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Channel

Data Portal | DS & ML

@LLMScience

Not marked by Telegram, checked 24 September 2026

On this record: Topic · Observations · Growth · Engagement · What this channel posts · Stars · Advertising · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Telegram's recommendations · Cite this entry

11,300subscribers

+2,944 since we began measuring on 9 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1002177572398
TypeChannel
Username@LLMScience
DescriptionВсё самое интересное из мира Data Science и машинного обучения Cотрудничество: @devmangx № 8378747991
CreatedBetween 1 June 2024 and 30 September 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded9 August 2026
Last confirmed live9 November 2026
Measurements held42
Confirmed unchanged1 time, most recently 9 November 2026
On Telegramt.me/LLMScience

Topic

Education — 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 11 September 2026 and assigned it the closest of 31 fixed categories, at 99% 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 the other topics.

Observations

These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.

Content that also appears on other registered channels

Posts published here appear in near-verbatim form on 2 other registered channels. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.

Matching posts — open both and compare (6 of the pairs behind the counts below)
Posted firstThenOverlapGap
@LLMScience/1303 · this entry1 Nov 2026, 04:29 UTC@PythonPortal/62618 Nov 2026, 06:07 UTC1.007 days
@LLMScience/1049 · this entry25 Aug 2026, 04:50 UTC@PythonPortal/607226 Aug 2026, 06:07 UTC1.0025 hours
@LLMScience/1322 · this entry10 Nov 2026, 08:11 UTC@PythonPortal/626610 Nov 2026, 16:07 UTC0.997.9 hours
@LLMScience/1012 · this entry15 Aug 2026, 11:48 UTC@PythonPortal/605518 Aug 2026, 06:07 UTC0.993 days
@LLMScience/1318 · this entry8 Nov 2026, 05:48 UTC@PythonPortal/62628 Nov 2026, 16:07 UTC0.9910 hours
@LLMScience/1292 · this entry29 Oct 2026, 04:52 UTC@python_Ninjas/198730 Oct 2026, 17:36 UTC0.9937 hours
Channels this entry shares post bodies with, where at least one matching post pair has no forward credit between the two channels, or to a common source, recorded in our data
ChannelMatching postsText overlapTypical gapPublished first
@PythonPortal14 (8/8 hand-verifiable sample passed)0.9916 hoursthis entry (13–1)
@python_Ninjas7 (7/7 hand-verifiable sample passed)0.9830 hoursthis entry (7–0)

Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.

What this cannot establish

MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.

Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. 1 of the 1 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.

“Published first” means first in this corpus. We hold 110 comparable posts for this entry, running 4 August 2026 to 10 November 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.

The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.

  • Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
  • 'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
  • shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
  • Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).

Recorded under the key clone_source, last confirmed 11 November 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.

Growth

8,35511,3009,827.59 August 2026 — 8,356 subscribers10 August 2026 — 8,355 subscribers12 August 2026 — 8,376 subscribers16 August 2026 — 8,368 subscribers19 August 2026 — 8,366 subscribers22 August 2026 — 8,406 subscribers25 August 2026 — 8,431 subscribers28 August 2026 — 8,418 subscribers31 August 2026 — 8,405 subscribers3 September 2026 — 8,385 subscribers9 September 2026 — 8,382 subscribers17 September 2026 — 8,674 subscribers27 September 2026 — 10,236 subscribers4 October 2026 — 10,455 subscribers9 October 2026 — 10,459 subscribers13 October 2026 — 10,560 subscribers14 October 2026 — 10,664 subscribers15 October 2026 — 10,723 subscribers16 October 2026 — 10,761 subscribers17 October 2026 — 10,788 subscribers18 October 2026 — 10,861 subscribers19 October 2026 — 10,933 subscribers20 October 2026 — 10,975 subscribers22 October 2026 — 10,999 subscribers23 October 2026 — 11,008 subscribers24 October 2026 — 11,015 subscribers24 October 2026 — 11,009 subscribers25 October 2026 — 11,004 subscribers26 October 2026 — 11,090 subscribers28 October 2026 — 11,116 subscribers28 October 2026 — 11,122 subscribers30 October 2026 — 11,129 subscribers31 October 2026 — 11,126 subscribers1 November 2026 — 11,125 subscribers2 November 2026 — 11,156 subscribers3 November 2026 — 11,240 subscribers4 November 2026 — 11,265 subscribers5 November 2026 — 11,281 subscribers6 November 2026 — 11,287 subscribers7 November 2026 — 11,295 subscribers8 November 2026 — 11,297 subscribers9 November 2026 — 11,300 subscribers9 August 20269 November 2026
42 measurements spanning 92 days, net +2,944. 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 7,913–11,742 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 42
Measured (UTC)SubscribersChange
9 Nov 2026, 22:1911,300+3
8 Nov 2026, 14:4911,297+2
7 Nov 2026, 11:1911,295+8
6 Nov 2026, 10:5711,287+6
5 Nov 2026, 11:3511,281+16
4 Nov 2026, 11:0111,265+25
3 Nov 2026, 07:3511,240+84
2 Nov 2026, 06:5111,156+31
1 Nov 2026, 06:1811,125-1
31 Oct 2026, 05:2611,126-3
30 Oct 2026, 02:0611,129+7
28 Oct 2026, 23:5511,122+6
28 Oct 2026, 02:0911,116+26
26 Oct 2026, 23:5111,090+86
25 Oct 2026, 22:1411,004-5
24 Oct 2026, 22:3711,009-6
24 Oct 2026, 02:0911,015+7
23 Oct 2026, 04:1911,008+9
22 Oct 2026, 03:0610,999+24
20 Oct 2026, 21:4510,975first reading

Engagement

122 posts held, back to 4 August 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 34 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
24.2%
avg views ÷ 11,300 subscribers
Avg views / post
2,730
34 posts measured
Reaction rate
0%
reactions ÷ views · ER floor
Posts in window
34
of 122 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 2 of 34 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 November 2026
Posts held122 (4 August 2026 – 11 November 2026)
Views total92,918
Reactions total0
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken11 Nov 2026, 09:21 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.

What this channel posts

Photos
754
Videos
179
Links
968

Lifetime counters from Telegram’s own channel header, read 11 November 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.

Video runtime
6m 26s
Average length
55s

Measured directly from 7 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.

Telegram Stars

Stars received
3
across the posts below
Posts paid on
3
of 122 we hold a reading for · 2%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @LLMScience. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 122 most recent posts we hold for this entry, published 4 August 2026 to 11 November 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Advertising

Ad load
4.10%
5 of 122 posts carry an ad marker
Regulatory tokens
5
posts carrying an erid · 5 distinct tokens
Median views · ads
1,400
over 5 measured posts
Median views · rest
788
over 117 measured posts

An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.

This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.

Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. They are printed side by side with the count behind each rather than as a ratio: an ad and an ordinary post are not otherwise matched — for topic, for length, for hour of day — so the gap between them is a description of two groups and not the effect of one being an ad.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2VtzqueM1E615 November 20265 November 2026
2VtzqvfcpBD111 August 202611 August 2026
2VtzqxRmGTg110 November 202610 November 2026
2VtzqxUz24T119 August 202619 August 2026
2VtzqxiTEce130 October 202630 October 2026

A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it.

Measured over the 122 most recent posts we hold, published 4 August 2026 to 11 November 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.

Recent posts

Telegram’s own ‘edited’ marker: 1 of 34 posts held from the last 30 days (2.94%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

11 Nov 2026, 08:11 UTC525 viewsread 11 November 2026
Photo

Учитесь создавать собственные харнессы. В этой работе Microsoft разработала харнесс, который улучшает способность моделей рассуждать в задачах с длинным контекстом без синтетических данных и дополнительного обучения. Исследователи показали, что Llama3-8B в среднем улучшает результаты на бенчмарках с длинным контекстом на 64,18 пункта без какого-либо обучения. Весь прирост обеспечивается харнессом. Он разбивает зад…

11 Nov 2026, 07:01 UTC713 viewsread 11 November 2026
Photo

За последние две недели были переработаны и обновлены все статьи раздела, посвящённого комплексным числам, на платформе Algebrica. Полный список обновлённых материалов: 1. Комплексные числа 2. Операции с комплексными числами 3. Тригонометрическая форма комплексного числа 4. Формула Эйлера 5. Показательная форма комплексного числа 6. Формула Муавра 7. Корни из единицы 8. Основные неравенства для комплексных чисел 9.…

10 Nov 2026, 17:16 UTC≈1,250 viewsread 11 November 2026

Лю Чэнь, аспирант факультета компьютерных наук Йельского университета, собрал в одном репозитории скрипты для построения графиков из своих научных работ, опубликованных в Nature Machine Intelligence, NeurIPS и других изданиях. Позднее он превратил этот репозиторий в ИИ-скилл. Сейчас проект набрал более 10 000 звёзд на GitHub. При написании научных статей можно поручить ИИ построение столбчатых и лепестковых диаграм…

10 Nov 2026, 15:12 UTC≈1,400 viewsread 11 November 2026
Advertisementerid 2VtzqxRmGTgPhoto

🔍 Открытое ML-собеседование с техлидом Яндекса| 12 ноября, 19:00 мск Приходи онлайн посмотреть, как проходит техническое интервью на ML-инженера, и попробуй ответить на вопросы вместе с кандидатом Как всё будет проходить: 📂 Интервьюер: Любовь Чубарова, техлид в Яндексе (RnD Alice AI), одна из первых сотрудниц YandexArt, преподаватель ВШЭ, экс-руководитель аналитики Центра ИИ Сколтеха. В роли кандидата – инженер-до…

Signed Coloda

10 Nov 2026, 14:17 UTC≈1,310 viewsread 11 November 2026
Photo

Исследователь Google DeepMind Петар Величкович сообщил о завершении полного черновика книги по геометрическому глубокому обучению. Все главы уже доступны на сайте проекта. https://geometricdeeplearning.com/book/

10 Nov 2026, 08:11 UTC≈1,500 viewsread 11 November 2026
Photo

HDMI-кабели могут излучать электромагнитные сигналы, содержащие информацию об изображении на экране. Антенна и программно-определяемый радиоприёмник улавливают эти излучения, а программное обеспечение, например TempestSDR, позволяет восстановить из них распознаваемое изображение. Deep-TEMPEST использует машинное обучение для более точного восстановления текста и изображений из тех же исходных сигналов. В ходе испыт…

10 Nov 2026, 07:24 UTC≈1,450 views0 reactions1 Starread 11 November 2026
Photo

Опубликовано интересное исследование о масштабировании мультиагентных систем. Сегодня одна из главных задач в этой области заключается в том, чтобы научить группы ИИ-агентов лучше координировать свои действия и эффективнее работать сообща. Обычно мультиагентные системы действуют по заранее заданному разработчиком плану. Он определяет, какие задачи выполняет каждый агент и в какой момент агенты должны обмениваться и…

9 Nov 2026, 04:58 UTC≈1,660 viewsread 11 November 2026
Photo

Google показала, как повысить воспроизводимость ML-экспериментов с помощью ИИ-агентов. В исследовании успешность повторного выполнения экспериментов выросла с 76,4% до 92,6%, а расходы на API сократились на 19,7%. Для этого исследователи разработали ReproBridge — систему, которая превращает один успешно воспроизведённый эксперимент в набор материалов для следующих агентов. В него входят очищенный код, настройки окр…

8 Nov 2026, 05:48 UTC≈1,810 viewsread 11 November 2026
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Колумбийский университет опубликовал подробный учебный материал по архитектуре Transformer 📚 В нём разбираются математические основы одной из ключевых технологий современных ИИ-систем: от рекуррентных нейросетей и архитектуры encoder-decoder до механизмов attention и self-attention. Отдельное внимание уделено query, key и value, scaled dot-product attention, multi-head attention и softmax. Главная особенность мате…

7 Nov 2026, 11:11 UTC≈1,900 viewsread 11 November 2026
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Настоятельно рекомендую к прочтению. Узнайте, как устроена одна из самых передовых систем памяти ИИ-агентов. Система памяти — один из важнейших компонентов агентного harness, от которого во многом зависит эффективность его работы. При этом я не думаю, что сегодня существует универсальная система памяти, подходящая для всех задач. Память необходимо постоянно совершенствовать и поддерживать, внося изменения там и т…

5 Nov 2026, 17:15 UTC≈2,180 viewsread 11 November 2026
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NVIDIA выяснила, что ИИ-агентам необязательно хранить всю историю переписки, чтобы хорошо справляться со сложными задачами. Исследователи опубликовали работу, в которой изучили агрессивное сжатие контекста. Вместо того чтобы передавать модели всю историю предыдущих действий, агент сохраняет лишь краткое описание своего текущего состояния. Результаты оказались любопытными. Claude Opus 4.5 набрала 70,2 балла на SWE-b…

Showing the 12 most recent of 122 posts we hold for @LLMScience. 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.

Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.

Posts edited after publishing

@LLMScience edited 3 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
18 August 2026
Most recent edit
11 November 2026

Posts recorded as deleted after publishing

We recorded 1 post on @LLMScience as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.

A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.

First seen missing
12 August 2026
Most recent seen missing
12 August 2026
Median time before seen missing
3 days
Seen missing in the last 30 days
0

Forward network

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 13 registered channels — 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.

Names

Channels on the register whose handles appear in this channel's posts.

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.

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.

Python Portal
@PythonPortal · 50,915
Telegram ranks this channel #15 of 80 here — alongside 79 others — read 25 August 2026
InfoSec Portal
@cybersecinform · 20,042
Telegram ranks this channel #55 of 75 here — alongside 74 others — read 2 October 2026
Montana.exe
@dejavu041 · 49,466
Telegram ranks this channel #60 of 80 here — alongside 79 others — read 25 August 2026
Python Developer
@python_tg · 21,216
Telegram ranks this channel #68 of 85 here — alongside 84 others — read 25 September 2026

This channel appears in 4 seed channels' Telegram-generated recommendation lists 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 November 2026 — this entry's latest reading, not the date you are reading this.

“Data Portal | DS & ML” (@LLMScience), 11,300 subscribers as measured 9 November 2026. Telegram Register, tgregister.com/channel/LLMScience.

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.