有模有样的,已经可以购买了: https://www.lulu.com/shop/xiangpeng-hao/white-box-caching-for-cloud-data-systems/hardcover/product-57qn42z.html 再配上我们组的传统:在 thesis 上阴阳怪气 reviewer
❤16

Channel
@life_xiangpeng
On this record: Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Cite this entry
479subscribers
+6 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001126205663 |
|---|---|
| Type | Channel |
| Username | @life_xiangpeng |
| Created | Between 1 June 2017 and 30 September 2020 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 17 September 2026 |
| Measurements held | 6 |
| Confirmed unchanged | 1 time, most recently 17 September 2026 |
| On Telegram | t.me/life_xiangpeng |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 17 Sept 2026, 04:57 | 479 | +2 |
| 10 Sept 2026, 04:37 | 477 | -1 |
| 24 Aug 2026, 04:32 | 478 | +4 |
| 15 Aug 2026, 14:24 | 474 | +1 |
| 8 Aug 2026, 04:45 | 473 | no change |
| 7 Aug 2026, 21:52 | 473 | first reading |
18 posts held, back to 2 February 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 18 posts for this entry, the most recent from 25 July 2026. An engagement rate over an empty window would be a number about nothing.
Measured directly from 3 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
431 reactions across 18 posts, in 8 distinct kinds. The most used accounts for 25.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 111 | 25.8% | |
| 🤩 | 98 | 22.7% | |
| ❤ | 95 | 22.0% | |
| 🎉 | 46 | 10.7% | |
| 😢 | 29 | 6.73% | |
| 🦄 | 21 | 4.87% | |
| 😁 | 18 | 4.18% | |
| 😱 | 13 | 3.02% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 18 of the 18 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 431 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 18 most recent posts we hold, published 2 February 2026 to 25 July 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @life_xiangpeng. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 18 most recent posts we hold for this entry, published 2 February 2026 to 25 July 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
有模有样的,已经可以购买了: https://www.lulu.com/shop/xiangpeng-hao/white-box-caching-for-cloud-data-systems/hardcover/product-57qn42z.html 再配上我们组的传统:在 thesis 上阴阳怪气 reviewer
❤16
在纽约上班:太臭了这个地方,一个城市怎么能这么臭
😁18😢5
能坐在这里看一整天,非常解压(视频未加速)
❤7
人到中年开始关心自己的血糖,之前看大物带货了一个血糖仪(微泰动态)感觉很酷,就从京东转运了几个过来。 (美国的血糖仪需要处方|很贵) 但可惜不能在美国使用,于是让 codex 和 Claude 双方会诊,逆向了这个血糖仪的 protocol,现在可以在浏览器上直接连接并查看数据。 https://cgm.xiangpeng.systems
👍24
正式成为 Dr. Hao,总算熬出头了
🤩44🎉22❤14
买了 Insta 360 Luna,准备当视频博主了 有什么 AI 剪视频工具推荐吗👊
❤6
写了一个新 blog: A system programmer's guide to LLM inference 希望大家喜欢! ========== 最近写 LLM inference 是想搞明白我一直不明白的点:Nvidia 到底哪里好,Cuda 到底好在哪里。 从技术上来说,英伟达做的大多数东西都没什么 taste, 都给人一种 working but ugly 的感觉。Cuda kernel 也许曾经是 moat,但现在这些东西都可以让LLM来优化,并且非常 effective。我也不觉得 performance 是什么很难的事情。 但英伟达强在管理层的执行能力, 我们往往低估 bad ideas with good execution,他们能找准一条路持之以恒的走下去,再配合上他们现在拥有的资源和hype,几乎能在任何事情上掀起一片浪花。
❤16👍5
终于签了 offer,这个申请季算是把 faculty, VC, 和 startup 都试遍了,最后兜兜转转还是回到了最符合直觉的地方。 我终于再也不用和人争论什么是 research 什么是 engineering了,与其困于这些语言游戏,不如实实在在做点事。
👍30🎉24❤1
最近写 Agent 的一些感受: 1. Deepseek v4 flash 太牛了,速度快,价格低,质量好,还能稳定 hit cache 2. Minimax 很拉,Qwen3.6 35BA3B 还可以,但 instruction following 有点问题, Qwen 3.6 27B 不错,但有点慢,还很贵。 3. 买了一张 intel 推理卡,感受一下 cuda 的 moat 到底在哪里。 4. 感觉 Agents 给 data system 带来太多的的机遇了
❤13
现在还没有收到任何面试回复,我推测应该是全聚德了,情理之中意料之外吧🥲毕竟今年 funding 砍半还要 double hire 确实很难。遗憾和学术失之交臂了。 准备 move on 找 industry 的工作,接下来的几年想做一点 Agents 相关的事情,或者能加入一些早期的创业公司。 最后总结了我 PhD 期间的四个 major mistakes,希望能给下一阶段的自己提供一些指导 https://blog.xiangpeng.systems/posts/phd-mistakes/
😢24👍18
要开始为期两周的地狱行程了,希望被温柔以待😢
🤩12❤8
qwen3.5 9b 这个模型感觉很有东西:能在我的 4070ti 上跑到 60 tok/s,我常用的几个 llm test 都能完美通过,跑了一个 20 min orc task 没有任何 crash/loop/truncate,输出质量惊人。 感觉 local llm 的时代真的要来了。期待摆脱 openai/anthropic 的那一天。
😱13🤩7👍5
Showing the 12 most recent of 18 posts we hold for @life_xiangpeng. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 17 September 2026 — this entry's latest reading, not the date you are reading this.
“Patrick 舆情发布” (@life_xiangpeng), 479 subscribers as measured 17 September 2026. Telegram Register, tgregister.com/channel/life_xiangpeng.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.