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Machine Learning lab

@machine_learning_lab_k

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Polls · Telegram's recommendations · Cite this entry

935subscribers

+0 since we began measuring on 9 September 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1001243078576
TypeChannel
Username@machine_learning_lab_k
DescriptionWelcome to Machine Learning Lab! Explore machine learning and data science with discussions, tutorials, and resources. Discover insights in ML approaches, Projects and practical applications. Admin: @kian_bd
CreatedBetween 1 March 2018 and 31 August 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded9 September 2026
Last confirmed live9 September 2026
Measurements held2
On Telegramt.me/machine_learning_lab_k

Growth

9359 Sept 2026, 10:47 — 935 subscribers9 Sept 2026, 11:22 — 935 subscribers9 Sept 2026, 10:479 Sept 2026, 11:22
2 measurements taken within a single day. 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 934–936 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
9 Sept 2026, 11:22935no change
9 Sept 2026, 10:47935first reading

Engagement

20 posts held, back to 26 November 2025the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page 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 16 July 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Photos
31
Videos
21
Links
175

Lifetime counters from Telegram’s own channel header, read 9 September 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.

Reaction mix

18 reactions across 10 posts, in 3 distinct kinds. The most used accounts for 38.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍738.9%
633.3%
🔥527.8%

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 10 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 18 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 26 November 2025 to 16 July 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

16 Jul 2026, 02:40 UTC209 viewsread 9 September 2026

NOTICING THE WATCHER: LLM AGENTS CAN INFER COT MONITORING FROM BLOCKING FEEDBACK Chain-of-thought (CoT) monitoring is proposed as a method for overseeing the internal reasoning of language-model agents. Prior work has shown that when models are explicitly informed that their reasoning is being monitored, or are finetuned to internalize this fact, they may learn to obfuscate their CoTs in ways that allow them to evad

Signed Kian

29 Apr 2026, 02:07 UTCviews —

Machine Learning lab pinned «Quick check-in! Which AI are you talking to the most lately?»

29 Apr 2026, 02:04 UTC431 viewsread 9 September 2026
Poll

Quick check-in! Which AI are you talking to the most lately?

  1. ChatGPT38%
  2. Gemini29%
  3. Claude42%
  4. Grok4%
  5. Other2%

The shares total 115%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.

Signed Kian

27 Apr 2026, 03:12 UTC434 views1 reactionsread 9 September 2026

CME 296 - Diffusion & Large Vision Models This course explores diffusion-based generative models for vision. You will study the foundations of diffusion, score matching and flow matching, modern architectures such as Diffusion Transformers, and methods for controllable image generation and evaluation. Link: https://www.youtube.com/playlist?list=PLoROMvodv4rNdy8rt2rZ4T2xM0OjADnfu @Machin_learning_lab_K

1

Signed Kian

27 Apr 2026, 03:09 UTC386 viewsread 9 September 2026

CME 295: Transformers & Large Language Models This course explores the world of Transformers and Large Language Models (LLMs). You will learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications. Link: https://www.youtube.com/playlist?list=PLoROMvodv4rOCXd21gf0CF4xr35yINeOy

Signed Kian

18 Apr 2026, 05:20 UTC362 viewsread 9 September 2026

Ollama version 0.21 includes supports Hermes Agent, the self-improving AI agent built by Nous Research. Link: https://docs.ollama.com/integrations/hermes @Machin_learning_lab_K

Signed Kian

18 Apr 2026, 05:15 UTC311 viewsread 9 September 2026

Hermes Agent The self-improving AI agent built by Nous Research. It's the only agent with a built-in learning loop — it creates skills from experience, improves them during use, nudges itself to persist knowledge, searches its own past conversations, and builds a deepening model of who you are across sessions. Run it on a $5 VPS, a GPU cluster, or serverless infrastructure that costs nearly nothing when idle. It's n

Signed Kian

18 Apr 2026, 04:02 UTC277 viewsread 9 September 2026

Building with the Claude API This course provides comprehensive coverage of the Claude API, from basic usage through advanced agent architectures. You'll learn to integrate Claude into applications, implement tool calling, build RAG pipelines, and design both deterministic workflows and flexible agent systems. Link: https://anthropic.skilljar.com/claude-with-the-anthropic-api @Machin_learning_lab_K

Signed Kian

18 Apr 2026, 04:00 UTC254 viewsread 9 September 2026

Claude Code in Action This course covers Claude Code, a command-line AI assistant that uses language models to perform development tasks. You'll learn how Claude Code reads files, executes commands, and modifies code through its tool system, along with techniques for managing context, creating custom workflows, extending Claude Code with hooks, and integrating with external services. Link: https://anthropic.skillja

Signed Kian

17 Apr 2026, 01:43 UTC268 views2 reactionsread 9 September 2026

If you're looking to learn AI from a top institution, Harvard University offers a great collection of Artificial Intelligence courses—many of them free. Explore topics like machine learning, data science, and AI fundamentals, all in one place: https://pll.harvard.edu/subject/artificial-intelligence Good resource for both beginners and experienced developers looking to strengthen their AI skills. @Machin_learning_

2

Signed Kian

15 Dec 2025, 02:48 UTC550 views2 reactionsread 9 September 2026

Build your first CrewAI Agent Dive in and start creating your own agentic workflows: https://docs.crewai.com/en/quickstart#build-your-first-crewai-agent @Machin_learning_lab_K

👍2

Signed Kian

Showing the 12 most recent of 20 posts we hold for @machine_learning_lab_k. 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.

Polls

The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.

29 Apr 2026, 02:04 UTCAnonymous Poll52 voters

Quick check-in! Which AI are you talking to the most lately?

  1. ChatGPT38%
  2. Gemini29%
  3. Claude42%
  4. Grok4%
  5. Other2%

The shares total 115%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.

Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.

The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.

Read from the 20 most recent posts we hold, published 26 November 2025 to 16 July 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.

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.

Faculty Plus
@facultypluss · 28,696
Telegram ranks this channel #45 of 75 here — alongside 74 others — read 9 September 2026

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.

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 9 September 2026 — this entry's latest reading, not the date you are reading this.

“Machine Learning lab” (@machine_learning_lab_k), 935 subscribers as measured 9 September 2026. Telegram Register, tgregister.com/channel/machine_learning_lab_k.

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.