Channel
@generativeai_gpt
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
30,415subscribers
+81 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1002130939818 |
|---|---|
| Type | Channel |
| Username | @generativeai_gpt |
| Description | ✅ Welcome to Generative AI Channel 👨💻 Join us to understand and use the tech 👩💻 Learn how to use Open AI & Chatgpt 🤖 The REAL No.1 AI Community Buy ads: https://telega.io/c/generativeai_gpt |
| Created | Between 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 8 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/generativeai_gpt |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 09:34 | 30,415 | +22 |
| 12 Aug 2026, 06:39 | 30,393 | +4 |
| 11 Aug 2026, 05:35 | 30,389 | +24 |
| 10 Aug 2026, 07:24 | 30,365 | +20 |
| 9 Aug 2026, 07:12 | 30,345 | +5 |
| 8 Aug 2026, 04:22 | 30,340 | +4 |
| 7 Aug 2026, 03:14 | 30,336 | +2 |
| 6 Aug 2026, 14:48 | 30,334 | first reading |
24 posts held, back to 29 June 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 18 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 10 August 2026 |
|---|---|
| Posts held | 24 (29 June 2026 – 10 August 2026) |
| Views total | 26,442 |
| Reactions total | 57 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 13 Aug 2026, 10:15 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 13 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.
118 reactions across 23 posts, in 5 distinct kinds. The most used accounts for 95.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 113 | 95.8% | |
| 👎 | 2 | 1.69% | |
| 👌 | 1 | 0.847% | |
| 🔥 | 1 | 0.847% | |
| 😍 | 1 | 0.847% |
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 24 of the 24 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 118reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 24 most recent posts we hold, published 29 June 2026 to 10 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.
🚀 Generative AI Fundamentals – Part 6 ⚙️ Fine-Tuning, LoRA, PEFT, RLHF & Model Alignment Fine-tuning and model adaptation are important topics for GenAI Engineer, LLM Engineer, and Applied AI interviews. 1. What is Fine-Tuning? Fine-tuning is the process of taking a pretrained model and training it further on a smaller, specialized dataset. Example: General LLM → Financial Documents → Fine-Tuning → Financial AI…
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Simplified Process: LLM generates responses → Humans evaluate → Preferred responses identified → Reward signal → Model optimized The goal is to make the model more: Helpful, Safe, Aligned, Instruction-following 11. What is Model Alignment? Model alignment means making an AI system behave consistently with intended human goals, values, and safety requirements. An aligned model should: Follow legitimate instruction…
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9. Components of a RAG System A production RAG system usually includes: Data Source Document Loader Text Splitter Embedding Model Vector Database Retriever LLM Response Generator Each component plays a role in retrieving and generating accurate responses. 10. Advantages of RAG Reduces hallucinations Uses the latest information Supports private enterprise data No need to retrain the model frequently Lo…
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🚀 Generative AI Fundamentals – Part 5 🔎 Embeddings, Vector Databases, Semantic Search & RAG Deep Dive These concepts are the backbone of modern enterprise GenAI applications. Most LLM Engineer and GenAI interviews include questions on them. 1. Why do LLMs need external knowledge? LLMs are trained on historical data and have limitations: Knowledge becomes outdated Cannot access private company documents by defau…
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Benefits: Improves diversity Reduces repetitive outputs Balances creativity and quality Top-p is often tuned together with temperature. 10. What is Max Tokens? Max Tokens defines the maximum number of tokens the model is allowed to generate in its response. Example: Max Tokens = 100 The response stops after generating up to 100 output tokens, even if the answer could be longer. This helps control: Response…
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🚀 Generative AI Fundamentals – Part 2 🧠 Large Language Models (LLMs) Deep Dive Understanding LLMs is one of the most important topics in GenAI interviews. 1. What is a Large Language Model (LLM)? A Large Language Model (LLM) is a deep learning model trained on massive amounts of text data to understand, generate, summarize, translate, and reason about human language. LLMs are built using the Transformer architec…
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Example: Cat → [0.34, 0.67, 0.11...] Dog → [0.32, 0.69, 0.15...] Car → [0.91, 0.18, 0.76...] Cat and Dog embeddings are closer than Cat and Car because their meanings are more similar. Used in: RAG, Semantic Search, Recommendation Systems, Vector Databases 11. Why is Generative AI so Powerful? Because it combines: Massive datasets Powerful GPUs Transformer architecture Large-scale pretraining Cloud comput…
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🚀 Generative AI Fundamentals You Should Know 1. What is Generative AI? Generative AI is a branch of Artificial Intelligence that creates new content by learning patterns from existing data. Unlike traditional AI, which mainly predicts or classifies, Generative AI produces original outputs. It can generate: Text Images Audio Video Code Music 3D models Example Input: "Write a Python function to sort a list…
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GenAI isn’t a chapter in this course. It’s the spine. GANs & Diffusion models → LLM fine-tuning with LoRA → RAG with vector DBs → Autonomous AI Agents. If a syllabus doesn’t have these in 2026, it’s history class. Certification in AI & ML - Vishlesan i-Hub, IIT Patna ✅ 9 Months | Online | IIT faculty & industry mentors ✅ Deploy GenAI projects end-to-end ✅ Placement support through Masai's network of 5000+ compa…
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LLMOps vs MLOps
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Showing the 12 most recent of 24 posts we hold for @generativeai_gpt. 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 — 921,333 of 1,169,250entries 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.
Named by
Channels on the register whose posts name this channel's handle.
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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“Generative AI” (@generativeai_gpt), 30,415 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/generativeai_gpt.
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.