Channel
@epythonlab
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
6,167subscribers
-92 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001173821469 |
|---|---|
| Type | Channel |
| Username | @epythonlab |
| Created | Between 1 March 2018 and 31 July 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 14 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/epythonlab |
Technology — 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 12 September 2026 and assigned it the closest of 31 fixed categories, at 87% 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 |
|---|---|---|
| 18 Sept 2026, 19:16 | 6,167 | -6 |
| 14 Sept 2026, 05:15 | 6,173 | -8 |
| 10 Sept 2026, 10:59 | 6,181 | -12 |
| 5 Sept 2026, 05:00 | 6,193 | -11 |
| 1 Sept 2026, 06:37 | 6,204 | -11 |
| 29 Aug 2026, 00:55 | 6,215 | -1 |
| 25 Aug 2026, 23:17 | 6,216 | -15 |
| 22 Aug 2026, 21:54 | 6,231 | -4 |
| 19 Aug 2026, 03:57 | 6,235 | -10 |
| 16 Aug 2026, 05:36 | 6,245 | -10 |
| 12 Aug 2026, 19:54 | 6,255 | -1 |
| 9 Aug 2026, 22:21 | 6,256 | -3 |
| 6 Aug 2026, 21:15 | 6,259 | no change |
| 6 Aug 2026, 21:09 | 6,259 | first reading |
22 posts held, back to 17 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 9 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 22 posts for this entry, the most recent from 10 August 2026. An engagement rate over an empty window would be a number about nothing.
66 reactions across 19 posts, in 2 distinct kinds. The most used accounts for 97.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 64 | 97.0% | |
| ❤ | 2 | 3.03% |
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 19 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 66 reactions 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 17 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.
⇒ 𝐌𝐨𝐬𝐭 𝐨𝐟 𝐮𝐬 𝐭𝐡𝐢𝐧𝐤 𝐀𝐈 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐚𝐧 𝐋𝐋𝐌 + 𝐚 𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐫𝐨𝐦𝐩𝐭. Actually, that approach may work for a demo, but production support needs much more. When a customer asks to check an order, change a reservation, or request a refund, the system needs to manage 𝙨𝙩𝙖𝙩𝙚, 𝙩𝙤𝙤𝙡𝙨, 𝙥𝙚𝙧𝙢𝙞𝙨𝙨𝙞𝙤𝙣𝙨, 𝙫𝙖𝙡𝙞𝙙𝙖𝙩𝙞𝙤𝙣, 𝙖𝙣𝙙 𝙚𝙭𝙚𝙘𝙪𝙩𝙞𝙤𝙣. A solid architecture looks like this: ✅ 𝙆𝙚𝙚𝙥 𝙨𝙩𝙖𝙩𝙚 𝙤𝙪𝙩𝙨𝙞𝙙𝙚 𝙩𝙝𝙚 𝙇𝙇𝙈: your application should ma…
👍3
Learn an AI Agent with LangChain & Gemini | Python + LangGraph | Autonomous Customer Support https://www.youtube.com/watch?v=AgconCK-l4g
👍2
🚀 Everyone is building AI wrappers. Very few developers are building AI systems. 🤔 There's a big difference. A production-ready AI agent is much more than an LLM. 🤖 It requires: ✅ A decision loop 🔄 ✅ Tool integration 🛠️ ✅ Intent recognition 🎯 ✅ Error handling and recovery 🛡️ ✅ Context and state management 🧠 ✅ Clear separation between reasoning and execution ⚖️ ✅ An extensible architecture 🏗️ The LLM is just one…
Create your first ai agent using Python and ollama https://youtu.be/tkA6vCPihuE
👍4
🚀 Stop shipping broken ML code. A Machine Learning project shouldn’t end as a collection of messy Jupyter Notebooks, global package conflicts, and code that only works on your laptop. If you want to build scalable, maintainable, and production-ready ML systems, the project structure matters. Here’s a practical framework for setting up an ML project properly: 🛡️ 1. Isolate your dependencies Avoid installing packa…
🚀 Your Python Learning Roadmap 🐍 Thinking of learning to code? Start with Python — simple, powerful, and in high demand. Here’s a quick path to follow: 1. 📚 Learn the Basics: Variables, Loops, Functions 2. 🧠 Master Data Structures: Lists, Dicts, Strings 3. 🧱 Understand OOP: Classes, Inheritance 4. 💻 Build Mini Projects & push to GitHub 5. 🧰 Use Libraries: math, pandas, matplotlib 6. 🧩 Solve Problems: Le…
👍5
A Practical Python Roadmap to Become an AI Developer Here is the start of your journey: https://youtu.be/ldR3NdSDiyE #Python #PythonDeveloper #LearnPython #ArtificialIntelligence #AI #AIDeveloper #MachineLearning #DeepLearning #GenerativeAI #LLM #DataScience #MLOps #FastAPI #PyTorch #ScikitLearn #SoftwareEngineering #Programming #Coding #TechCareer #BuildInPublic #OpenSource #100DaysOfCode #Developer #TechEducation …
🤖 AI Is Fighting AI Generative AI has fundamentally changed the fraud landscape. Not long ago, creating a convincing fake identity required specialized skills and significant effort. Today, powerful AI tools have made it possible for almost anyone to generate: ✔ Fake identity documents ✔ Realistic AI-generated faces ✔ Deepfake videos ✔ Human-like voice clones As the barrier to entry drops, fraudsters can launch m…
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Traditional Credit Scores Are Losing Their Edge in the Generative AI Era The fraud landscape has changed. For years, financial institutions relied on credit scores, declared income, and identity documents to make lending and fraud decisions. Those signals worked when identities were difficult to fake. Today, Generative AI has changed the rules. Fraudsters can now create convincing synthetic identities, generate f…
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A 750 credit score is no longer proof of a trustworthy applicant. In the age of Generative AI, it might just be a perfectly engineered fraud. When we analyze credit scoring and risk underwriting, traditional financial checks are officially failing to catch modern scammers. Here is why: ✅ The Credit Score Illusion: On paper, fraudsters and legitimate users look identical. Because scammers use pristine synthetic iden…
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Scammers look identical to good users https://youtu.be/kgNgKtmAlR0
👍4
🚨 Traditional Fraud Detection Is No Longer Enough A few years ago, checking a customer's credit score, ID, and income was often enough to detect fraud. Today, fraudsters are using: ✔ AI-generated identities ✔ Deepfake videos ✔ Voice cloning ✔ Synthetic documents Many legacy fraud detection systems were never designed for this new reality. The next generation of fraud detection combines multiple machine learning s…
👍4
Showing the 12 most recent of 22 posts we hold for @epythonlab. 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.
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 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 18 September 2026 — this entry's latest reading, not the date you are reading this.
“Epython Lab” (@epythonlab), 6,167 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/epythonlab.
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.