Channel
@computer_science_and_programming
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
140,868subscribers
-311 since we began measuring on 5 August 2026
Risers and fallers across the register · movement among entries of 100,000–316,228.
| Telegram ID | -1001309924870 |
|---|---|
| Type | Channel |
| Username | @computer_science_and_programming |
| Description | Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science |
| Created | 12 April 2018 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 21 August 2026 |
| Measurements held | 17 |
| Confirmed unchanged | 1 time, most recently 21 August 2026 |
| On Telegram | t.me/computer_science_and_programming |
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 9 August 2026 and assigned it the closest of 31 fixed categories, at 100% 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 why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 21 Aug 2026, 13:17 | 140,868 | -22 |
| 20 Aug 2026, 15:22 | 140,890 | -5 |
| 19 Aug 2026, 13:59 | 140,895 | -12 |
| 18 Aug 2026, 14:07 | 140,907 | -15 |
| 17 Aug 2026, 10:47 | 140,922 | -50 |
| 15 Aug 2026, 18:52 | 140,972 | +32 |
| 14 Aug 2026, 07:05 | 140,940 | -18 |
| 13 Aug 2026, 00:25 | 140,958 | -31 |
| 12 Aug 2026, 01:37 | 140,989 | -37 |
| 11 Aug 2026, 04:55 | 141,026 | -51 |
| 10 Aug 2026, 01:42 | 141,077 | -25 |
| 8 Aug 2026, 23:16 | 141,102 | -27 |
| 7 Aug 2026, 22:32 | 141,129 | -25 |
| 7 Aug 2026, 01:17 | 141,154 | -19 |
| 6 Aug 2026, 03:00 | 141,173 | -6 |
| 6 Aug 2026, 01:31 | 141,179 | no change |
| 5 Aug 2026, 22:11 | 141,179 | first reading |
28 posts held, back to 28 June 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 36 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.
| Window | Rolling 30 days · latest post in window 19 August 2026 |
|---|---|
| Posts held | 28 (28 June 2026 – 19 August 2026) |
| Views total | 182,410 |
| Reactions total | 267 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 22 Aug 2026, 11:23 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 22 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.
522 reactions across 27 posts, in 7 distinct kinds. The most used accounts for 46.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 243 | 46.6% | |
| 👍 | 151 | 28.9% | |
| 👎 | 50 | 9.58% | |
| 🔥 | 35 | 6.70% | |
| 👨💻 | 16 | 3.07% | |
| 🗿 | 15 | 2.87% | |
| 😁 | 12 | 2.30% |
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 28 of the 28 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 536reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 28 most recent posts we hold, published 28 June 2026 to 19 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.
How MongoDB Reduced PR Review Times with GitDailie A case study describes how MongoDB adopted GitDailies, a GitHub PR alerting and metrics tool, to help engineering teams meet PR review SLAs. Customizable alerts, Slack integration, and personalized dashboards replaced ineffective existing alerting, reportedly cutting review times by up to 54% for one team and over 28% for nearly half the teams involved.
👍5
Stop burning tokens on code review An engineering leader shares why AI-based code review tools like Cursor's BugBot and custom Claude code review skills became too slow, noisy, and expensive (hitting $1000/day in one case) for a team producing thousands of PRs a quarter. The fix that worked: converting team-specific coding rules from markdown guidelines into custom linters, which run in seconds, are deterministic, c…
👍7❤5
Does anyone run Postgres without PgBouncer? Almost every notable managed Postgres provider bundles a connection pooler like PgBouncer, according to a survey of major providers including AWS RDS, Azure, Google Cloud SQL, Supabase, Neon, DigitalOcean, and others. Only IBM Cloud and Oracle OCI lack a managed pooling option, and both are dismissed as unlikely choices outside enterprise sales cycles. The piece argues conn…
❤7👍5
WebSocket vs SSE vs Long Polling: The Real Cost of 1,000 Events A hands-on benchmark built with a Node harness and a byte-counting TCP proxy measures the real wire cost of WebSocket, SSE, and long polling delivering 1,000 events. WebSocket used 119,692 bytes, SSE 131,596 (10% more), and HTTP/1.1 long polling 884,698 bytes (7.4x), mostly re-sent headers. On HTTP/2, long polling drops to 182,475 bytes (1.56x payload). …
👍9❤3
Dark mode toggles: two states are enough • Lea Verou (Light, Dark, System). The core argument is that users only seek out theme toggles when something is wrong — not to preemptively express intent — making the 'System' state irrelevant at the moment of interaction. Tri-state toggles expose the underlying data model rather than aligning with actual user goals, adding cognitive load and UI friction for an extremely rar…
❤12👎3👍1
No Index GroupBy Optimization Dolt and Doltgres were using a secondary index for GROUP BY queries even when a full table scan would be faster. By analyzing EXPLAIN output and flame graphs, the team discovered that when a filter selects more than ~50% of rows, the secondary index lookup overhead outweighs its benefits. They added heuristics to the query coster: secondary indexes are only preferred when they select few…
❤5👍3
DuckDuckGo "Normal F**king Sunglasses" Sold Out In Under a Week DuckDuckGo partnered with sunglasses brand Knockaround to release "Normal F**king Sunglasses" — a satirical product mocking AI-powered smart glasses. The glasses have no camera, microphone, AI, battery, or electronics of any kind. They sold out in under a week. The collaboration is a commentary on growing surveillance concerns around AI glasses, particu…
❤12👍8😁3🔥2
What's new in PHP 8.6 PHP 8.6, scheduled for release on November 19, 2026, introduces several notable features: partial function application (PFA) for prefilling closure parameters, readonly property defaults (now allowed when implementing interfaces with property hooks), a new low-level Polling API for I/O multiplexing with epoll/WSAPoll backends, a built-in clamp() function, new isReadable/isWriteable reflection me…
❤17👍3🔥1🗿1
Retiring the Copilot Billing Preview app GitHub has retired the Copilot Billing Preview app. Copilot spend can now be managed directly in GitHub billing settings, which offers a more complete view including user-level budgets, cost centers, and usage pool allocation. Users can view AI usage, set spending budgets, access user-level budget controls for organizations and enterprises, and pull raw usage data via usage re…
❤5👍4😁2
Substack writers, you need a website Writers who rely on Substack as their primary digital home are making a strategic mistake. Substack and similar platforms are distribution tools, not permanent homes — when you build on someone else's platform, you're a tenant, not an owner. The POSSE model (Publish on your Own Site, Syndicate Elsewhere) offers a better approach: publish first to a domain you own and control, then…
❤14👍9
The Disappearing Senior: AI and Entry Level Jobs AI is eliminating entry-level developer work, but there's a less-discussed second effect: senior engineers are quietly losing their skills by only approving AI-generated output rather than doing the work themselves. The 'reasoning muscle' atrophies when judgment is never exercised. Meanwhile, no new seniors are being produced because the junior work that used to forge …
👍19❤11
CVE-2026-59328: Cross-Site Scripting in Eclipse Spring Boot Starter Wizard Dependency Tooltips A CVE advisory (CVE-2026-59328) has been published for a Cross-Site Scripting (XSS) vulnerability found in the Eclipse Spring Boot Starter Wizard's dependency tooltips. The advisory is hosted on spring.io and relates to the Eclipse IDE plugin used for Spring Boot project setup.
❤3🔥3👍1👨💻1
Showing the 12 most recent of 28 posts we hold for @computer_science_and_programming. 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 — 182,498 of 1,583,249entries 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.
Named by 8 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.
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.
Telegram’s own answer, not this register’s.When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 13 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
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.
This channel appears in 31 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 21 August 2026 — this entry's latest reading, not the date you are reading this.
“Computer Science and Programming” (@computer_science_and_programming), 140,868 subscribers as measured 21 August 2026. Telegram Register, tgregister.com/channel/computer_science_and_programming.
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.