CPtNl

[Mario Nawfal @MarioNawfal]

🇨🇳 China is testing a motorized mobility device that can reportedly boost a person’s running speed to 50 km/h.

We're slowly becoming cyborgs.

[Factual Index @factualindexx]

🇨🇳 Chinese inventions that changed the world:

1. Papermaking
2. Printing (Woodblock & Movable Type)
3. Compass
4. Gunpowder
5. Paper Money
6. Abacus
7. Silk Production
8. Porcelain
9. Seismograph (Zhang Heng device)
10. Kites
11. Wheelbarrow
12. Iron Casting
13. Blast Furnace Steel
14. Crossbow
15. Tea Processing
16. Tofu
17. Toothbrush (early bristle form)
18. Umbrella (early versions)
19. Rocketry (fire arrows)
20. Matches (early sulfur-based fire starters)

[AB Kuai.Dong @_FORAB]

知道消费赛道有多难了吧

外资投行看消费的高级分析师，直接连夜提桶跑路了。

[蔡子博士Chris @caiziboshi]

这段视频展现了世界著名头部播客博主Lex Fridman 在中国旅行搭乘顺风车时的场景，应该不是AI!

Lex Fridman 近期公开表示，自己从2026年5月中旬开始展开一场“无固定计划”的全球旅行，第一站就是中国。他在5月14日发文称，计划先前往北京、上海、深圳等大城市，与中国 AI 工程师和科技从业者交流，了解中国 AI 产业的发展；随后会背着背包前往中国农村地区，尝试人生第一次搭顺风车（hitchhiking），深入接触普通民众与长途卡车司机。他提到希望重点前往河北、山西、陕西、甘肃、四川、云南等地，并可能在中国停留数周，然后继续前往亚洲、中东、欧洲以及美洲等地区展开开放式旅行。整个行程强调“没有明确路线、只有背包和随机冒险”，核心目标是接触不同社会阶层的人，体验真实的地方文化与日常生活。

[汪江徽 @wangjianghuiCN]

山東女子深夜帶自閉症兒子擺攤，1男子試探着問：「我能免費吃碗涼皮嗎？」女子愣了愣，隨即手腳麻利做好遞給他：「吶，高配版！」男子吃完轉身就走，誰料，女子手機卻到賬20000元……

[Mario Nawfal @MarioNawfal]

The Huawei MateBook Fold is wild, but thanks to export controls and sanctions, you can't buy it in the U.S.

[Wendy @0xWendy99]

下午看到36氪财经发的“解码万亿长鑫朋友圈”，没过几个小时就被删了，文章全面梳理了相关产业链上的公司，还好对标的做了笔记。

从持股投资机构的角度来说，除了阿里，腾讯这两大巨头的投资以外（对巨无霸来说这笔投资对市值没啥影响），比较引人注目的还有兆易创新，有1.8%左右。更值得注意的是，兆易创新和长鑫科技的创始人是同一人，朱一明。资方还有上峰水泥（0.15%），合百集团（0.05%）以及合肥城建等等，目前二级市场的相关涨幅跟持股比例已经有一定脱钩。

从上下游产业链的相关公司角度分析，有一些企业并没有明确说明合作关系，因此相关标的仅供参考，不构成任何投资建议。

**封测** ：
- 深科技
- 汇成股份
- 通富微电

**原材料供应** ：
- 麦捷科技
- 雅克科技
- 晶瑞电材
- 广钢气体
- 金宏气体
- 立昂微
- 沪硅产业
- 江化微
- 兴福电子
- 安集科技
- 神工股份
- 中船特气

**设备** ：
- 柏诚股份
- 正帆科技
- 至纯科技
- 华清海科
- 精测电子

**下游经销商** ：
- 商洛电子
- 雅创电子
- 力源信息
- 大为股份
- 德明利
- 中电港

[Noisy @noisyb0y1]

A GERMAN DEVELOPER REPLACED HIS ENTIRE DEV TEAM WITH KIMI K2.6, VISUALIZED EVERYTHING IN OBSIDIAN AND NOW MAKES $80,000/MONTH SOLO

1 trillion parameters, 32 billion activated per token and a SWE-Bench score of 65.8 - Kimi K2.6 reads the entire client codebase, understands the architecture, writes production code and ships for $150-300 in API costs while a traditional agency pays developers $4,800 for the exact same project.

300 parallel agents per run deliver 100+ files simultaneously - search, analysis, coding and writing all in parallel - and Obsidian visualizes the entire knowledge graph in real time while the agents work.

A traditional agency with 10-15 people keeps 30% margin after salaries. He keeps 90% - $72,000 in monthly profit with $500 in overhead.

By month 10 Kimi handles 80% of the technical work and he manages only strategy and client relationships - while Obsidian maps every project, every client and every agent in one graph that updates itself.

[sigma capitalist @phithetasigma]

On Nvidia’s Vera CPU –

First, let’s revisit the CPU architecture (see attached image) and its bundled memory

The referenced SOCAMM is a data centre class modular form factor for LPDDR5X (not to be conflated as two different things)

Now, 1 LPDDR5X DRAM die = 9.6Gbps/bit (max). Assuming a 32-bit package, 1 LPDDR5X DRAM package = 9.6 x 32 = 307.2Gbps (~38GB/s) bandwidth

1 SOCAMM is constructed using four LPDDR5X DRAM packages. Total bandwidth per SOCAMM = 38 x 4 = ~154GB/s

The Vera CPU setup uses 8 SOCAMM (8-channel), and therefore has 8 x 154 = ~1.2TB/s of bandwidth

The 1.5TB refers to the capacity. Assuming 32 (8 x 4) LPDDR5X DRAM packages in 1 Vera CPU, this infers the use of 48GB DRAM packages (not 192GB), i.e., 32 x 48. This could be for thermal/power management reasons

In short, each Vera CPU set up = 8 SOCAMMs = 32 LPDDR5X DRAM packages = 1.5TB capacity (32 x 48GB) = up to 1.2TB/s of bandwidth

Second, on market opportunity

Nvidia guided visibility into ~$20B of CPU revenue this year. What is unknown is how this breaks down into sales configurations (and by extension, the memory modules/density types that may be required)

Possible configurations:

1 – as part of Vera Rubin. Assuming NVL72 setup (72 Rubin GPUs, 36 Vera CPUs), total LPDDR5X memory capacity = ~55TB (36 x 8 x 4 x 48), which could potentially be higher if they use higher-capacity memory packages (with liquid cooling)

2 – dedicated Vera CPU racks. Each rack packs 256 Vera CPUs, and therefore up to ~400TB of LPDDR5X memory (256 x 8 x 4 x 48), which could potentially be higher if they use higher-capacity memory packages (with liquid cooling)

3 – individual Vera CPU servers. On the low end will be single and dual socket servers, with one or two Vera CPUs per tray/node respectively. For this configuration, memory capacity = 1.5TB / 3.0TB (assuming use of 48GB modules, with air cooling). Whereas on the high-end, such as the ones being developed by HPE (Cray Supercomputing GX240), this could go up to 640 Vera CPUs = close to ~1000TB. There is also the HGX Rubin NVL8 configuration, with one or two CPUs wired up (with similar memory capacities as a the single/dual socket servers)

In short, it’s challenging to accurately size up the total bit demand for LPDDR5X without knowing the exact sales configurations

What may be a better play is memory PHY/controllers IP –

Each SOCAMM requires 4 x LPDDR5X PHY and memory controllers. For Vera CPU which uses 8 SOCAMMs, each CPU = 32 LPDDR5X PHYs and memory controllers. All that is needed is the average list/sale price for CPU, divide $20B by this number to derive the implied number of Vera CPUs to be sold, that then allows you to derive the dollar content for memory PHY/controllers

[财经天下 @Caijingtianxia]

当时听泉鉴宝揭露了太多古董圈的黑幕，

据说就是因为这个视频被搞。

[Snowball（抓住金狗版）🔶 BNB ETHGas ⛽️ 🤖ボッ🦅 @one_snowball]

我亲眼见证了五个朋友返贫

1、第一个朋友，家里拆迁赔了四百多万，全款买了房，结果房子烂尾，拆迁款等于在卡里过了一下，啥也没留下

2、第二个朋友，跟合伙人开餐厅，每人投一百多万，每天营业额不到一千块，除去房租水电人工，一年到头倒贴钱

3、第三个朋友，酒后讲义气，借给发小八十万救急，结果发小投资失败，公司倒闭，钱要不回来，至今还在打官司

4、第四个朋友，为人仗义，听不得好话，一夸就飘，替老同学担保三百多万，同学没钱跑了，他背连带责任，成了冤大头

5、第五个朋友，是位老师，平时抠抠搜搜，把所有钱都投进股市，账户不断贬值还不舍得割肉，一家老小至今住不到四十平的一室一厅

这些事，于你于我，都是警醒

[RamenPanda @IamRamenPanda]

这是个什么新赛道？！

[Flyby 🕊 @theunknownma11]

@MarioNawfal ✨🇨🇳China’s core business districts across major cities are being transformed by giant naked-eye 3D screens. No glasses are needed.

[Mario Nawfal @MarioNawfal]

🇨🇳 China is pushing screen technology to another level

[Massimo @Rainmaker1973]

No electricity, no fuel, just water. The Bunyip Pump is transforming the way irrigation works in Australian farms.

Gravity-powered, it uses the energy of falling water to pump a smaller volume of water uphill, without the need for electricity or fuel.

[STOCK调研公社 @STOCK6688]

不仅仅是内存了，全球基板供应紧张，现在也开始迫使客户预付货款。自2026年4月台积电管理层在公司公开业绩交流会上明确表示正同步推进CoPoS后，玻璃基板正在从“远期概念”进入“产业验证期”，验证路径由英特尔、台积电等头部厂商共同推动。

[Olivert @indiehackercase]

长鑫存储是真牛逼，背后的朱一明和合肥市政府更牛逼。整整投资了十年，直到去年还在亏损，今年一下子赚回来了！

合肥眼光超前，比南京还牛逼。当一线城市都不敢投资对的时候，合肥市政府直接拿出四分之三的投资，合144亿，砸向长鑫。

今年，光长鑫这一家公司，合肥今年GDP排名估计就要提高好几个位次。要不了几年，合肥GDP可能要排到全国前十了。

[DRV6 @DRV2035]

美国专家：是时候向中国学习了

[我是梦嫣❤️ @mengyan1234567]

馆长： “他们乖乖的都在等谁？！”

画面里面的每一个人，如果去趟台湾，那就是天一样高高在上的存在。而在北京，只能毕恭毕敬的站在一起等着。

馆长说的是事实，他还想骂醒更多的台湾民众，可惜……