Channel
@KDnuggets
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
23,458subscribers
-58 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1001105757390 |
|---|---|
| Type | Channel |
| Username | @KDnuggets |
| Description | Posts from world's largest datascientists community and latest trends learning articles in Machine learning, deep learning, AI, IOT and tools Part of @nuggetsnetwork Instagram: kdnuggets Chat @datasciencechats Admin: @LordAdminBot |
| Created | Between 1 February 2017 and 28 February 2019— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 9 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/KDnuggets |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 07:02 | 23,458 | -21 |
| 11 Aug 2026, 06:15 | 23,479 | -6 |
| 10 Aug 2026, 06:45 | 23,485 | -10 |
| 9 Aug 2026, 05:03 | 23,495 | -8 |
| 8 Aug 2026, 05:01 | 23,503 | -8 |
| 7 Aug 2026, 08:14 | 23,511 | -4 |
| 6 Aug 2026, 05:05 | 23,515 | -1 |
| 6 Aug 2026, 02:20 | 23,516 | no change |
| 6 Aug 2026, 02:19 | 23,516 | first reading |
22 posts held, back to 26 December 2024 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 17 pagesof 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.
| Window | Rolling 30 days · latest post in window 11 August 2026 |
|---|---|
| Posts held | 22 (26 December 2024 – 11 August 2026) |
| Views total | 8,033 |
| Reactions total | 18 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 22:48 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.
Lifetime counters from Telegram’s own channel header, read 12 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.
18 reactions across 8 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 18 | 100.0% |
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 9 of the 22 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 18reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 22 most recent posts we hold, published 26 December 2024 to 11 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.
🔬 AI Research Digest 📅 Week of August 4–10, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🤖 AOSpec: Action and Observation Co-Speculation for Low-Latency Agent Serving Authors/Org: Hao Mark Chen, Jinnan Guo, Wayne Luk | arXiv: 2608.00881 Bottleneck solved: End-to-end inference latency in production AI agent systems. AOSpec reduces perceived response time by co-speculating actions and observations in parallel before they are need…
❤1
🤖 AI Weekly Digest 📅 Week of Aug 4–Aug 10, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 💸 OpenAI Slashes GPT-5.6 Luna Prices by 80% OpenAI cut GPT-5.6 Luna to just $0.20 per million input tokens (down from $1.00), making frontier-quality AI dramatically cheaper for high-volume workloads. Developers building automation pipelines, internal tools, or API-heavy products can now scale at a fraction of the previous cost. 🔗 OpenAI Sla…
❤3
🔬 AI Research Digest 📅 Week of Jul 28–Aug 4, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧠 Explorative Modeling: Unlocking a Third Pretraining Axis Authors/Org: Alexi Gladstone, Heng Ji, Yilun Du | arXiv: 2607.27372 Bottleneck solved: Mode collapse and blurry predictions in generative models — by exploring K candidate generations and training on the best match, models commit to sharp modes instead of averaging them. FLOP effic…
❤2
🤖 AI Weekly Digest 📅 Week of Jul 28–Aug 3, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧮 OpenAI's Astra Solves 10 Long-Standing Math Problems OpenAI's next-generation model Astra solved 10 previously open problems in mathematics and theoretical computer science — including the first-ever proof that non-sofic groups exist — publishing formal Lean 4 proofs on GitHub for roughly $2,000 in compute. This signals that AI is crossing…
❤1
Channel name was changed to «AI, Data Science, Machine Learning & IOT»
🔬 AI Research Digest 📅 Week of Jul 21–28, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧠 codebase-memory-mcp — Persistent Knowledge Graph for AI Coding Agents Authors/Org: DeusData | GitHub: DeusData/codebase-memory-mcp Bottleneck solved: Eliminates redundant codebase scanning by AI agents, cutting token usage for structural queries by up to 99%. Builds a persistent knowledge graph (functions, classes, call chains) using tree-s…
❤1
🤖 AI Weekly Digest 📅 Week of Jul 21–Jul 27, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧠 Claude Opus 5 Arrives as Anthropic's New Frontier Model Anthropic released Claude Opus 5 on July 25, its most capable model yet — benchmarks show it outperforms GPT-5.6 Sol on FrontierCode 1.1 with a higher mergeability score at lower rollout cost. For developer and data teams, Opus 5 excels at long-horizon agentic coding tasks, complex r…
❤4
🔬 AI Research Digest 📅 Week of July 14–21, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🐦 Colibri: Run a 744B MoE Model on 25 GB of RAM Authors/Org: JustVugg (open-source) | GitHub: JustVugg/colibri Bottleneck solved: Hardware/cost barriers for running frontier-scale models locally — no GPU, no cloud spend required. Colibri is a ~2,400-line pure-C inference engine that streams only the active MoE experts from disk at runtime, k…
❤1
🤖 AI Weekly Digest 📅 Week of July 14–20, 2026 ━━━━━━━━━━━━━━━━━━━━━━━━ 1. 🧠 Claude Sonnet 5: Anthropic's Most Agentic Model Yet Anthropic launched Claude Sonnet 5 on June 30, making it the default model for all Free and Pro users starting July 1 — it features a 1M-token context window, adaptive thinking on by default, and near-Opus 4.8 performance at lower cost ($2/$10 per 1M tokens through August). For developers a…
❤5
Brief Summary: OpenAI officially released GPT-5.5 on April 23, 2026, transitioning from a chat assistant to a fully "agentic" system. Here are the 5 key updates: Agentic Workflows: Autonomously handles multi-step tasks across browsers and software with self-correction. SOTA Reasoning: Achieves 82.7% on Terminal-Bench 2.0, significantly outperforming Claude 4.7 and Gemini 3.1. 1M Token Context: Vastly improved reliab…
🚀 Popular YouTube Channels to Learn AI & ML 🤖📚 Whether you’re a beginner or going deep into research, these channels are gold for AI/ML learning 👇 1️⃣ Andrej Karpathy – Learn deep learning straight from a legend 👉 Watch here 2️⃣ Sebastian Raschka – Clear explanations on ML, PyTorch & research 👉 Watch here 3️⃣ sentdex – Hands-on Python, ML & practical coding 👉 Watch here 4️⃣ StatQuest with Josh Starmer – ML & stats ma…
Google is expanding the Gemini 3 model family with the release of Gemini 3 Flash, which offers frontier intelligence built for speed at a fraction of the cost @kdnuggets @datasciencechats Read More: https://blog.google/products/gemini/gemini-3-flash
Showing the 12 most recent of 22 posts we hold for @KDnuggets. 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 — 25,776 of 1,160,990entries 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
Named by 2 registered channels — 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.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“AI, Data Science, Machine Learning & IOT” (@KDnuggets), 23,458 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/KDnuggets.
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.