Channel
@ramikrispinds
On this record: Topic · Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
4,591subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001634145994 |
|---|---|
| Type | Channel |
| Username | @ramikrispinds |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 14 September 2026 |
| Measurements held | 14 |
| Confirmed unchanged | 1 time, most recently 14 September 2026 |
| On Telegram | t.me/ramikrispinds |
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 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 |
|---|---|---|
| 14 Sept 2026, 21:19 | 4,591 | -5 |
| 11 Sept 2026, 06:56 | 4,596 | +3 |
| 6 Sept 2026, 12:41 | 4,593 | +3 |
| 2 Sept 2026, 10:04 | 4,590 | +6 |
| 30 Aug 2026, 09:25 | 4,584 | -4 |
| 27 Aug 2026, 07:04 | 4,588 | +2 |
| 24 Aug 2026, 05:35 | 4,586 | -5 |
| 20 Aug 2026, 15:23 | 4,591 | +4 |
| 17 Aug 2026, 16:06 | 4,587 | +1 |
| 14 Aug 2026, 03:15 | 4,586 | -2 |
| 10 Aug 2026, 21:01 | 4,588 | -2 |
| 7 Aug 2026, 23:55 | 4,590 | -1 |
| 7 Aug 2026, 17:47 | 4,591 | no change |
| 7 Aug 2026, 17:37 | 4,591 | first reading |
26 posts held, back to 14 July 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 6 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 26 posts for this entry, the most recent from 11 August 2026. An engagement rate over an empty window would be a number about nothing.
82 reactions across 25 posts, in 6 distinct kinds. The most used accounts for 62.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 51 | 62.2% | |
| ❤ | 16 | 19.5% | |
| 🔥 | 11 | 13.4% | |
| 👏 | 2 | 2.44% | |
| 🎉 | 1 | 1.22% | |
| 😁 | 1 | 1.22% |
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 82 reactions 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.
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
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
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
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
👍3❤1
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
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
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
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
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
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
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
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.
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 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.
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 14 September 2026 — this entry's latest reading, not the date you are reading this.
“Rami Krispin's Data Science Channel” (@ramikrispinds), 4,591 subscribers as measured 14 September 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.