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Channel

Rami Krispin's Data Science Channel

@ramikrispinds

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

4,588subscribers

-3 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001634145994
TypeChannel
Username@ramikrispinds
CreatedBetween 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live27 August 2026
Measurements held9
Confirmed unchanged1 time, most recently 27 August 2026
On Telegramt.me/ramikrispinds

Growth

4,5864,5914,588.57 August 2026 — 4,591 subscribers7 August 2026 — 4,591 subscribers7 August 2026 — 4,590 subscribers10 August 2026 — 4,588 subscribers14 August 2026 — 4,586 subscribers17 August 2026 — 4,587 subscribers20 August 2026 — 4,591 subscribers24 August 2026 — 4,586 subscribers27 August 2026 — 4,588 subscribers4,5887 August 202627 August 2026
9 measurements spanning 20 days, net -3. 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 4,585–4,592 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
27 Aug 2026, 07:044,588+2
24 Aug 2026, 05:354,586-5
20 Aug 2026, 15:234,591+4
17 Aug 2026, 16:064,587+1
14 Aug 2026, 03:154,586-2
10 Aug 2026, 21:014,588-2
7 Aug 2026, 23:554,590-1
7 Aug 2026, 17:474,591no change
7 Aug 2026, 17:374,591first reading

Engagement

26 posts held, back to 14 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 6 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
6.55%
avg views ÷ 4,588 subscribers
Avg views / post
301
10 posts measured
Reaction rate
1.10%
reactions ÷ views · ER floor
Posts in window
10
of 26 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. It is computed over the 9 of 10 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 11 August 2026
Posts held26 (14 July 202611 August 2026)
Views total3,005
Reactions total29
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 06:23 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

82 reactions across 25 posts, in 6 distinct kinds. The most used accounts for 62.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍5162.2%
1619.5%
🔥1113.4%
👏22.44%
🎉11.22%
😁11.22%

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 25 of the 26 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 82reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 26 most recent posts we hold, published 14 July 2026 to 11 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

11 Aug 2026, 15:37 UTC135 views1 reactionsread 12 August 2026

Meta Muse Glimmer 🚀 Meta released a 30B open-weight agentic model under Apache 2.0 for local workflows on consumer hardware. It supports tool use, long-horizon reasoning, failure recovery, text+image input, adjustable reasoning effort, and 100+ languages. A roughly 4-bit version fits under 20 GB. More details: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model

👍1

11 Aug 2026, 15:31 UTC136 views2 reactionsread 12 August 2026

Hermes Desktop Workflows 🚀 This video from Tonbi's AI Garage walks through recent Hermes desktop workflows. The 16-minute video covers: ✅ Remote machines over SSH ✅ Multi-session panes and tabs ✅ Drag-in session context ✅ Custom widgets with the Plugin SDK ✅ The Kanban plugin ✅ Agent delegation across researcher, writer, and verifier profiles 📽️: https://www.youtube.com/watch?v=bifDX18uyUk

👍2

11 Aug 2026, 15:23 UTC120 views4 reactionsread 12 August 2026
Photo

I feature a data science book every week in my newsletter, and last week's pick focuses on Transformers: The Definitive Guide - Applications Beyond NLP by Nicole Königstein. The book starts with attention, embeddings, and transformer architecture, then shows how the same ideas extend across different data types and applications. Topics include: ✅ Time-series forecasting and anomaly detection ✅ Computer vision and im

2👍2

10 Aug 2026, 13:44 UTC241 views4 reactionsread 12 August 2026

Claude Code Full Course 🚀 This course from freeCodeCamp, developed by EricWTech, walks through Claude Code setup and day-to-day workflows. The 82-minute course covers: ✅ VS Code setup ✅ Permission modes ✅ Plan mode and autonomous goals ✅ Claude skills ✅ Context and token usage ✅ Slash commands ✅ GitHub version control ✅ MCP tools and app deployment 📽️: https://www.youtube.com/watch?v=7l6bXLAKyEI

👍31

9 Aug 2026, 16:01 UTC292 views5 reactionsread 12 August 2026

I am starting a new series of tutorials focusing on Docker 🐳 for ML/AI Ops 👇🏼 I recently released a new LinkedIn Learning course focused on Docker for AI/ML developers. While creating this course, I spent a lot of time preparing learning materials, and I decided to turn those materials into a sequence of tutorials. Here is what this Docker series is going to cover: 🔹What is Docker and when should you use it 🔹 Conta

👍5

8 Aug 2026, 13:19 UTC328 views4 reactionsread 12 August 2026

Issue 100 is out! 🔹 Open Source of the Week - Prime Agent 🔹 New learning resources 🔹 Book of the week - Generative AI at AWS https://ramikrispin.substack.com/p/prime-agent-generative-ai-at-aws

🔥3🎉1

4 Aug 2026, 16:53 UTC526 views5 reactionsread 12 August 2026
Photo

Stanford CS229 Machine Learning - Spring 2026 🚀 Stanford Online released a 17-lecture playlist from its graduate machine learning course. It provides a structured path from supervised learning foundations to modern generative models and reinforcement learning. The course covers: ✅ Supervised learning setup ✅ Weighted least squares ✅ Generalized linear models ✅ Gaussian discriminant analysis ✅ Dataset splits and ML

5

4 Aug 2026, 16:40 UTC402 views1 reactionsread 12 August 2026

Terraform Crash Course - Infrastructure as Code 🚀 This tutorial from NeuralNine provides a practical introduction to Terraform. It moves from installation and a minimal AWS example to combining AWS services, then shows smaller examples with GCP and Docker. 📽️: https://www.youtube.com/watch?v=pnzlqoYNuQc

👏1

4 Aug 2026, 16:40 UTC378 viewsread 12 August 2026

Why AI Agents Need a Context Layer 🚀 This talk from MotherDuck's Bev Turnbaugh explains why correct SQL is not enough when an agent lacks a company's business definitions. It covers semantic vs. context layers, RAG and rules files, MotherDuck guides, and an MCP walkthrough that surfaces relevant context alongside the data. 📽️: https://www.youtube.com/watch?v=hmjRc6KJ-hw

1 Aug 2026, 14:19 UTC447 views3 reactionsread 12 August 2026

Issue 99 is out! This week's agenda: 🔹 Open Source of the Week - Forge3D by Milos Popovic, PhD 🔹 New learning resources - Codex workflows, local model fine-tuning, context layers for AI agents, and Terraform 🔹 Book of the week - Transformers: The Definitive Guide by Nicole Königstein https://ramikrispin.substack.com/p/the-forge3d-project-transformers

🔥3

28 Jul 2026, 03:20 UTC603 views1 reactionsread 12 August 2026

Fine-Tune AI Models Locally with Unsloth Studio 🚀 This tutorial from Tech With Tim walks through fine-tuning an open model locally with Unsloth Studio. It covers fine-tuning basics, LoRA vs. QLoRA, model and dataset setup, training, comparing the result with the base model, and exporting the model. 📽️: https://www.youtube.com/watch?v=4JofSJIrjwU

1

26 Jul 2026, 22:36 UTC527 views8 reactionsread 12 August 2026

Setting Yourself Up for Success with Codex 🚀 This workshop from Jason Liu, an AI Engineer from OpenAI, walks through a practical Codex workflow. This 75-minute workshop covers personal memory vaults, long-running project threads, collaboration between threads, skills and plugins, computer use, scheduled automations, goals with verification, and choosing lower reasoning levels when the task does not require extra thi

👍6👏1😁1

Showing the 12 most recent of 26 posts we hold for @ramikrispinds. 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

Citation-graph rank — 535,876 of 1,622,561entries 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.

Forward network

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.

Mentions

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.

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

“Rami Krispin's Data Science Channel” (@ramikrispinds), 4,588 subscribers as measured 27 August 2026. Telegram Register, tgregister.com/channel/ramikrispinds.

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.