Channel
@littleQA
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
18subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003775476977 |
|---|---|
| Type | Channel |
| Username | @littleQA |
| Created | Between 1 February 2026 and 30 June 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 11 August 2026 |
| Last confirmed live | 11 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/littleQA |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Aug 2026, 18:16 | 18 | no change |
| 7 Aug 2026, 12:48 | 18 | first reading |
20 posts held, back to 9 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 1 pageof Telegram’s post history, 20 posts per page.
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.
| Window | Rolling 30 days · latest post in window 11 August 2026 |
|---|---|
| Posts held | 20 (9 August 2026 – 11 August 2026) |
| Views total | 92 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 11 Aug 2026, 18:16 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.
Lifetime counters from Telegram’s own channel header, read 11 August 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.
Использование AST для более точной селекции тестов в PR В статье на dev.to подробно описан подход к автоматическому выбору тестов для pull request'ов с использованием агента AI. Ключевой элемент - модуль diff_parser.py, который анализирует изменения с помощью Abstract Syntax Tree (AST). AST позволяет не просто фиксировать изменённые строки, но и идентифицировать, какие именно функции или классы были затронуты. Это …
Esto dolió
😂 «Верь в себя!» Anthropic поставила перед Claude задачу — «просто попробовать» доказать одну из главных нерешённых проблем математики. Модель не справилась, но по пути неожиданно улучшила результат, над которым математики бились десятилетиями. ИИ две сессии подряд гонял 60 субагентов, перебирал сотни идей и в итоге сдвинул планку с 41,6% до 67,2%. А человек просто писал «продолжай» и «верь в себя». Детали истории…
The double pill dilemma
Минутка зануды Рассказывают про успешное внедрение AI-агента в прод, используя Spring Boot и Spring AI. Ключевые моменты - канареечные релизы, автоматический откат моделей и ограничение затрат. Но если присмотреться: после внедрения на 5% трафика P95 latency вырос на 38%. Тестовые 40 кейсов из предыдущих частей не учли длинные реальные диалоги. Агент начал вызывать shipping-tool вдвое чаще, чем ожидалось. Откатилис…
🔥 Топ AI-репозитории на GitHub 1️⃣ paperclipai/paperclip ⭐ 76,712 (+198 сегодня) · TypeScript The open-source app everyone uses to manage agents at work 2️⃣ opa334/Dopamine ⭐ 6,097 (+111 сегодня) · C Dopamine is a semi-untethered jailbreak for iOS 15 to 26(.0.1) 3️⃣ sv-number/skills ⭐ 117 · Python Give your AI agent a phone number: order a private number in 200+ countries over the API, read the SMS verification co…
densely от MIT - это компрессия для LLM со сжатием в 2-8 раз, при этом восстанавливает данные байт в байт. Вместо стандартного резюме, которое убирает важные детали, densely использует lzma и конвертирует сжатые байты в 65,536 английских слов, каждое из которых стоит один токен. Это позволяет достичь 16 бит на токен. Для сравнения, lzma+base64 дает лишь 8,8 бит/токен. Бенчмарки: логи сжимаются в 6,94 раза, JSON - в …
Архитектуры глубоких исследовательских агентов: ничего нового Статья заявляет о некоем "сходстве" в архитектуре GPT Researcher, LangChain's Open Deep Research и Milvus's DeepSearcher. Все они включают планировщик, параллельных поисковых агентов и публикацию. Однако, это не то, что можно назвать прорывом. Многопоточность и планирование - стандартные практики в системах, требующих обработки больших массивов данных. …
Put everything in ausb and mail it to aws
Muse Glimmer от Meta: хайп или реальность? Заявляют: Meta представила Muse Glimmer, новый мультимодальный LLM с 30B параметров, ориентированный на локальные и агентные задачи. Открытый исходный код под лицензией Apache 2.0. За: Модель превосходит конкурентов по многим бенчмаркам. Например, на DeepSearch QA - 74.6 против 61.7 у Gemma4-31B и 71.1 у Qwen3.6-27B. Поддерживается в популярных библиотеках, включая transfo…
Anthropic включает автоматический режим Claude Code по умолчанию Что: Anthropic с 14 августа включает авто-режим в Claude Code по умолчанию для Pro, Max и Team аккаунтов. Вместо запроса на каждое действие, система продолжает работу, если действие не является "необратимым, разрушительным или выходящим за пределы окружения". Как: В тестах авто-режим показал себя безопаснее ручного обзора - 89% вредоносных действий бы…
“we sandboxed the agent” -- meanwhile the agent...
Showing the 12 most recent of 20 posts we hold for @littleQA. 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 — 418,034 of 1,340,412entries 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.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
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.
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.
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 11 August 2026 — this entry's latest reading, not the date you are reading this.
“Маленький QA в большом IT” (@littleQA), 18 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/littleQA.
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.