Channel
@AlgorithmDesign_DataStructuer
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
6,512subscribers
-9 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001981517816 |
|---|---|
| Type | Channel |
| Username | @AlgorithmDesign_DataStructuer |
| Description | این کانال برای تمامی علاقهمندان به کامپیوتر، مخصوصاً حوزه ساختمان دادهها و الگوریتمها، مفید می باشد. آشنایی با ریاضیات مقدماتی، برنامهنویسی مقدماتی و پیشرفته و همچنین شیگرایی میتواند در درک بهتر مفاهیم این درس کمک کند. 👨💻Admin👉 @Se_mohamad |
| Created | Between 1 March 2023 and 31 October 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 11 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 11 August 2026 |
| On Telegram | t.me/AlgorithmDesign_DataStructuer |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Aug 2026, 03:54 | 6,512 | -5 |
| 8 Aug 2026, 13:37 | 6,517 | -4 |
| 7 Aug 2026, 17:31 | 6,521 | no change |
| 7 Aug 2026, 17:19 | 6,521 | first reading |
21 posts held, back to 25 November 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 9 pagesof 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. It is computed over the 2 of 3 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 10 August 2026 |
|---|---|
| Posts held | 21 (25 November 2025 – 10 August 2026) |
| Views total | 2,459 |
| Reactions total | 3 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 03:05 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 12 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
71 reactions across 19 posts, in 8 distinct kinds. The most used accounts for 31.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 22 | 31.0% | |
| 💯 | 16 | 22.5% | |
| 🔥 | 12 | 16.9% | |
| 🙏 | 11 | 15.5% | |
| 🤣 | 5 | 7.04% | |
| 👌 | 3 | 4.23% | |
| 👏 | 1 | 1.41% | |
| 👨💻 | 1 | 1.41% |
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 19 of the 21 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 71reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 21 most recent posts we hold, published 25 November 2025 to 10 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.
📊 دوره جامع علم داده (Data Science) 🧠 از مفاهیم پایه تا پروژههای واقعی 🐍 به همراه آموزش Python و SQL Server 👥 دوره آنلاین #علم_داده مناسب برای: ✅ دانشجوها ✅تحلیلگران داده ✅کارکنان واحدهای تخصصی و ⭐️ افرادی که دنبال تغییر مسیر شغلی به دنیای تحلیلگری داده و دیتاساینس هستن 📌 مشاوره رایگان + مشاهده جزئیات دوره: 👉 https://httb.ir/dqzwO 🎓 گواهینامه معتبر مؤسسه توسعه --- 📈 جمعبندی سریع: 🌐 دوره آنلاین ✅ شروع با مفاهیم…
🚀 اجرای سادهتر DeepSeek-V4-Flash بهصورت Local! تیم Unsloth نسخه بهینهشدهای از DeepSeek-V4-Flash منتشر کرده که اجرای این مدل را روی سیستمهای شخصی کمهزینهتر و سریعتر میکند. 🔹 پشتیبانی از Context Window بسیار بزرگ (تا ۱ میلیون توکن) 🔹 مصرف حافظه کمتر با نسخههای کوانتیزهشده 🔹 مناسب برای کدنویسی، چت و ساخت AI Agent 🔹 امکان اجرا روی سختافزار شخصی 📌 راهنمای اجرا: https://unsloth.ai/docs/models/deepseek-v4 #…
💯1
🎓 استنفورد دوره جدید Self-Improving AI Agents منتشر کرد. این دوره روی نسل جدید Agentها تمرکز دارد؛ سیستمهایی که فقط پاسخ تولید نمیکنند، بلکه با تفکر در زمان استنتاج، ارزیابی خروجی و خوداصلاحی میتوانند عملکردشان را بهمرور بهتر کنند. از موضوعات مهم آن میتوان به Test-time Compute، استفاده از Verifierها، Tool Use، Memory، RL و Multi-step Reasoning اشاره کرد. 🎥 پلیلیست دوره: https://www.youtube.com/playlist?list=P…
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🟢هوش مصنوعی Gemini فعال شد از طریق سایت و اپلیکیشن آن استفاده کنید. https://gemini.google.com/ نکته مهم: حتما باید اول برنامه شکن را فعال کنید تحریمشکن هست برای عبور از تحریمها👇 http://cafebazaar.ir/app/?id=co.bonyan.shecan&ref=share #هوش_مصنوعی 📣👨💻 @AlgorithmDesign_DataStructue
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سلام، امیدوارم حالتون خوب باشه 🤍 در روزهایی که فضای کشور برای خیلیهامون پر از نگرانی و سؤال شده، بیش از هر زمان دیگه به آرامش، آگاهی و کنار هم بودن نیاز داریم؛ امیدوارم دلهاتون آروم، تنتون سالم و آیندهمون روشنتر از امروز باشه 🌱✨
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۱۰۰+ سؤال و جواب مصاحبه LLM برای هر کسی که در حال آماده شدن برای مصاحبههای AI / ML است، داشتن دانش قوی در زمینه موضوعات مرتبط با مدلهای زبانی بزرگ (LLM) کاملاً ضروری است. این ریپازیتوری شامل بیش از ۱۰۰ سؤال مصاحبه LLM همراه با پاسخ است که طیف گستردهای از مباحث LLM را پوشش میدهد، از جمله: LLM Inference LLM Fine-Tuning LLM Architectures LLM Pretraining Prompt Engineering etc. https://github.com/KalyanKS-NLP/LLM-…
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🚀 روش جدید RLEP: آموزش مدلهای زبانی بزرگ (LLM) با روشهای تقویتی (RL) معمولاً ناپایدار و پرهزینه است؛ درست مثل کوهنوردی که تا نیمه راه میرود اما خسته میشود و دوباره باید از صفر شروع کند! این مقاله روشی به نام RLEP را معرفی میکند که این مشکل را حل کرده است. ایده ساده اما قدرتمند است: ۱. جمعآوری: ابتدا مدل تلاش میکند و مسیرهای رسیدن به پاسخ "صحیح" را ذخیره میکند. ۲. بازپخش (Replay): در آموزشهای بعدی، مدل این …
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🎓 Advanced Large Language Model Agents – Spring 2025 (UC Berkeley) درس پیشرفتهای درباره عاملهای هوشمند مبتنی بر LLM که روی مهارتهای کلیدی مثل استدلال پیچیده، برنامهریزی، تولید کد و ریاضیات تمرکز دارد. این درس بهصورت عمیق به تکنیکهای Inference-time و Post-training، جستوجو و برنامهریزی، Agentic Workflow، استفاده از ابزارها، و کاربرد LLMها در اثبات قضایا و راستیآزمایی برنامهها میپردازد. https://rdi.berkeley…
توی این سایت ابزار بصری جالبی برای تست و مشاهده عملکرد الگوریتمهای معروف جستجو ارائه میدهد. الگوریتمهای مسیریابی کوتاهترین مسیر را با استفاده از همین روشها پیدا میکنند. https://qiao.github.io/PathFinding.js/visual/ A*: جستجوی کمهزینه با استفاده از تخمینگر (Heuristic). IDA*: نسخهای از A* که حافظه کمتری مصرف میکنه. Breadth-First Search (BFS): جستجوی پهنای سطح، مسیر کوتاهترین تعداد قدمها رو پیدا میکنه…
💯2
گوگل. آمازون. مایکروسافت. نتفلیکس. بیش از ۳۰۰ مطالعهی موردی واقعی از طراحی سیستمهای یادگیری ماشین در حدود ۸۰ شرکت. یک ریپازیتوری پیدا کردم که دقیقاً توضیح میدهد سیستمهای ML در مقیاس صنعتی و واقعی چطور ساخته میشوند. https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies اگر در یادگیری ماشین، داده، یا Engineering Leadership جدی هستید، بررسی اینکه چرا سیستمها به این شکل طراحی شدهاند،…
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بیشتر افراد ساعتها وقت خود را صرف پیدا کردن منابع باکیفیت هوش مصنوعی میکنند. اما این ریپوی GitHub، بیسروصدا ۱۳ کتاب رایگان و درجهیک AI را منتشر کرده؛ همه مفید و عمیق، بدون حاشیه 📚✨ داخل این مجموعه چه چیزهایی پیدا میکنید؟ 👇 🧠 مبانی LLM 🎯 یادگیری تقویتی 💼 مصاحبههای Deep Learningه 📐 ریاضیات یادگیری ماشین 🤖 راهنمای OpenAI Agent موارد بیشتر: https://github.com/AniruddhaChattopadhyay/Books #هوش_مصنوعی 📣👨💻 @Algori…
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🤖 یادگیری تقویتی عمیق (Deep Reinforcement Learning) در مدت کوتاهی میتوانید، مباحث مقدماتی، متوسط و پیشرفته یادگیری تقویتی عمیق را یاد بگیرید! در این مخزن ، همه چیز بهصورت منظم و ساختارمند گردآوری شده است؛ از مقالات و آموزشها گرفته تا ویدئوهای یوتیوب، پیادهسازی مقالات، پروژهها و کدها 🚀 http://github.com/andri27-ts/Reinforcement-Learning #هوش_مصنوعی 📣👨💻 @AlgorithmDesign_DataStructue
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Showing the 12 most recent of 21 posts we hold for @AlgorithmDesign_DataStructuer. 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 — 517,971 of 1,151,006entries 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.
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.
@AlgorithmDesign_DataStructuer named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
References a handle that is not a live channel — we have no record it ever was one.
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.
“Algorithm design & data structureچ” (@AlgorithmDesign_DataStructuer), 6,512 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/AlgorithmDesign_DataStructuer.
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.