Neat. 从 Kimi 逃出来终于有个有点人味的 Cursor 能用了

Channel
tkm’s memo
@tkm_memo
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
104subscribers
+1 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
Register entry
| Telegram ID | -1002294877451 |
|---|---|
| Type | Channel |
| Username | @tkm_memo |
| Description | 番組、撮影、音楽 cs.CV/AI/CG/ML; math.AG/CA/DG/PR SNS & etc: https://t.me/tiankaima https://x.com/tiankaima https://github.com/tiankaima https://space.bilibili.com/516277218 Notes: https://notes.tiankaima.dev |
| Created | Between 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 9 August 2026 |
| Last confirmed live | 9 August 2026 |
| Measurements held | 3 |
| On Telegram | t.me/tkm_memo |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 9 Aug 2026, 09:31 | 104 | no change |
| 8 Aug 2026, 08:04 | 104 | +1 |
| 7 Aug 2026, 17:42 | 103 | first reading |
Engagement
16 posts held, back to 23 July 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 pageof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 64.7%
- avg views ÷ 104 subscribers
- Avg views / post
- 67.3
- 16 posts measured
- Reaction rate
- 2.06%
- reactions ÷ views · ER floor
- Posts in window
- 16
- of 16 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 1 of 16 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 8 August 2026 |
|---|---|
| Posts held | 16 (23 July 2026 – 8 August 2026) |
| Views total | 1,076 |
| Reactions total | 2 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 9 Aug 2026, 09:31 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
- Photos
- ≈1,020
- Videos
- ≈48
- Links
- ≈591
Lifetime counters from Telegram’s own channel header, read 9 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Reaction mix
2 reactions across 1 post, in 2 distinct kinds. The most used accounts for 50.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 😁 | 1 | 50.0% | |
| 🤣 | 1 | 50.0% |
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 1 of the 16 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 2reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 16 most recent posts we hold, published 23 July 2026 to 8 August 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.
Recent posts
Vibe Coding 将这种工具类的东西回归了最本质的、工具的属性。其实这些很细微的需求也可以被更好的 cover 到了。 我很庆幸我在 vibe coding 推广之前就有机会做这样的完整项目,有 ~200 DAU 的用户其实是个很有成就的项目。在 vibe coding 出来之后,网络信息中心差不多时间也更新了统一身份认证,那段时间比较忙就没及时维护,等再上手来维护的时候就不太满足一个单纯的 iOS 项目了。 做这个 side project 一方面是想回答「LLM 时代的应用应该长什么样」,或者说到底有具体的 pattern 吗,是 ChatGPT 这类模型厂商作为所有流量的入口?人机交互的方式还会有变化吗?还有办法向后兼容这些新形态吗?我对这些问题都没有答案,我看不了那么久,但是身在其中总是有一点乐趣的,即使是用自己的钱烧 token 来做项目。 作为半个前 vibe-coding 开发,也见证了很多 taste…
最近一年在做的 side project Life @ USTC tl;dr: 这个项目整合了科大教务系统面向学生的所有功能,选课、课表、考试、作业,同时拥有更现代化的 UI,做了配套的 Bot,并且支持通过 MCP 协议(DCR/CIMD 注册)来支持更多 LLM 的访问,例如下面有在 ChatGPT 中使用 Life @ USTC 的例子。 大部分这些功能是在做移动端应用的时候思考的,关于 LLM 时代「什么才是一个合格的应用」的回答,平台还提供了一些额外的功能,例如基于 iCal 协议同步到日历,GraphQL 的查询,Bot 目前接入了 Kimi K3 来提供多模态的支持,支持公开编辑的课程介绍、作业等功能,助教或者老师可以通过这个平台直接构建一个课程主页等等。 一如既往的还是开源在 https://github.com/life-ustc/server (大部分 vibe 出来的文档还没清理凑合看吧) 网页 h…
...?
也是好起来了,但是我的监控为什么一直在报警有什么头绪吗
Photo, posted without a caption
https://fixupx.com/githubprojects/status/2082232139593244690 没绷住
🤣1😁1
补了一下文档,如果有维护 GPU 集群的话可以参考下。 https://notes.tiankaima.dev/lab/admin/services/ 最近在配 ansible(并且同时在寻找替代品这玩意有点难用),这里面有一些 BeeGFS 调优, slurm 计费/QoS 的内容还没补充,可能得等有时间才能完善了。顺带还是蹲个 infra 的实习
另外疑似 codex 把我所有机器上的 refresh token 全都 invalidate 了我靠…… 登录四遍有感觉吗
最近整理了一些文档这个 prompt 非常好用。 其实有的时候还需要补一句「不要把对话的要求也写进文档里」这种话,但确实是一个非常完整的,给 AI 擦屁股的 prompt 了
AI 太喜欢写空话/废话了,最近我和 @hejiyan 收集了一些例子并分析其中的具体问题,整理了一份关于何为空话/废话的文档,可以直接发给 AI: https://nofluff.0x01.me/
我已经完全理解怎么优化 SSR 了.jpg
Showing the 12 most recent of 16 posts we hold for @tkm_memo. 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.
Citation-graph rank
Citation-graph rank — 1,074,560 of 1,176,251entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Cite this entry
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 9 August 2026 — this entry's latest reading, not the date you are reading this.
“tkm’s memo” (@tkm_memo), 104 subscribers as measured 9 August 2026. Telegram Register, tgregister.com/channel/tkm_memo.
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.