Channel
@machinelearningresearchnews
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
3,348subscribers
+87 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1002015513467 |
|---|---|
| Type | Channel |
| Username | @machinelearningresearchnews |
| Created | Between 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 30 August 2026 |
| Measurements held | 10 |
| Confirmed unchanged | 1 time, most recently 30 August 2026 |
| On Telegram | t.me/machinelearningresearchnews |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 30 Aug 2026, 20:34 | 3,348 | +20 |
| 27 Aug 2026, 13:48 | 3,328 | +7 |
| 24 Aug 2026, 04:26 | 3,321 | +12 |
| 20 Aug 2026, 16:26 | 3,309 | +9 |
| 17 Aug 2026, 19:57 | 3,300 | +15 |
| 14 Aug 2026, 13:24 | 3,285 | +14 |
| 11 Aug 2026, 04:17 | 3,271 | +7 |
| 8 Aug 2026, 13:40 | 3,264 | +3 |
| 7 Aug 2026, 18:33 | 3,261 | no change |
| 7 Aug 2026, 18:25 | 3,261 | first reading |
20 posts held, back to 14 July 2026 — 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.
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 3 of 5 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 5 August 2026 |
|---|---|
| Posts held | 20 (14 July 2026 – 5 August 2026) |
| Views total | 1,868 |
| Reactions total | 10 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 18:33 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.
Measured directly from 4 videos 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.
31 reactions across 13 posts, in 7 distinct kinds. The most used accounts for 71.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 22 | 71.0% | |
| 🌚 | 2 | 6.45% | |
| 👍 | 2 | 6.45% | |
| 🔥 | 2 | 6.45% | |
| 👀 | 1 | 3.23% | |
| 👏 | 1 | 3.23% | |
| 😁 | 1 | 3.23% |
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 13 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 31reactions 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 14 July 2026 to 5 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.
NVIDIA Releases Alpamayo 2 Super: A 34B Open Vision-Language-Action Model for Robotaxis and Autonomous Driving Under OpenMDW-1.1 Here are some key takeaways: 1. The architecture is split → 32B VLM backbone, built on Cosmos 3 Super Reasoner, post-trained with reinforcement learning → 2.3B diffusion-based action decoder → Roughly 3x the scale of the 10B Alpamayo 1 and Alpamayo 1.5 2. It ranks first on LingoQA → …
How to Secure AI Agents, MCP Servers, and LLM Apps in Production Application security rests on one assumption: software does what its code says. ---AI agents broke it. Mend.io's new practitioner guide — 𝘚𝘦𝘤𝘶𝘳𝘪𝘯𝘨 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴, 𝘔𝘊𝘗 𝘴𝘦𝘳𝘷𝘦𝘳𝘴 & 𝘓𝘓𝘔 𝘢𝘱𝘱𝘴 — starts from that break. An agent's behavior emerges from the model, the system prompt, retrieved context, and the tools it's permitted to call. The failure modes never a…
Alibaba Qwen Releases Qwen3.8-Max: A 2.4 Trillion Parameter MoE Model and the Most Capable One in the Qwen Family to Date 1. What shipped → 2.4T parameters, mixture-of-experts → 1M context, 991K max input, 131K max output → Text, image and video input → $2.00 input, $6.00 output, $0.25 cached input per 1M tokens → Open weights next week 2. Where it leads Fable5 → Terminal Bench 2.1: 86.6 vs 84.6 → PaperBench: 93.0…
❤1
AMD released Instella-MoE-16B-A3B, a fully open Mixture-of-Experts model trained from scratch on AMD Instinct MI300X and MI325X GPUs. Here are some key takeaways: 1. The model → 16B total parameters, 2.8B active per token → 2 shared experts plus 6 of 64 routed, across 27 layers → 7.1T pre-training tokens, context extended from 4K to 64K 2. Where the efficiency comes from → FarSkip-Collective overlaps expert-parall…
❤5
MiniMax Releases MiniMax H3: An Omni-Modal Video Model That Generates 15-Second 2K Clips With Native Stereo Audio. It ranks #1 in video editing on Artificial Analysis, at $0.13 per second of 2K output. Here are some important key takeaways: 𝟭. 𝗧𝗵𝗲 𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗲𝗿 𝗶𝘀 𝘁𝗵𝗲 main 𝘀𝘁𝗼𝗿𝘆 MiniMax rebuilt the H-series tokenizer from scratch as H3-VAE. → 4× gain in effective sequence length → That compression is what makes nati…
❤4
We just released 'Token Saver' for Claude-Desktop: An Open-Source MCP Extension Using Local Hybrid RAG to Cut Claude PDF Token Costs 90-99% When you drop a 200-page document into Claude Desktop, the full context gets re-sent on every single turn. That compounding "PDF Tax" adds up fast—both in token costs and context window bloat. How it works: Instead of uploading raw documents to the cloud, Token Saver runs a li…
Liquid AI released two bidirectional encoders this week: LFM2.5-Encoder-230M and LFM2.5-Encoder-350M. Here's what's actually interesting: 1. They converted a decoder instead of training from scratch Both models start from the LFM2.5 decoder backbones. Three changes turn them into encoders: the causal mask is replaced with a bidirectional one, the short convolutions are made non-causal with symmetric center padding…
👍1
Sakana AI Releases Fugu-Cyber: An Orchestration Model Reporting 86.9% on CyberGym and 72.1% on CTI-REALM It is not a new frontier model. It is a third endpoint on the Fugu orchestrator, tuned for security reasoning. Here's what's actually interesting. 1. The CyberGym number only means something with context → Fugu-Cyber: 86.9% → GPT-5.5-Cyber: 85.6% → Claude Mythos Preview: 83.1% → Best agent in the original Cyber…
🌚2
Meet Open Dreamer: A JAX/Flax Reproduction of the Dreamer 4 World Model Pipeline, With the Full Training Recipe Published No VAE. No KL loss. No adversarial loss. Here's how it works: 1. Two models, one backbone A causal video tokenizer and an action-conditioned dynamics model share the same block-causal transformer. Space layers move information inside a frame. Causal time layers move it between frames. 2. The t…
Claude Opus 5 is out. The agentic numbers, not the coding ones, are the story: • FrontierBench v0.1 → 43.3% vs Opus 4.8's 18.7% • OSWorld 2.0 → 70.57% vs 55.7% • AutomationBench → 26.0% vs 17.0% (Fable 5: 17.4%) • ARC-AGI-3 → 30.16%, ~4x the previous best on the leaderboard Same $5/$25 pricing. Fable 5 still edges it on SWE-bench Pro (80.0 vs 79.2). Full analysis: https://www.marktechpost.com/2026/07/24/meet-the-…
Andrew Ng Just Released OpenWorker: An Open-Source, Local-First Desktop AI Coworker That Returns Finished Deliverables Instead of Chat Here are some key takeaways: 1. The approval layer is typed, not cosmetic Most desktop agents bolt approvals onto the UI. OpenWorker classifies every tool call into one of four risk classes before it runs: → read — no side effects, always allowed → write_local — mutates the works…
❤1👀1
Meet Gigatoken: A Rust BPE Tokenizer that Encodes Text at 24.53 GB/s on a 144-core AMD EPYC 9565, against 24.8 MB/s for HuggingFace tokenizers and 36.0 MB/s for tiktoken on the same machine Both baselines are multithreaded Rust implementations. The difference comes from how the work is structured, not the language. 1. Pretokenization without a regex engine Most tokenizers delegate pretokenization to a regex engine.…
❤1🔥1
Showing the 12 most recent of 20 posts we hold for @machinelearningresearchnews. 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 — 738,359 of 1,628,524entries 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.
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.
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.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 30 August 2026 — this entry's latest reading, not the date you are reading this.
“Artificial Intelligence AI News” (@machinelearningresearchnews), 3,348 subscribers as measured 30 August 2026. Telegram Register, tgregister.com/channel/machinelearningresearchnews.
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.