Telegram RegisterThe public register of Telegram

Channel

AI Technology | Claude & ChatGPT Prompts

@aijobss

Not marked by Telegram, checked 10 August 2026

On this record: Growth · Engagement · Reactions · Posts · Telegram's recommendations · Cite this entry

13,375subscribers

+61 since we began measuring on 9 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1002103649948
TypeChannel
Username@aijobss
CreatedBetween 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded9 August 2026
Last confirmed live15 August 2026
Measurements held8
Confirmed unchanged1 time, most recently 15 August 2026
On Telegramt.me/aijobss

Growth

13,31413,37513,344.59 August 2026 — 13,314 subscribers9 August 2026 — 13,314 subscribers9 August 2026 — 13,323 subscribers11 August 2026 — 13,325 subscribers12 August 2026 — 13,337 subscribers13 August 2026 — 13,347 subscribers14 August 2026 — 13,359 subscribers15 August 2026 — 13,375 subscribers9 August 202615 August 2026
8 measurements spanning 7 days, net +61. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 13,305–13,384 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
15 Aug 2026, 20:4813,375+16
14 Aug 2026, 13:0613,359+12
13 Aug 2026, 05:2313,347+10
12 Aug 2026, 02:4713,337+12
11 Aug 2026, 01:5713,325+2
9 Aug 2026, 23:4313,323+9
9 Aug 2026, 02:5113,314no change
9 Aug 2026, 01:3013,314first reading

Engagement

21 posts held, back to 17 February 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 13 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
18.5%
avg views ÷ 13,375 subscribers
Avg views / post
2,470
5 posts measured
Reaction rate
0.332%
reactions ÷ views · ER floor
Posts in window
6
of 21 held

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.

What these figures were computed from
WindowRolling 30 days · latest post in window 10 August 2026
Posts held21 (17 February 202610 August 2026)
Views total12,340
Reactions total41
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken15 Aug 2026, 11:39 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.

Reaction mix

204 reactions across 20 posts, in 7 distinct kinds. The most used accounts for 89.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
18389.7%
🔥73.43%
👌41.96%
👏41.96%
👎31.47%
👍20.98%
🥰10.49%

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 20 of the 21 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 204reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 21 most recent posts we hold, published 17 February 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.

Recent posts

10 Aug 2026, 06:52 UTC≈1,020 views4 reactionsread 15 August 2026

5 FREE AI Courses You Should Know About 1️⃣ Introduction to AI for Work — DataCamp New to AI? This beginner-friendly course explains AI, machine learning, Generative AI, and LLMs with a focus on practical workplace use. No technical background required! 👉 Best for: Students, managers, marketers & beginners 🔗 Click Here: https://www.datacamp.com/courses/introduction-to-ai-for-work 2️⃣ Easy-Vibe AI Coding Guide fro

4

4 Aug 2026, 11:03 UTC≈1,850 views8 reactionsread 15 August 2026

🔥 10 YouTube Channels Keeping You Ahead in AI 1️⃣ Two Minute Papers Complex AI research explained in simple, visual, and exciting videos. Perfect for discovering the latest breakthroughs. 👉 Click Here: Two Minute Papers 2️⃣ Yannic Kilcher Want to understand AI papers in depth? Yannic breaks down models, mathematics, architectures, and research methodology. 👉 Click Here: Yannic Kilcher 3️⃣ AI Jason Learn how to b

7👏1

28 Jul 2026, 05:19 UTCviews —

AI Technology | Claude & ChatGPT Prompts pinned «🚀 5 FREE Resources to Master Agentic AI 📘 Microsoft AI Agents for Beginners Learn AI agents, RAG, MCP, memory, and multi-agent systems with hands-on Python examples. 👉 Click Here: https://github.com/microsoft/AI-For-Beginners/tree/main/12-building-ai-agents…»

28 Jul 2026, 05:18 UTC≈2,660 views6 reactionsread 15 August 2026

5 Free Courses to Go From AI Beginner to Practitioner 1️⃣ Harvard CS50: Introduction to AI with Python 🎓 Learn the fundamentals of AI before diving into machine learning. Build AI projects like Tic-Tac-Toe, search algorithms, and logic solvers while mastering core AI concepts. 👉 Click Here: https://cs50.harvard.edu/ai/ 2️⃣ Google Machine Learning Crash Course 📊 Google's official ML course teaches gradient descent,

6

21 Jul 2026, 07:47 UTC≈3,270 views12 reactionsread 15 August 2026

🚀 5 FREE Resources to Master Agentic AI 📘 Microsoft AI Agents for Beginners Learn AI agents, RAG, MCP, memory, and multi-agent systems with hands-on Python examples. 👉 Click Here: https://github.com/microsoft/AI-For-Beginners/tree/main/12-building-ai-agents 🤗 Hugging Face AI Agents Course Build real-world AI agents using LangGraph, LlamaIndex, and other popular frameworks. 👉 Click Here: https://huggingface.co/learn

8🔥4

17 Jul 2026, 06:27 UTC≈3,540 views11 reactionsread 15 August 2026

Best YouTube Channels to learn AI in 2026 1. AI Explained 👉 http://youtube.com/@aiexplained-o… 2. Andrej Karpathy 👉 https://www.youtube.com/@AndrejKarpathy 3. Cole Medin 👉 http://youtube.com/@WesRoth/ 4. DeepLearningAI 👉 http://youtube.com/@Deeplearninga… 5. Futurepedia 👉 http://youtube.com/@futurepedia_i… 6. Matthew Berman 👉 http://youtube.com/@matthew_berma… 7. Skill Leap AI 👉 http://youtube.com/@SkillLeapAI

11

1 Jul 2026, 13:20 UTC≈5,020 views10 reactionsread 15 August 2026

🚀 7 Real-World Python Projects You Can Build in 2026 1. AI Scam & Notice Checker Detect scam SMS, phishing messages, and fake notices with AI. 📖 https://huggingface.co 2. Multi-Agent Research Assistant Build AI agents that research the web and generate reports. 📖 https://machinelearningmastery.com 3. Breast Cancer Prediction API Train an ML model and deploy it with FastAPI. 📖 https://machinelearningmastery.com

9👍1

18 Jun 2026, 03:16 UTC≈5,250 views9 reactionsread 15 August 2026

7 Best Small Language Models Under 10B Parameters in 2026 1. IBM Granite 4.1 8B With industry-leading coding performance and a massive context window, Granite 4.1 8B is built for enterprise applications, RAG systems, and tool-calling workflows. 🔗 Click Here: https://huggingface.co/ibm-granite/granite-4.1-8b-instruct 2. Qwen3.5-9B One of the strongest reasoning models under 10B parameters, Qwen3.5-9B excels in mu

6🔥2👍1

9 Jun 2026, 03:58 UTC≈5,120 views6 reactionsread 15 August 2026

5 Fun Papers That Explain LLMs Clearly 1️⃣ Attention Is All You Need 📝 Description: Introduced the Transformer, the architecture behind every modern LLM. Replaced older recurrent/convolutional models for sequences. 🔑 Key Ideas: Self-attention • Multi-head attention • Positional encoding • Transformer block 🔗 Paper: https://arxiv.org/abs/1706.03762 ━━━━━━━━━━━━━━━ 2️⃣ Language Models Are Few-Shot Learners 📝 Descrip

6

9 Jun 2026, 03:52 UTC≈3,820 views3 reactionsread 15 August 2026

5 Must-Know Python Concepts for AI Engineers 1. 🔥 Tensors & Autograd Stop writing backprop by hand. requires_grad=True tracks every operation → .backward() applies the chain rule automatically. import torch x = torch.tensor(2.0) y = torch.tensor(5.0) w = torch.tensor(0.5, requires_grad=True) b = torch.tensor(0.1, requires_grad=True) pred = w * x + b loss = (pred - y) ** 2 loss.backward() print(w.grad.item(), b.

3

1 Jun 2026, 04:29 UTC≈4,320 views10 reactionsread 15 August 2026

7 Real World AI Projects to Build in 2026 🤖 Build an AI Job Search Assistant Searching for jobs is repetitive — JobFit AI reads your CV, searches live postings, and generates a ranked job-fit report automatically. 📖 Guide: Kimi K2.6 API Tutorial 🐙 GitHub: kingabzpro/JobFit-AI 🔬 Build a Multi-Agent Research Assistant Most research workflows involve several steps — this multi-agent system handles web search, sourc

10

30 Apr 2026, 04:00 UTC≈5,990 views11 reactionsread 15 August 2026

10 Python Libraries for Building LLM Applications 🔹 1. Transformers Core library for loading, fine-tuning, and running LLMs with ease. 👉 Learn more: https://huggingface.co/docs/transformers 🔹 2. LangChain Connect prompts, tools, APIs, and models into powerful workflows. 👉 Learn more: https://docs.langchain.com 🔹 3. LlamaIndex Bring your own data into LLMs for smarter, grounded responses (RAG). 👉 Learn more: https:

11

Showing the 12 most recent of 21 posts we hold for @aijobss. 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.

Appears in Telegram’s recommendations for other channels

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.

AI Tools
@Best_AI_tools · 170,314
Telegram ranks this channel #18 of 66 here — alongside 65 others — read 13 August 2026
Hi, AI • Tech News
@hiaimediaen · 568,239
Telegram ranks this channel #25 of 83 here — alongside 82 others — read 9 August 2026
Data Analytics
@sqlspecialist · 110,748
Telegram ranks this channel #44 of 83 here — alongside 82 others — read 14 August 2026
Jobs | Internships | Placement | Interviews
@getjobss · 113,104
Telegram ranks this channel #47 of 83 here — alongside 82 others — read 14 August 2026

This channel appears in 4 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

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 15 August 2026 — this entry's latest reading, not the date you are reading this.

“AI Technology | Claude & ChatGPT Prompts” (@aijobss), 13,375 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/aijobss.

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.