Telegram RegisterThe public register of Telegram

Channel

AI_repo_News

@aigithubnews

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

274subscribers

+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-1003771100203
TypeChannel
Username@aigithubnews
CreatedBetween 1 February 2026 and 30 June 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live8 August 2026
Measurements held3
On Telegramt.me/aigithubnews

Growth

273274273.57 Aug 2026, 20:47 — 273 subscribers8 Aug 2026, 08:04 — 274 subscribers8 Aug 2026, 08:46 — 274 subscribers7 Aug 2026, 20:478 Aug 2026, 08:46
3 measurements taken within a single day, 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 273–274 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
8 Aug 2026, 08:46274no change
8 Aug 2026, 08:04274+1
7 Aug 2026, 20:47273first reading

Engagement

20 posts held, back to 8 August 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
1.48%
avg views ÷ 274 subscribers
Avg views / post
4.0
20 posts measured
Reaction rate
this channel exposes no reaction counts
Posts in window
20
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 8 August 2026
Posts held20 (8 August 20268 August 2026)
Views total81
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken8 Aug 2026, 08:46 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
Links
6,160

Lifetime counters from Telegram’s own channel header, read 8 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.

Recent posts

8 Aug 2026, 08:44 UTC2 viewsread 8 August 2026

RongxinAI: 로컬 AI 에이전트 워크스페이스 RongxinAI는 파일, 터미널, 브라우저와 통합되어 작업할 수 있는 오픈소스 로컬 AI 에이전트입니다. 문서 작업, 브라우저 기반 작업, 내부 도구 자동화 등 다양한 용도로 활용할 수 있습니다. https://github.com/rongxinzy/RongxinAI ⭐ Stars 21 / Forks 2 #에이전트 #ai_agents #깃허브 #github #agent

8 Aug 2026, 08:39 UTC3 viewsread 8 August 2026

Markdown 사양을 위한 인라인 리뷰 코멘트 이 프로젝트는 Markdown 문서에 대한 인라인 리뷰 코멘트를 제공하며, MCP 서버를 통해 AI 에이전트와 직접 피드백을 주고받을 수 있습니다. 사용자는 로컬 앱을 통해 리뷰 워크플로우를 간편하게 실행하고, 작업 중간에 리뷰 요청을 할 수 있습니다. https://github.com/dejuknow/md-redline ⭐ Stars 31 / Forks 4 #에이전트 #ai_agents #mcp #도구연동 #노트앱

8 Aug 2026, 08:34 UTC4 viewsread 8 August 2026

BMG-M3 파인튜닝 임베딩 모델 이 모델은 BAAI/bge-m3 기반의 SentenceTransformer로, 문장 유사도 측정에 사용됩니다. 다양한 크기의 임베딩을 지원하며, 품질 손실 없이 잘라낼 수 있는 기능이 있어 텍스트 임베딩 추출에 적합합니다. https://huggingface.co/Luong23/bmg-m3-finetune-embedding ⬇️ Downloads 0 / Likes 0 #rag #검색증강 #ai인프라 #ai_infra #허깅페이스

8 Aug 2026, 08:29 UTC5 viewsread 8 August 2026

E5 파인튜닝 임베딩 모델 이 모델은 문장 유사성 평가를 위한 임베딩을 생성하며, Sentence Transformers 라이브러리를 통해 쉽게 사용할 수 있습니다. 다양한 데이터셋에서 훈련된 이 모델은 텍스트 임베딩 추출에 적합합니다. https://huggingface.co/Luong23/e5-finetune-embedding ⬇️ Downloads 0 / Likes 0 #rag #검색증강 #ai인프라 #ai_infra #허깅페이스

8 Aug 2026, 08:24 UTC5 viewsread 8 August 2026

오픈 소스 기여를 위한 자율 AI 에이전트 ContribAI는 GitHub의 오픈 소스 프로젝트를 탐색하고, 코드를 분석하며, 수정 사항을 생성해 Pull Request를 제출하는 자율 AI 에이전트입니다. 40개 이상의 명령어를 CLI 또는 인터랙티브 메뉴를 통해 사용할 수 있어, 개발자들이 코드 품질을 향상시키는 데 도움을 줍니다. https://github.com/tang-vu/ContribAI ⭐ Stars 246 / Forks 87 #llm #대형언어모델 #에이전트 #ai_agents #ai보안

8 Aug 2026, 08:19 UTC4 viewsread 8 August 2026

Cognito: AI 기반 웹 노트 어시스턴트 Cognito는 Chrome 브라우저에서 자연어로 질문하고 제어할 수 있는 지능형 사이드바 어시스턴트입니다. RAG, TTS/STT 기능을 활용하여 메모를 작성하고, 필요에 따라 히스토리를 검색하거나 삭제할 수 있습니다. https://github.com/3-ark/Cognito-AI_Sidekick ⭐ Stars 57 / Forks 7 #llm #대형언어모델 #깃허브 #github #ai

8 Aug 2026, 08:14 UTC3 viewsread 8 August 2026

Trace-First Workflow 시작하기 Trace-First Workflow(TFW)는 AI 세션 간의 결정, 추론 및 지식을 보존하는 방법론입니다. 이 프로젝트는 코드, 연구, 분석 및 비즈니스 프로세스 작업을 지원하며, 새로운 팀원이 기존 지식을 쉽게 활용할 수 있도록 돕습니다. https://github.com/saubakirov/trace-first-starter ⭐ Stars 28 / Forks 1 #깃허브 #github #agentic_ai #ai #knowledge_management

8 Aug 2026, 08:09 UTC5 viewsread 8 August 2026

개인 영양 추적을 위한 원격 MCP 서버 식사를 기록하고 매크로를 추적하며 대화를 통해 영양 이력을 검토할 수 있는 기능을 제공합니다. Claude.ai와 연결하여 사용자 인증을 통해 데이터를 안전하게 관리할 수 있습니다. https://github.com/akutishevsky/nutrition-mcp ⭐ Stars 35 / Forks 20 #mcp #도구연동 #깃허브 #github #bun

8 Aug 2026, 08:04 UTC6 viewsread 8 August 2026

Gemma 4-12B 모델 Gemma 4-12B 모델은 Mila 플랫폼에서 사용할 수 있는 양자화된 모델로, 로컬 저장소에서 파일을 관리하며, 모델 로딩 시 Mila를 필요로 합니다. 이 모델은 추가적인 미세 조정 없이 원래 학습한 내용을 그대로 유지합니다. https://huggingface.co/mila-llm/gemma-4-12b-it-fp4 ⬇️ Downloads 0 / Likes 1 #llm #대형언어모델 #허깅페이스 #huggingface #mila

8 Aug 2026, 07:59 UTC5 viewsread 8 August 2026

Qwen3.6 27B + 35B 최적화 정보 Radeon AI Pro R9700을 최적화한 경험을 공유하며, 단일 카드에서의 설정 방법을 제공합니다. 이 정보는 AI 시스템을 구성하려는 사용자에게 유용할 것입니다. https://www.reddit.com/r/LocalLLaMA/comments/1viq0pq/qwen36_27b_35b_on_vllm_single_r9700_gfx1201/ Reddit 추천 0 / 댓글 0 #비전ai #computer_vision #ai인프라 #ai_infra #데이터셋

8 Aug 2026, 07:54 UTC4 viewsread 8 August 2026

Firehouse-Cactus-1.0 모델 이 모델은 텍스트 생성 기능을 제공하며, mlx, safetensors, qwen3_5, unsloth와 같은 다양한 기술을 활용합니다. 대화형 응용 프로그램에서 사용될 수 있으며, 사용자와의 상호작용을 통해 자연스러운 대화를 생성하는 데 적합합니다. https://huggingface.co/Ironwood-LLM-Team/Firehouse-Cactus-1.0 ⬇️ Downloads 0 / Likes 1 #llm #대형언어모델 #허깅페이스 #huggingface #mlx

8 Aug 2026, 07:49 UTC6 viewsread 8 August 2026

DeepSeek V4 Flash 0731 사용 후기 DSV4F 0731은 일상 업무와 코딩 작업을 효율적으로 처리할 수 있는 강력한 도구입니다. Hermes 에이전트와 OpenCode를 활용하여 문서 관리와 통합 작업을 손쉽게 수행할 수 있습니다. https://www.reddit.com/r/LocalLLaMA/comments/1vio0x6/deepseek_v4_flash_0731_appreciation_post/ Reddit 추천 0 / 댓글 0 #에이전트 #ai_agents #레딧 #reddit #local_lla_ma

Showing the 12 most recent of 20 posts we hold for @aigithubnews. 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 — 1,078,908 of 1,350,102entries 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.

Mentions

Named by 1 registered channel — 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 8 August 2026 — this entry's latest reading, not the date you are reading this.

“AI_repo_News” (@aigithubnews), 274 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/aigithubnews.

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.