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Channel

David's random thoughts

@david_random

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

1,950subscribers

-1 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1001696788466
TypeChannel
Username@david_random
CreatedBetween 1 December 2021 and 31 March 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live11 August 2026
Measurements held3
Confirmed unchanged2 times, most recently 11 August 2026
On Telegramt.me/david_random

Growth

1,9501,9511,950.56 August 2026 — 1,951 subscribers6 August 2026 — 1,951 subscribers9 August 2026 — 1,950 subscribers6 August 20269 August 2026
3 measurements spanning 3 days, net -1. 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 1,950–1,951 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
9 Aug 2026, 00:541,950-1
6 Aug 2026, 06:331,951no change
6 Aug 2026, 05:081,951first reading

Engagement

20 posts held, back to 7 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
86.5%
avg views ÷ 1,950 subscribers
Avg views / post
1,690
14 posts measured
Reaction rate
1.53%
reactions ÷ views · ER floor
Posts in window
14
of 20 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 3 August 2026
Posts held20 (7 July 20263 August 2026)
Views total23,621
Reactions total361
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 17:12 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.

Reaction mix

443 reactions across 19 posts, in 16 distinct kinds. The most used accounts for 51.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
😁22751.2%
👍5312.0%
🤡5312.0%
😭235.19%
🤯204.51%
👏194.29%
🤓122.71%
71.58%
👎71.58%
🤣71.58%
40.903%
💯30.677%
🖕30.677%
🤔20.451%
🥴20.451%
👀10.226%

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 19 of the 20 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 443reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 7 July 2026 to 3 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

3 Aug 2026, 03:52 UTC868 views28 reactionsread 7 August 2026

AI分析师们一个个都引用MS财报里卖出多少M365 copilot订阅来洋洋洒洒的分析AI的前景和盈利能力,可是我看到的用公司订的copilot的员工怎么没一个是不对这货骂娘的😂

😁28

1 Aug 2026, 13:16 UTC983 views11 reactionsread 7 August 2026
Photo

3个月电费账单A$393,这就是为什么我一直想把家用server从9955HX换成PTL……

👍101

30 Jul 2026, 02:49 UTC≈1,350 views2 reactionsread 7 August 2026

本来现在gcc在生态上就已经被llvm远远甩在身后,再拒绝AI生成代码会怎么样我不好说。。。 https://www.phoronix.com/news/GCC-Declining-AI-Contributions

👎2

28 Jul 2026, 07:08 UTC≈2,970 views47 reactionsread 7 August 2026

K3本质上还是个闭源模型,所谓开源主要是一种营销手段。 虽然我这两天吐槽了A\和OpenAI等一堆商业公司,但是现在很多人对开源模型有奇怪的期待,也是时候该泼下冷水了。开源并不能省钱,你算出来便宜只是在白嫖别人的训练成本,长期看这个钱你还是得以某种形式付出去。 当然我100%支持模型厂商收钱。 https://x.com/cherylnatsu/status/2081996644476965119

🤡24👍12👎4🤣3🤔2👀1🖕1

28 Jul 2026, 04:00 UTC≈3,470 views29 reactionsread 7 August 2026

从未主张禁止开源AI模型,但是主张禁止我以外的模型 https://x.com/AnthropicAI/status/2081864750296658008

🤡29

25 Jul 2026, 15:46 UTC≈2,060 views24 reactionsread 7 August 2026

可是他自家的open model就是一坨巨大的狗屎,说这么多鬼话到头来还是想白嫖中国公司融资的钱然后倾销到美国去跟OpenAI/Anthropic恶性竞争罢了( https://x.com/hjc4869/status/2080658505732858223 现在看到那几个美国公司谈开源模型就想笑。 两三年时间,要么是造了几吨Nemotron那种下载嫌浪费流量的不可回收垃圾,要么发一次gpt oss就没了下文,要么弄phi那种改几个字就跑分骤降的过拟合刷分神器。没哪家拿出真正SOTA方案给客户本地部署。 现在倒是想起来还有开源模型这回事了,怎么这么巧呢?

👍24

25 Jul 2026, 02:43 UTC≈1,270 views27 reactionsread 7 August 2026

那你的gpt oss呢😅 https://x.com/sama/status/2080683363174945065

😁25🖕2

24 Jul 2026, 10:28 UTC≈1,290 views39 reactionsread 7 August 2026

我用最直接、最真相、最不绕弯的方式来告诉你,如果一个跑分软件3年大改3次评分标准,那不如大家都别测试了,直接找厂商搞竞价排名。

😁363

23 Jul 2026, 01:35 UTC≈1,250 views21 reactionsread 7 August 2026

感觉至少有一百万人对RDMA组cluster跑LLM的负载特性毛都不懂一个就知道复读Spark有200GbE,不得不说NVIDIA的营销能力深入人心😅 https://x.com/FrameworkPuter/status/2080005377434005756

😁21

21 Jul 2026, 16:01 UTC≈1,490 views13 reactionsread 7 August 2026

cp -r Gemini\ 3.5\ Flash Gemini\ 3.6\ Flash https://x.com/ArtificialAnlys/status/2079596244339707956

😁13

21 Jul 2026, 07:25 UTC≈1,770 views25 reactionsread 7 August 2026

首发那点时间根本不够深度体验一个模型的适用场景和能力好坏,到目前为止k3我看到的键政人讨论远比真正的使用体验要多。(不管是中国还是海外) 别问,问就是oneshot前端审美决定国运。 https://x.com/__oQuery/status/2079298132794659244

😁25

Showing the 12 most recent of 20 posts we hold for @david_random. 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 — 119,429 of 1,160,990entries 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

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.

“David's random thoughts” (@david_random), 1,950 subscribers as measured 9 August 2026. Telegram Register, tgregister.com/channel/david_random.

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.