CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation https://arxiv.org/abs/2602.24286 @LLM_learning
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Channel
@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.
| Telegram ID | -1001465350755 |
|---|---|
| Type | Channel |
| Username | @LLM_learning |
| Created | Between 1 April 2019 and 31 October 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 21 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 21 August 2026 |
| On Telegram | t.me/LLM_learning |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 21 Aug 2026, 09:52 | 742 | +1 |
| 15 Aug 2026, 00:33 | 741 | +1 |
| 8 Aug 2026, 02:03 | 740 | no change |
| 7 Aug 2026, 13:40 | 740 | first reading |
20 posts held, back to 4 March 2025 — 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 29 June 2026. An engagement rate over an empty window would be a number about nothing.
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.
67 reactions across 19 posts, in 4 distinct kinds. The most used accounts for 59.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 40 | 59.7% | |
| ❤ | 15 | 22.4% | |
| 🔥 | 9 | 13.4% | |
| ❤🔥 | 3 | 4.48% |
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.
CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation https://arxiv.org/abs/2602.24286 @LLM_learning
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When AIs Judge AIs: The Rise of Agent-as-a-Judge Evaluation for LLMs 📚 Read @LLM_learning
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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…
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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
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…
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Owen 3 release 📖 Blog @LLM_learning
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SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators 📚 Read @LLM_learning
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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
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Large Language Model Course 😮 📌 Github @LLM_learning
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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…
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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
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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, …
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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.
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.
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.
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.