4 measurements spanning 6 days, net +8. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 5,381–5,392 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Aug 2026, 11:23
5,391
+1
9 Aug 2026, 18:23
5,390
+8
7 Aug 2026, 04:18
5,382
-1
6 Aug 2026, 09:34
5,383
first reading
Engagement
22 posts held, back to 2 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 8 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
37.8%
avg views ÷ 5,391 subscribers
Avg views / post
2,040
16 posts measured
Reaction rate
0.319%
reactions ÷ views · ER floor
Posts in window
17
of 22 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 13 of 16 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 11 August 2026
Posts held
22 (2 July 2026 – 11 August 2026)
Views total
32,614
Reactions total
88
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
12 Aug 2026, 04:20 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
Video runtime
26s
Average length
26s
Measured directly from 1 video 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.
Reaction mix
134 reactions across 16 posts, in 9 distinct kinds. The most used accounts for 59.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
79
59.0%
👍
18
13.4%
🥰
15
11.2%
🐳
7
5.22%
🌚
4
2.99%
🤔
4
2.99%
🤣
4
2.99%
👏
2
1.49%
😁
1
0.746%
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 16 of the 22 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 134reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 22 most recent posts we hold, published 2 July 2026 to 11 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.
#sqlite
《How We Built a Zero-Disk, S3-Tiered Storage Engine for SQLite》
如何给 sqlite 加上远程备份一直是一个热点问题，这个文章提供另一种思路：通过 sqlite 的 VFS 接口，给 sqlite 的数据存储提供冷、热分层存储。
#人工智能
传统的软件行业，是有“边际效益”的：成本主要集中在软件开发期内，开发完毕之后，同一份软件给一个人用和给一万个人用，成本差不多。当然，如果是互联网行业的软件，多一个用户会多一些带宽和存储成本，但是总体而言，多出来用户之后的成本可以认为是满足边际效应的。
但是如今大模型领域则不是这样。姑且把大模型领域分为训练和推理两个阶段，前期花费大量成本训练模型完毕了还不算，模型部署上线后，推理的成本是实打实的：多一个用户的请求，就多一份不能被忽略的推理成本。
从这个意义上来说，就现阶段而言，模型推理服务（Model As A Service，MAAS）其实更像是制造业。区别在于：传统制造业，原材料和产出物，都是实体；而模型服务，输入和输出，是虚拟的数字物件token。
#人工智能
《Clean Code for AI Agents》
这篇文章讨论了在2026年，当代码的主要读者从人类程序员转变为AI代理（如Claude Code、Cursor等）时，《Clean Code》原则需要如何重新排序和调整。
AI代理有独特的技术约束——上下文窗口有限、文件截断、依赖grep导航、每次工具调用都消耗token和延迟。因此，传统的代码整洁原则需要按新的优先级重新整理：
最重要的原则：小函数和小文件（适配代理的一次性读取）、单一职责（方便隔离推理）、独特有意义的命名（grep友好）、带原因和背景的注释（代理爱读注释，且不应被删掉）、显式类型（避免类型推断错误）、DRY（防止代理漏改重复代码）、可无头运行的测试（TDD成为技术义务）。
🐳7❤6
Signed 老C
Showing the 12 most recent of 22 posts we hold for @codedump_notes. 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 — 331,489 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.
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.
Mentions
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
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 12 August 2026 — this
entry's latest reading, not the date you are reading this.
“codedump的电报频道” (@codedump_notes), 5,391 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/codedump_notes.
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.
