Channel
@ai_mirosphere
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
24subscribers
-1 since we began measuring on 12 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002120306394 |
|---|---|
| Type | Channel |
| Username | @ai_mirosphere |
| Created | Between 1 November 2023 and 31 May 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 12 August 2026 |
| Last confirmed live | 7 October 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 6 times, most recently 7 October 2026 |
| On Telegram | t.me/ai_mirosphere |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 19 Aug 2026, 22:48 | 24 | -1 |
| 13 Aug 2026, 01:24 | 25 | no change |
| 12 Aug 2026, 10:32 | 25 | first reading |
20 posts held, back to 17 December 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 20 posts for this entry, the most recent from 5 January 2025. An engagement rate over an empty window would be a number about nothing.
18 reactions across 15 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 18 | 100.0% |
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 15 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 17 December 2024 to 5 January 2025, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Выпуск репозитория пакетов pkgsrc 2024Q4 Разработчики проекта NetBSD представили релиз репозитория пакетов pkgsrc-2024Q4, который стал 85 по счёту выпуском проекта. Pkgsrc поддерживает 23 платформы, среди которых AIX, FreeBSD, OpenBSD, DragonFlyBSD, HP-UX, Haiku, IRIX, Linux, QNX и UnixWare. Система создана в 1997 году на основе портов FreeBSD и в настоящее время используется по умолчанию для управления коллекцией п…
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Artificial Intelligence for Quantum Error Correction: A Comprehensive Review Quantum Error Correction (QEC) is the process of detecting and correcting errors in quantum systems, which are prone to decoherence and quantum noise. QEC is crucial for developing stable and highly accurate quantum computing systems, therefore, several research efforts have been made to develop the best QEC strategy. Recently, Google's b…
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Using AI for Efficient Statistical Inference of Lattice Correlators Across Mass Parameters Lattice QCD is notorious for its computational expense. Modern lattice simulations require large-scale computational resources to handle the large number of Dirac operator inversions used to construct correlation functions. Machine learning (ML) techniques that can increase, at the analysis level, the information inferred fr…
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Few-shot Metric Domain Adaptation: Practical Learning Strategies for an Automated Plant Disease Diagnosis Numerous studies have explored image-based automated systems for plant disease diagnosis, demonstrating impressive diagnostic capabilities. However, recent large-scale analyses have revealed a critical limitation: that the diagnostic capability suffers significantly when validated on images captured in environ…
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Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers Machine learning systems increasingly drive innovation across scientific fields and industry, yet challenges in compute overhead, specifically during inference, limit their scalability and sustainability. Responsible AI guardrails, essential for ensuring fairness, transparency, and privacy, further exacerbate these computatio…
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Multimodal Learning with Uncertainty Quantification based on Discounted Belief Fusion Multimodal AI models are increasingly used in fields like healthcare, finance, and autonomous driving, where information is drawn from multiple sources or modalities such as images, texts, audios, videos. However, effectively managing uncertainty - arising from noise, insufficient evidence, or conflicts between modalities - is cr…
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Выпуск дистрибутива 4MLinux 47.0 Доступен выпуск 4MLinux 47.0, минималистичного пользовательского дистрибутива, не являющегося ответвлением от других проектов и использующего графическое окружение на базе оконного менеджера JWM. 4MLinux может использоваться как в роли Live-окружения для воспроизведения мультимедийных файлов и решения пользовательских задач, так и в качестве системы для восстановления после сбоев и п…
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AI-Based Teat Shape and Skin Condition Prediction for Dairy Management Dairy owners spend significant effort to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce these costs, yet obstacles arise when adapting advanced tools to farming environments. In this work, we adapt AI tools to dairy cow teat localization, teat shape,…
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How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts The compute requirements associated with training Artificial Intelligence (AI) models have increased exponentially over time. Optimisation strategies aim to reduce the energy consumption and environmental impacts associated with AI, possibly shifting impacts from the use phase to the manufacturing phase in the life-cycle of hardware. …
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Emerging Security Challenges of Large Language Models Large language models (LLMs) have achieved record adoption in a short period of time across many different sectors including high importance areas such as education [4] and healthcare [23]. LLMs are open-ended models trained on diverse data without being tailored for specific downstream tasks, enabling broad applicability across various domains. They are commonly…
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A Systems Thinking Approach to Algorithmic Fairness Systems thinking provides us with a way to model the algorithmic fairness problem by allowing us to encode prior knowledge and assumptions about where we believe bias might exist in the data generating process. We can then model this using a series of causal graphs, enabling us to link AI/ML systems to politics and the law. By treating the fairness problem as a com…
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A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for these algorithms such as modeling of diseases. The majority of these applications employ supervised rather than unsupervised ML algorithms. In addition, each year,…
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Showing the 12 most recent of 20 posts we hold for @ai_mirosphere. 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.
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.
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 19 August 2026 — this entry's latest reading, not the date you are reading this.
“ИИ - Исключительно Интересно AI & ML” (@ai_mirosphere), 24 subscribers as measured 19 August 2026. Telegram Register, tgregister.com/channel/ai_mirosphere.
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.