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Channel

Mutt Technologies

@mutt_tech

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

1,329subscribers

+0 since we began measuring on 7 August 2026

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

Register entry

Telegram ID-1002594610061
TypeChannel
Username@mutt_tech
Description돈 이야기 안해요 (아마도) English speakers might want https://x.com/haxxton1
CreatedBetween 1 March 2025 and 31 July 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live11 August 2026
Measurements held2
On Telegramt.me/mutt_tech

Growth

1,3297 Aug 2026, 15:10 — 1,329 subscribers7 Aug 2026, 22:30 — 1,329 subscribers7 Aug 2026, 15:107 Aug 2026, 22:30
2 measurements taken within a single day. 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 1,328–1,330 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
7 Aug 2026, 22:301,329no change
7 Aug 2026, 15:101,329first reading

Engagement

18 posts held, back to 8 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 1 pageof Telegram’s post history, 20 posts per page.

ERR · 30 days
67.9%
avg views ÷ 1,329 subscribers
Avg views / post
902
13 posts measured
Reaction rate
0.098%
reactions ÷ views · ER floor
Posts in window
13
of 18 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 4 of 13 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 7 August 2026
Posts held18 (8 July 20267 August 2026)
Views total11,728
Reactions total4
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 22:30 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
191
Links
201

Lifetime counters from Telegram’s own channel header, read 7 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Reaction mix

9 reactions across 6 posts, in 4 distinct kinds. The most used accounts for 33.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
333.3%
🔥333.3%
👏222.2%
👍111.1%

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 6 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 9reactions 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 8 July 2026 to 7 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

7 Aug 2026, 12:17 UTC169 viewsread 7 August 2026

세상 좋아졌다 집에서 반도체 굽는것도 유튜브로 보고 https://www.youtube.com/watch?v=2iY1aEI_VIg

Signed Mutt

4 Aug 2026, 06:01 UTC357 viewsread 7 August 2026

거의 사실상 보편지능에 가까워진 LLM 시대에 사람은 뭘 해야할까? • 아래 공유된 글의 논점은 "LLM은 generalist에 가깝기 때문에, 사람은 specialist로서의 가치를 여전히 가진다" 라고 주장. • 즉 LLM 이 무엇이든 할 수 있지만 도메인 지식을 가진 사람이 해당 영역에서 LLM을 더 효율적으로 이용할 수 있음 • 도메인 지식이 없을 때 LLM 에게서 배우는 것은 전혀 나쁜것이 아님. 도메인 지식이 있던 사람이 더 앞에서 출발할 뿐. 그러한 점에서 사람은 여전히 쓸모를 가짐. 당분간 계속 이럴 것 https://www.seangoedecke.com/llms-reward-expertise/

Signed Mutt

31 Jul 2026, 12:02 UTC≈1,230 views1 reactionsread 7 August 2026
Photo

하락장에 돈 잃으셔서 속쓰리시죠? 스레드의 최신 도파민 분출 이슈나 보고 가시죠 • K-Skill 이라고 한국 최적화(KTX 예매, 카카오톡 조작, ...) 에이전트 스킬목록을 만들어서 배포하던 개발자가 있음(Github Star >6K) • 맛집 DB 블루리본 관련된 스킬도 포함. 근데 얘네는 알다시피 그걸로 먹고사는 사업자라 크롤러 차단했음 • 역공학하고 이런저런 방법으로 뚫어서 씀(개발자 명의로 결제해서 재배포까지 함) https://github.com/NomaDamas/k-skill/pull/94 • 블루리본이 바로 고소 https://www.threads.com/@bunniesossdev/post/DbaTxkPkXSO • 정신 못차리고 블루리본의 오픈소스 대체 DB(??) 를 만들자고 함 https://www.threads.com/

1

Signed Mutt

31 Jul 2026, 11:05 UTC363 viewsread 7 August 2026

DeepSeek V4 Flash 를 사후학습 더 많이 한 0731 버전이 API로 출시. 벤치마크 상으로는 DS V4 Pro와 GLM-5.2를 발라버림(???) 실사용 후기가 나와봐야 알겠지만 뭔가 엄청난 일이 일어나고 있는듯...;;; GPT Luna 떨이가 이래서 나왔나 가중치는 아직 안올라온듯 올라옴. https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731 https://x.com/deepseek_ai/status/2083084415157022911

Signed Mutt

31 Jul 2026, 02:14 UTC331 viewsread 7 August 2026

OpenAI 의 최신 모델들 GPT-5.6 업데이트 • Sol Fast 모드 추가 • Terra 20% 할인 • Luna 80% 할인 (?????) 이정도면 중국모델들 제발 쓰지말고 우리꺼 써줘 수준인데... Luna 가 GLM-5.2랑 비슷한 체급을 보여주는데 가격을 여기에 맞춘듯 https://x.com/OpenAI/status/2082878156483219672

Signed Mutt

29 Jul 2026, 13:06 UTC215 views1 reactionsread 7 August 2026
Forwarded from @minchoisfuturePhoto

오늘 러시아 체포영장 소식을 들은 두로프가 이런걸 올렸네요 Giga Chad 행동 https://x.com/telegram/status/2082364471339786357

🔥1

Signed Mutt

22 Jul 2026, 02:48 UTC≈2,860 viewsread 7 August 2026

1주 단위의 GPU 계산용량 거래소 등장 (코인 찍는거 아니니까 그냥 구경만 하셈) sfcompute라는 데에서 비슷하게 미래의 컴퓨팅용량을 거래할수 있게 했다가 CFTC한테 무허가 선물거래소라고 두드려맞고 철수했는데, 여기는 그러한 선례를 보고 현물인수만 가능하게 하고 100% 선불로 해서 레버리지가 불가능하게 하는 등 제한을 빡세게 건듯 https://www.getcomputable.com/

Signed Mutt

20 Jul 2026, 05:23 UTC446 viewsread 7 August 2026
Forwarded from @muohnismPhoto

앤쓰로픽 직원이 페이블 몇시간 좀 돌려서 80년동안 안풀린 수학문제 반례찾음 (자코비안 추측) 검산이 너무 간단한 식이라서 증명하고 말것도 없고 확정이라고 함 제가 페이블한테 반례주고 “내가 공책끄적이다가 찾았는데 필즈상되냐”라고 물어보니 믿을수가없다먼서 계속 다시 계산해보네요 참고로 저는 저게 머고 왜 중요한진 잘 모릅니다 🤡 https://x.com/__alpoge__/status/2079028340955197566?s=46

Signed Mutt

18 Jul 2026, 06:37 UTC≈2,390 views1 reactionsread 7 August 2026

Kimi가 클로드를 증류해서 사후학습에 썼는가: 높은 확률로 그럴것이라 생각 Kimi K3의 대성공이 클로드 증류빨인가: 도움은 될 수 있어도 근본적 비결은 될 수 없음 현재까지 알려진 바로 모델의 지능은 체급(파라미터 수)에 의해 주도적으로 결정되는 것이 정론으로 이해됨. 중국 회사들이 초거대 모델의 체급을 훨씬 높여서도 안정적인 학습이 가능하다는 것이 이번 키미 쇼크(있다면)의 본질이라 생각함. 클로드 증류 백날천날 하는건 전교1등 연습장 깜지 따라쓰는 건데 한계가 무조건 있을것(이라는 제 망상입니다)

👍1

Signed Mutt

18 Jul 2026, 05:13 UTC230 views1 reactionsread 7 August 2026
Forwarded from @catallactic

Kimi K3에 대한 OpenAI 전략 리드의 생각(여기서 오픈 웨이트는 오픈소스를 이야기 하는 거 같습니다): 1. 정말 좋은 모델이다. 이 정도 성능을 단순히 증류(distillation) 같은 것으로 설명할 수는 없다고 생각한다. 에이전트 기반 코딩(agentic coding) 세션에서는 2026년 1분기 기준 공개된 최고 수준의 모델들과 거의 동급으로 보인다. 다만 내가 제한적으로 사용해본 경험으로는 토큰을 상당히 많이 사용하는 편이었다. 그래서 실제로 운영 비용이 그렇게 저렴한 모델인지는 잘 모르겠다. 2. 개인적으로는 중국 정부가 이 정도 수준의 모델을 계속 오픈소스로 공개하도록 허용하고 있다는 점이 의외다. 분명히 말하지만, 나라는 개인의 입장에서 봤을, 이 정도 수준의 추가적인 위험(marginal risk)을 가진 모델

1

Signed Mutt

17 Jul 2026, 03:16 UTC413 viewsread 7 August 2026

가장 놀라운 점은 바로 가격인데 Sonnet 5와 API 가격이 거의 비슷함. Reasoning 효율이 안좋다면 같은작업에 토큰수를 더 많이 쓰니 실질적인 가격이 높아질수는 있지만 일단 Opus보다는 압도적으로 가성비 좋을 것 같고 GPT-5.6 Sol/Terra 와도 비교해봐야 할듯

Signed Mutt

17 Jul 2026, 03:13 UTC≈2,210 viewsread 7 August 2026

3T 파라미터 모델 Kimi K3 공개, 가중치는 7월 27까지 업로드 예정 중국 모델들이 벤치맥싱이 심하지만 Fable에 근접하거나 일부는 능가한다는 점에서 상당히 고무적 http://kimi.com/blog/kimi-k3

Signed Mutt

Showing the 12 most recent of 18 posts we hold for @mutt_tech. 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 — 235,304 of 1,151,006entries 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 7 August 2026 — this entry's latest reading, not the date you are reading this.

“Mutt Technologies” (@mutt_tech), 1,329 subscribers as measured 7 August 2026. Telegram Register, tgregister.com/channel/mutt_tech.

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.