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Channel

Github LLMs

@LLM_learning

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

742subscribers

+2 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-1001465350755
TypeChannel
Username@LLM_learning
CreatedBetween 1 April 2019 and 31 October 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live21 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 21 August 2026
On Telegramt.me/LLM_learning

Growth

7407427417 August 2026 — 740 subscribers8 August 2026 — 740 subscribers15 August 2026 — 741 subscribers21 August 2026 — 742 subscribers7 August 202621 August 2026
4 measurements spanning 14 days, net +2. 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 740–742 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
21 Aug 2026, 09:52742+1
15 Aug 2026, 00:33741+1
8 Aug 2026, 02:03740no change
7 Aug 2026, 13:40740first reading

Engagement

20 posts held, back to 4 March 2025the 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 29 June 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Video runtime
4s
Average length
4s

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

67 reactions across 19 posts, in 4 distinct kinds. The most used accounts for 59.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍4059.7%
1522.4%
🔥913.4%
❤‍🔥34.48%

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 67reactions 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 4 March 2025 to 29 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

29 Jun 2026, 18:49 UTC≈2,700 views3 reactionsread 8 August 2026

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation https://arxiv.org/abs/2602.24286 @LLM_learning

3

2 Oct 2025, 17:49 UTC≈1,050 views3 reactionsread 8 August 2026

When AIs Judge AIs: The Rise of Agent-as-a-Judge Evaluation for LLMs 📚 Read @LLM_learning

3

5 Aug 2025, 07:15 UTC≈1,260 views3 reactionsread 8 August 2026
Forwarded from @scopeofaiPhoto

With so many LLM papers being published, it's hard to keep up and compare results. This study introduces a semi-automated method that uses LLMs to extract and organize experimental results from arXiv papers into a structured dataset called LLMEvalDB. This process cuts manual effort by over 93%. It reproduces key findings from earlier studies and even uncovers new insights—like how in-context examples help with coding

3

16 Jun 2025, 09:04 UTC≈5,550 viewsread 8 August 2026

Deep-Live-Cam Real time face swap and one-click video deepfake with only a single image Creator: Hacksider Stars ⭐️: 50,498 Forked by: 7,491 Github Repo: https://github.com/hacksider/Deep-Live-Cam @LLM_learning

13 May 2025, 17:24 UTC≈1,280 views3 reactionsread 8 August 2026
Photo

Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating their prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and violations of content regulator policies. Limited studies have been conducted to formalize and analyze these attacks and their mitigations. We bridge this gap by proposin

🔥3

1 May 2025, 12:21 UTC≈6,770 views4 reactionsread 8 August 2026
Photo

Owen 3 release 📖 Blog @LLM_learning

3🔥1

14 Apr 2025, 05:47 UTC≈4,350 views3 reactionsread 8 August 2026
Photo

SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators 📚 Read @LLM_learning

👍3

12 Apr 2025, 11:52 UTC985 views4 reactionsread 8 August 2026
Forwarded from @Machine_learnPhoto

Large Language Model Agent: A Survey on Methodology, Applications and Challenges Paper: https://arxiv.org/pdf/2503.21460v1.pdf Code: https://github.com/luo-junyu/awesome-agent-papers @Machine_learn

👍4

5 Apr 2025, 07:49 UTC≈4,280 views2 reactionsread 8 August 2026
Photo

Large Language Model Course 😮 📌 Github @LLM_learning

👍2

4 Apr 2025, 09:20 UTC955 views6 reactionsread 8 August 2026
Photo

This essay explores whether contemporary Large Language Models (LLMs) can pass the Turing test, a benchmark proposed by Alan Turing to evaluate machine intelligence. The study involved evaluating four systems—GPT-4.5, LLaMa-3.1-405B, GPT-4o, and ELIZA—in randomized, controlled three-party Turing tests with two independent populations: UCSD undergraduate students and Prolific workers. Participants engaged in simultane

🔥4👍2

28 Mar 2025, 07:37 UTC820 views3 reactionsread 8 August 2026
Forwarded from @Machine_learnVideo

Magic of open source is taking over the Video LoRA space✨ just dropped👇🔥 🍬LTX video community LoRA trainer with I2V support 🍬LTX video Cakify LoRA 🍬LTX video Squish LoRA (🧨diffusers & comfy workflow) trainer: https://github.com/Lightricks/LTX-Video-Trainer LoRA: https://huggingface.co/Lightricks/LTX-Video-Cakeify-LoRA LoRA2 : https://huggingface.co/Lightricks/LTX-Video-Squish-LoRA 🔥 @Machine_learn

👍3

27 Mar 2025, 17:20 UTC764 views4 reactionsread 8 August 2026
Photo

This repository is a curated collection of survey papers focused on Large Language Models (LLMs), organized to help researchers and practitioners navigate the rapidly evolving field. It compiles existing surveys across multiple topics, including foundational overviews of LLMs, technical aspects like Transformer architectures and efficient model design, and societal considerations such as alignment with human values,

👍4

Showing the 12 most recent of 20 posts we hold for @LLM_learning. 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.

Mentions

Names

Channels on the register whose handles appear in this channel's posts.

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 21 August 2026 — this entry's latest reading, not the date you are reading this.

“Github LLMs” (@LLM_learning), 742 subscribers as measured 21 August 2026. Telegram Register, tgregister.com/channel/LLM_learning.

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.