Channel
@ml4se
On this record: Growth · Engagement · Reactions · Posts · Polls · Citations · Cite this entry
495subscribers
-3 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001222764814 |
|---|---|
| Type | Channel |
| Username | @ml4se |
| Created | Between 1 March 2018 and 31 July 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 8 September 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 8 September 2026 |
| On Telegram | t.me/ml4se |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Sept 2026, 06:55 | 495 | -1 |
| 16 Aug 2026, 08:45 | 496 | -2 |
| 8 Aug 2026, 04:18 | 498 | no change |
| 7 Aug 2026, 10:30 | 498 | first reading |
19 posts held, back to 22 October 2024 — the 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 19 posts for this entry, the most recent from 26 March 2026. An engagement rate over an empty window would be a number about nothing.
34 reactions across 8 posts, in 7 distinct kinds. The most used accounts for 55.9% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 19 | 55.9% | |
| 🔥 | 8 | 23.5% | |
| 🤯 | 3 | 8.82% | |
| ❤ | 1 | 2.94% | |
| 👏 | 1 | 2.94% | |
| 😢 | 1 | 2.94% | |
| 🤔 | 1 | 2.94% |
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 19 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 34 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 19 most recent posts we hold, published 22 October 2024 to 26 March 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.
TurboQuant: Redefining AI efficiency with extreme compression Google Research presents TurboQuant—a suite of theoretically grounded quantization algorithms that tackle the biggest memory bottlenecks in AI. By combining PolarQuant (which uses polar coordinates to eliminate memory overhead) and QJL (a 1‑bit error‑correction trick), TurboQuant compresses the key‑value cache to just 3 bits per value with zero accuracy …
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WebMCP – Proposed Specification WebMCP allows web applications to expose JavaScript functions as tools that AI agents can discover and invoke. - Sites register tools via navigator.modelContext.registerTool() with name, description, JSON schema, and execute callback. - Agents call tools to perform actions; tools can request user confirmation. - Currently a draft by the W3C Community Group, not a formal web standard.
Can LLMs Be Computers? Researchers at Percepta built a computer inside a transformer by compiling C code to WebAssembly and having the model generate the execution trace step by step. The key breakthrough: using 2D attention heads to turn linear KV cache scans into logarithmic-time geometric lookups. The result: The model runs the Hungarian algorithm on a 10×10 matrix, solves the world’s hardest Sudoku, and streams…
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Towards a Science of AI Agent Reliability AI agents are getting smarter, but are they getting more reliable? Not really. The paper shows a worrying gap: while accuracy on benchmarks keeps rising, reliability lags far behind. The authors propose a safety‑critical engineering lens for agents, breaking reliability into four dimensions: - Consistency – do they give the same result every time? - Robustness – can they h…
System 3: Collective Intelligence in the Multiplayer AI Era The author expands on Daniel Kahneman's two systems of thought (fast System 1 and slow System 2), applying them not to individuals, but to organizations. With the rise of "multiplayer AI," a System 3 is emerging—a new mode of collective cognition where synthesis happens not inside a single mind, but within the network of interactions between people and AI a…
Robots need your body AI agents rent humans to perform tasks in the physical world. Users register, list their skills and rates, and receive assignments from AIs—ranging from errands and shopping to equipment testing.
The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models The results demonstrate that the Assistant persona in LLMs corresponds to a specific linear direction—the "Assistant Axis"—within activation space. This axis is inherited from base models and encodes Assistant-like properties. The model's position on this axis is fragile: it can be perturbed by intentional prompts or through organic…
AI is making us work more The article highlights the paradox that AI tools, designed to increase efficiency, are instead fueling a culture of overwork. With systems available 24/7, a psychological pressure emerges where any moment not spent being "productive" feels like falling behind. This mirrors historical shifts, like artificial lighting, which turned the ability to work longer into an obligation. Personally, I…
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Open-source has continued to trail frontier, closed-source models in performance by nine to 12 months Open-source models offer clear enterprise advantages: greater customization, potential cost savings, and the ability to deploy within private cloud or on-premises environments. But despite these benefits and recent improvements, open-source has continued to trail frontier, closed-source models in performance by nine…
At what maturity level is the use of AI in SE in your company?
Shares as published, totalling 101%. No per-option vote count is published by Telegram, so none is shown.
Looks promising. We'll see how it goes https://nof1.ai/
Subliminal Learning: Language models transmit behavioral traits via hidden signals in data The paper investigates _subliminal learning_, a phenomenon where language models transmit behavioral traits (e.g., animal preferences or misalignment) through generated data that is semantically unrelated to those traits. Experiments show that training a student model on a teacher's number sequences, code, or reasoning trace…
🔥5🤯3👍1🤔1
Showing the 12 most recent of 19 posts we hold for @ml4se. 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.
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.
At what maturity level is the use of AI in SE in your company?
Shares as published, totalling 101%. No per-option vote count is published by Telegram, so none is 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 19 most recent posts we hold, published 22 October 2024 to 26 March 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.
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.
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 8 September 2026 — this entry's latest reading, not the date you are reading this.
“ml4se” (@ml4se), 495 subscribers as measured 8 September 2026. Telegram Register, tgregister.com/channel/ml4se.
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.