https://github.com/mindmuxai/brain.md

Channel
ABCD 1024 News
@ABCD1024News
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
28subscribers
-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 | -1001622518376 |
|---|---|
| Type | Channel |
| Username | @ABCD1024News |
| Created | Between 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 23 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 23 August 2026 |
| On Telegram | t.me/ABCD1024News |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 23 Aug 2026, 11:16 | 28 | -1 |
| 11 Aug 2026, 04:47 | 29 | no change |
| 7 Aug 2026, 09:40 | 29 | first reading |
Engagement
20 posts held, back to 2 May 2023 — 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.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 26 June 2026. An engagement rate over an empty window would be a number about nothing.
Reaction mix
1 reaction across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 1 | 100.0% |
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 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 1reactions 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 2 May 2023 to 26 June 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
https://mp.weixin.qq.com/s/NMW2p8xalh9c8lTLN9B5gg 📌 Description: 本文探讨了 AI 行业的最新动态,特别是 DeepSeek 在推动新范式方面的作用,以及其对市场和竞争格局的影响。 🎯 Key Points: - DeepSeek 推动了 RL 和推理模型的新范式,尽管没有发明新范式,但其开源策略加速了行业的接受度和发展速度。 - DeepSeek 在技术上超越了 Meta Llama,但与 OpenAI、Anthropic 和 Google 等顶级公司仍有差距。 - DeepSeek 的开源策略填补了市场空白,尤其是在 OpenAI 转为闭源后,成为开源生态的重要参与者。 - DeepSeek 的发布时机与市场事件的结合引发了广泛关注,包括对美国科技霸主地位的挑战。 - DeepSeek 的成功对 Chatbot 市场造成冲击,但对开发者和企业市场的影响有限。…
https://m.bilibili.com/video/BV11erPYhEHx
https://mp.weixin.qq.com/s/pg2Mg15ZF8jhoYBcxmTIJg
https://mp.weixin.qq.com/s/74SaMd9urByBhyBiWBFGbg
https://mp.weixin.qq.com/s/bdx9QgYw3SmNqCt5P0BURg
https://mp.weixin.qq.com/s/YfFN7yjbyyPIy3MC89HdXA 📌 Description: 本文详细记录了中国大模型产业在2023年的资本运作和关键人物的决策过程,揭示了投资者在不确定环境下的策略和心态变化。 🎯 Key Points: - 中国投资圈从德州扑克转向大模型产业,投资者们纷纷采用“Club Deal”模式以分散风险。 - ChatGPT问世后,中国大模型产业迅速崛起,一些公司在首轮融资中估值高达10亿美元。 - 王慧文在2023年2月宣布成立光年之外,目标是成为“中国OpenAI”,并迅速筹集了3亿美元资金。 - 光年之外的融资吸引了红杉中国、五源资本、腾讯等多家知名投资机构和个人投资者。 - 王慧文因工作压力大,出现疑似抑郁症状,最终美团以约20.65亿元人民币收购光年之外。 - 光年之外的退出促进了其他大模型公司的融资进程,如百川智能和月之暗面等。 - Minimax在…
https://shyam.blog/posts/beyond-self-attention/
📌 Description: 文章探讨了如何通过深入研究Transformer模型的内部工作原理,特别是在注意力机制之后的处理过程,来理解其如何生成下一个令牌的预测。 🎯 Key Points: - Transformer模型通过多头自注意力机制学习令牌之间的多重关系 - 研究重点在于理解注意力计算后的处理过程如何转化为下一个令牌的准确预测 - 通过分析Transformer块的工作原理,提出了一种工作理论来解释Transformer的预测产生方式 - 实现了一种命令式代码,模拟了Transformer的预测过程,并与Transformer产生的输出非常相似 - 提供了实验和理论支持,表明Transformer通过将给定提示与训练语料中的字符串类关联来产生预测 - 每个Transformer块的输出预测是基于与该提示相似的训练语料中字符串后跟的令牌分布 - 最终的Transformer输出是每个块预测的线性组合 - 研究揭示了…
https://mp.weixin.qq.com/s/yoFity0oojhpkvr92Jt7Jg
https://mp.weixin.qq.com/s/A9s7YQiF1JvwScOqQa0dnw
https://mp.weixin.qq.com/s/VVZu7jpZwK8BkvFW-101aA
Showing the 12 most recent of 20 posts we hold for @ABCD1024News. 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.
Forward network
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 23 August 2026 — this entry's latest reading, not the date you are reading this.
“ABCD 1024 News” (@ABCD1024News), 28 subscribers as measured 23 August 2026. Telegram Register, tgregister.com/channel/ABCD1024News.
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.