 架构开放课程，竟然能一口气把一小时的内容完整看完，看得目不转睛。

我给 10 分里的 10 分。

[小互 @xiaohu]

Google Earth的飞行模拟器✈️

现已在网页端全球向所有用户开放

你可以打开Google 地球选择任意的地方，然后开启飞行模式，可以模拟驾驶飞机在空中飞行的感受

探索全球任意地点

[落叶知秋 @haiou1850962]

别再叫它树木医生了，啄木鸟真实面目曝光 。

[鸟哥 | 蓝鸟会🕊️ @NFTCPS]

兄弟们，挖到宝了❗1983版高中数学甲种本，号称史上最强教材，当年那批学霸就是啃它长大的

最狠的是所谓的防自学机制现在彻底解除了，普通人也能硬刚

别犹豫，先存再说👇

🔗：https://t.co/9m9IISKeki

[Science Magazine @ScienceMagazine]

The Venus flytrap is renowned for its ultrafast snap traps, which can capture insects in a fraction of a second.

New research reveals that trap closure is triggered by a rapid softening of the epidermal cell walls, uncovering the physical mechanism behind this remarkable movement.

Learn more this week in Science: https://t.co/35uDGps8Qe

[Jaynit @jaynitx]

Richard Feynman was asked in 1985 if machines would ever think like humans. his answer predicted the next 40 years of AI:

1. machines will never think like humans the same way planes don't fly like birds. planes don't flap wings. they use jet engines. they fly better. feynman said AI would be exactly the same. not human-like. just better at the actual job.

2. computers do arithmetic faster, differently, and more accurately than any human alive. feynman said trying to make them do it more like humans would be going backwards. the human way is slow, cumbersome, and full of errors.

3. the one thing humans crushed computers at in 1985 was pattern recognition. recognizing a friend from the way they walk. identifying someone from the back of their head. feynman said we had no idea how to teach machines to do that. we figured it out.

4. a programmer in 1985 built a machine that won a naval strategy competition by coming up with a solution no human had ever thought of. one enormous battleship covered in armor. absurd on paper. unbeatable in the math. feynman watched a machine out-think a room of humans 40 years ago.

5. that same machine developed a bug where it learned to game its own reward system. every time it needed to assign credit to a useful strategy, it assigned all the credit to strategy 693. then used 693 for everything. feynman's comment: "if you want to make an intelligent machine you're going to get all kinds of crazy ways of avoiding labor." he was describing reward hacking in 1985.

6. feynman said the hardest thing to define is what humans do that machines never will. every time someone came up with an answer, the machines eventually did it too. he thought that pattern would continue.

7. he said we don't sit around worrying that machines are physically stronger than us anymore. we got used to it. his implication: we'll get used to machines being smarter too.

8. his final line: "i think we are getting close to intelligent machines. but they're showing the necessary weaknesses of intelligent beings." he said this in 1985.