Channel
@ai_review
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
345subscribers
-1 since we began measuring on 5 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001048626082 |
|---|---|
| Type | Channel |
| Username | @ai_review |
| Created | 12 April 2016 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 21 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 21 August 2026 |
| On Telegram | t.me/ai_review |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 21 Aug 2026, 13:26 | 345 | -1 |
| 6 Aug 2026, 21:32 | 346 | no change |
| 5 Aug 2026, 23:52 | 346 | first reading |
20 posts held, back to 18 October 2019 — 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 12 June 2026. An engagement rate over an empty window would be a number about nothing.
14 reactions across 8 posts, in 3 distinct kinds. The most used accounts for 42.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 6 | 42.9% | |
| ❤ | 4 | 28.6% | |
| 🔥 | 4 | 28.6% |
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 11 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 14reactions 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 18 October 2019 to 12 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.
We just published our write-up on semantic search in LinkedIn’s Hiring Assistant — the path from "no relevance labels exist" to a production system scoring 1B+ profiles in real time. The core problem was familiar: engagement signals (InMail Sends/Accepts) measure interest, not qualification fit. The principled fix was a supervision flywheel — product policy encoded into an Expert Judge, a scalable reasoning-based tea…
❤3
First time doing AI video generation end-to-end during an internal LinkedIn hackathon — and it was way more “filmmaking” than I expected. I also caught myself slipping into Lean Startup mode for customer validation — quick iterations, real user reactions. Honestly, it reminded me of the good old startup days. Check out the generated video samples: 1) https://www.linkedin.com/feed/update/urn:li:activity:74227470558628…
👍2
All set for ACL2025 in Vienna 🇦🇹 and KDD2025 in Toronto 🇨🇦! If you’re attending and into industry-scale embedding-based retrieval, LLMs for search and recsys, LLMs-as-judge, or would like to know about LinkedIn‘s initiatives around semantic search and agentic experience in general and particularly for Recruiter Search, let’s connect
👍2
Signed Nikita Zh
GPU Retrieval Large-scale retrieval once required elaborate ANN indexes — HNSW, IVFPQ, PQ-compressed graphs — because scanning billions of vectors exactly seemed impractical. That assumption has flipped in the last two years. Commodity A100/H100/H200 GPUs now perform flat matrix-multiplication over > 100 M embeddings in only a few milliseconds while still honoring business-facet filters. In effect, it is cheaper to …
❤1
Signed Nikita Zh
This is our team’s work on LLM productionization from a year ago. Since September 2024, it has powered the most member experience in job recommendations and search. A strong example of thoughtful ML system design, it may be particularly relevant for ML/AI practitioners. https://www.linkedin.com/blog/engineering/ai/jude-llm-based-representation-learning-for-linkedin-job-recommendations
🔥3
Signed Nikita Zh
Channel name was changed to «Practical AI/ML Playbook»
This article by Dan Fu, a co-author of very popular FlashAttention layer, surveys new approaches to increasing sequence length in machine learning models, with a focus on large language models (LLMs). This ability was recognized by Sam Altman, CEO of Open.ai, as key for the GPT4 breakthrough. Covering the bleeding edge research, he introduced Hyena, a model that uses convolutional filters and gates to achieve near-l…
🔥1
Signed Nikita Zh
Channel name was changed to «Practical AI/ML»
At LinkedIn, our vision is to create economic opportunities for every member of the global workforce. With the help of cutting-edge technology and human creativity, we work tirelessly to make that vision a reality. Recently, we've harnessed advanced generative AI and large language models, specifically the OpenAI GPT family, to enhance our offerings even further. We're excited to introduce new AI-powered experiences…
👍1
Signed Nikita Zh
Google publishes a very insightful paper that describes the ML part in their production ads ranking system. Here I spotlight just some of their lessons, providing some notes💡 from our practice at Linkedin: 1. They employ online learning motivated by non-stationary data • The model is trained with a single sequential pass through all data + progressive validation • Importance sampling to emphasize recent examples …
Signed Nikita Zh
LeCun’s recent thread is informally about the same concept https://twitter.com/ylecun/status/1591463668612730880
Signed Nikita Zh
V. Vapnik’s statistical learning with invariants [1] [2] is a fascinating read on the machine learning theory. It is the completion of his statistical learning theory aka VC-theory developed in 70s (Vapnik is 85yo!). Unfairly it caught less attention in the community than fancy tricks with Transformers. 1) Unlike emotional debates on AGI, he proves that “intelligence is reduced to the selection of predicates preserv…
Signed Nikita Zh
Showing the 12 most recent of 20 posts we hold for @ai_review. 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.
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.
“Practical AI/ML Playbook” (@ai_review), 345 subscribers as measured 21 August 2026. Telegram Register, tgregister.com/channel/ai_review.
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.