Channel
@llm_zoomcamp
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
4,701subscribers
+34 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1002136268305 |
|---|---|
| Type | Channel |
| Username | @llm_zoomcamp |
| 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 | 19 September 2026 |
| Measurements held | 11 |
| Confirmed unchanged | 1 time, most recently 19 September 2026 |
| On Telegram | t.me/llm_zoomcamp |
Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 13 September 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 19 Sept 2026, 00:00 | 4,701 | +4 |
| 10 Sept 2026, 22:20 | 4,697 | +7 |
| 5 Sept 2026, 12:18 | 4,690 | +8 |
| 29 Aug 2026, 08:08 | 4,682 | +2 |
| 26 Aug 2026, 12:02 | 4,680 | +3 |
| 23 Aug 2026, 16:38 | 4,677 | -1 |
| 20 Aug 2026, 01:51 | 4,678 | -4 |
| 17 Aug 2026, 11:04 | 4,682 | +8 |
| 14 Aug 2026, 00:36 | 4,674 | +6 |
| 8 Aug 2026, 05:22 | 4,668 | +1 |
| 7 Aug 2026, 11:31 | 4,667 | first reading |
20 posts held, back to 22 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 7 pages of 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 4 August 2026. An engagement rate over an empty window would be a number about nothing.
195 reactions across 18 posts, in 6 distinct kinds. The most used accounts for 48.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 94 | 48.2% | |
| 👍 | 50 | 25.6% | |
| 🔥 | 42 | 21.5% | |
| 👏 | 7 | 3.59% | |
| 👌 | 1 | 0.513% | |
| 😱 | 1 | 0.513% |
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 18 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 195 reactions 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 22 June 2026 to 4 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.
Great job working on your projects! Now it's time to learn from your peers. If you submitted your project for attempt 1, you will find your peer review assignments here: https://courses.datatalks.club/llm-zoomcamp-2026/project/project1/eval Have fun!
❤6👍6👌1🔥1😱1
Stream about the FAQ assistant Join now or watch later in recording: https://www.youtube.com/watch?v=CyH61xiYSnk
👍3
Today is the deadline for Attempt 1 of the final project. Submit your project by 1:00 AM CET today. As you continue working, check Module 7 for a complete project example that you can use as a reference. It covers: • Generating data, setting up the project, and building the initial RAG flow • Evaluating retrieval with ground truth data, Hit Rate, MRR, and boosting • Evaluating RAG with LLM-as-a-Judge and model com…
👍5❤1
In the course we use the FAQ dataset as the main running example In this article I describe how it's curated and how the FAQ assistant you see in Slack works https://alexeyondata.substack.com/p/rebuilding-a-faq-system-for-datatalksclub Also, if you'd rather watch me explain it than read the article, we'll have a live stream about it on YouTube https://luma.com/fb91wje9 See you soon! I hope you're having fun wit…
👏7👍2
We're talking about using tracking to get insights user tracing using Snowplow and Vercel AI https://www.youtube.com/watch?v=A2nMgZWyza8 Watch now or later in recording
❤5
We are hosting a live workshop on tracking and personalizing AI agents. 📅 Tuesday, July 21 🕟 16:30 CEST Agent applications usually use the current conversation as context. But users also interact with the rest of the product. They open pages, change filters, compare options, and complete different steps before asking the agent a question. In this workshop, we will show how to capture that behavioral data and pass …
👍7
It's time to start working on your final project. Check out Module 7 to see an example of a complete project as a reference. It covers: • Intro: Generating data, setting up the project, initial RAG flow • Evaluating Retrieval: Ground truth data, Hit Rate, MRR, boosting • Evaluating RAG: LLM-as-a-Judge, comparing models • Interface and Ingestion: Flask API, ingestion pipeline, project structure • Monitoring and Con…
👍11
We have prepared the homework for the dlt workshop as well as the monitoring module And they are both about monitoring and observability, and in both you will learn something new in addition to what we covered in the course: - for the monitoring homework we'll learn about OTel and instrument the RAG assistant with OTel collectors - for the dlt hub homework, we'll use Pydantic Logfire and see how we can use dlt to i…
❤13👍5
This week in LLM Zoomcamp: Module 5, Monitoring. We build: - A Streamlit chat app with RAG - Metric capture for LLM calls and cost - PostgreSQL storage for conversations - User feedback with thumbs up and thumbs down - Automatic relevance evaluation with a built-in judge - Streamlit and Grafana dashboards - A Docker Compose setup for running everything together You’ll see what your LLM application is doing after i…
👍5❤3
On Tuesday, July 21, we have a workshop on tracking and personalizing AI agents with Snowplow and the Vercel AI SDK. Your agent can see the chat history but not what the user is doing in the product. If you want the agent to respond based on the user's current session, capture that context and make it available to the model. This workshop shows how to do that with a Next.js travel chatbot. The approach: 1. Track …
🔥8❤1
We're starting in a few minites https://www.youtube.com/watch?v=rG2YW3YCq64 Join now or watch later in recording
We will have office hours today with Will from Kestra at 18:00 CET We will share the link here 5-10 minutes before the start If you want to get a reminder, you can use this link https://luma.com/d19r4ko1 You can ask questions in advance using this link https://app.sli.do/event/pH1w9PSq8dJZahTia24SKe
❤2
Showing the 12 most recent of 20 posts we hold for @llm_zoomcamp. 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
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 2 seed channels' Telegram-generated recommendation lists 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 19 September 2026 — this entry's latest reading, not the date you are reading this.
“LLM Zoomcamp” (@llm_zoomcamp), 4,701 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/llm_zoomcamp.
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.