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Channel

GitHub Trends

@githubtrending

On this record: Growth · Engagement · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry

11,300subscribers

+113 since we began measuring on 6 August 2026

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

Register entry

Telegram ID-1001080389054
TypeChannel
Username@githubtrending
Created27 November 2016measured — cross-checked against a third-party dataset (TGDataset)
First recorded6 August 2026
Last confirmed live22 August 2026
Measurements held17
Confirmed unchanged1 time, most recently 22 August 2026
On Telegramt.me/githubtrending

Growth

11,18711,30011,243.56 August 2026 — 11,187 subscribers6 August 2026 — 11,187 subscribers7 August 2026 — 11,201 subscribers8 August 2026 — 11,203 subscribers9 August 2026 — 11,219 subscribers10 August 2026 — 11,233 subscribers11 August 2026 — 11,247 subscribers12 August 2026 — 11,250 subscribers13 August 2026 — 11,258 subscribers14 August 2026 — 11,265 subscribers16 August 2026 — 11,274 subscribers17 August 2026 — 11,280 subscribers18 August 2026 — 11,285 subscribers19 August 2026 — 11,292 subscribers20 August 2026 — 11,296 subscribers21 August 2026 — 11,297 subscribers22 August 2026 — 11,300 subscribers6 August 202622 August 2026
17 measurements spanning 17 days, net +113. 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 11,170–11,317 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
22 Aug 2026, 18:5811,300+3
21 Aug 2026, 11:0411,297+1
20 Aug 2026, 08:4711,296+4
19 Aug 2026, 11:1611,292+7
18 Aug 2026, 12:4311,285+5
17 Aug 2026, 12:2511,280+6
16 Aug 2026, 03:1411,274+9
14 Aug 2026, 16:3411,265+7
13 Aug 2026, 06:0611,258+8
12 Aug 2026, 08:3211,250+3
11 Aug 2026, 10:4611,247+14
10 Aug 2026, 07:2011,233+14
9 Aug 2026, 06:4211,219+16
8 Aug 2026, 03:0111,203+2
7 Aug 2026, 05:4411,201+14
6 Aug 2026, 02:3611,187no change
6 Aug 2026, 00:3111,187first reading

Engagement

57 posts held, back to 29 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 27 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
4.92%
avg views ÷ 11,300 subscribers
Avg views / post
556
57 posts measured
Reaction rate
0.235%
reactions ÷ views · ER floor
Posts in window
57
of 57 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 35 of 57 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 22 August 2026
Posts held57 (29 July 202622 August 2026)
Views total31,677
Reactions total47
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken22 Aug 2026, 15:06 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.

Reaction mix

46 reactions across 30 posts, in 4 distinct kinds. The most used accounts for 60.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍2860.9%
1634.8%
12.17%
👎12.17%

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 35 of the 57 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 47reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 57 most recent posts we hold, published 29 July 2026 to 22 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.

Recent posts

22 Aug 2026, 13:30 UTC88 viewsread 22 August 2026

#shell #a311d #allwinner #amlogic #h96 #hg680p #hk1 #n1 #openwrt #r66s #r68s #rockchip #s905 #s905d #s905x #s905x2 #s905x3 #s912 #s922x #ugoos #x96 OpenWrt turns a TV box or router into a flexible Linux system you can customize with many packages. You can install it, update the system or kernel, make swap space, back up the old system, and reset it if needed. This helps you give old hardware new life, add features y

21 Aug 2026, 23:00 UTC282 views2 reactionsread 22 August 2026

#typescript #agent_runtime #ai #ai_agent #apache #cli #desktop #electron #event_sourcing #llm #local_first #maka #tool_use #typescript Maka is a local-first agent workspace that runs on your computer, lets you use your own model connection, and can read files, run tools, and save recoverable work records. This helps you keep data local, control permissions, and recover or continue work more safely. https://github.c

👍2

21 Aug 2026, 22:30 UTC279 viewsread 22 August 2026

#python This is a free research tool for removing a model’s refusal behavior while trying to keep its main language ability. It gives you an easy web app, a command line tool, a Python API, and tests to study how safety parts work and how they change. For you, the benefit is simple: you can run clear checks, compare models, save time, and choose how much control and risk you want when working with local AI models.

20 Aug 2026, 14:30 UTC420 viewsread 22 August 2026

#rust #dpi #gpui #hid #hidpp #local_first #logitech #logitech_mouse #logitech_options #mouse_remapping #mx_master #privacy #rust #smartshift OpenLogi is a light, local-first, open-source replacement for Logitech Options+ that works on macOS, Linux, and Windows. It lets you remap buttons and keys, use profiles, control Logitech mice, keyboards, and webcams, and save settings in one plain TOML file. The benefit to you

20 Aug 2026, 13:30 UTC394 viewsread 22 August 2026

#go Agent Substrate is a system for running many agent-like apps at scale on Kubernetes. It can pause and restart them quickly, keep their memory and files, and pack many actors onto fewer machines, which saves cost and improves speed for you. https://github.com/agent-substrate/substrate

20 Aug 2026, 12:30 UTC369 views1 reactionsread 22 August 2026

#kotlin Turn a Google Timeline file into a travel video on Android or iPhone, with exact date ranges, preview, and MP4 export. It works on-device, needs no Google sign-in, and keeps your Timeline file private. The benefit is fast, easy sharing of your trips as a video while keeping your location data under your control. https://github.com/mahlernim/google-timeline-visualizer

👍1

20 Aug 2026, 12:00 UTC355 views2 reactionsread 22 August 2026

#python #agent #agent_security #ai_infra #ai_red_teaming #ai_security #llm #llm_evaluation #llm_jailbreak #llm_security #mcp_scan #openclaw_security #prompt_injection #prompt_security #scanner #security #security_tools #skill_scanner #skills_security #vulnerability A.I.G (AI-Infra-Guard) is a Tencent Zhuque Lab tool for AI red teaming and security checks. It scans AI infrastructure, MCP servers, agent skills, and ja

👍2

20 Aug 2026, 11:30 UTC346 views1 reactionsread 22 August 2026

#mojo #ai #language #machine_learning #max #modular #mojo #programming_language This repo has open-source tools for building and running AI with the Modular Platform, including MAX and Mojo. You can use it to start faster with model serving, AI code, examples, docs, and community help. It also shows where to contribute, report bugs, and check licenses, which helps you learn, build, and use the platform safely. http

👍1

19 Aug 2026, 15:00 UTC444 views2 reactionsread 22 August 2026

#rust This text explains how to build, run, and test a local Amadeus node on Ubuntu with Docker or Podman, then start it as a system service with auto-updates and optional computor or validator startup. The main benefit is that you can quickly set up a working testnet, send transfers, deploy a contract, and run the node reliably on your machine. https://github.com/amadeusprotocol/node

1👎1

19 Aug 2026, 14:30 UTC453 views1 reactionsread 22 August 2026

#javascript #cloud #cloud_native #cncf #configuration_management #design_patterns #design_system #gatsbyjs #hacktoberfest #internship #kanvas #kubernetes #lfx #management_plane #meshery #orchestration #playground #visual_designer #visualization Layer5 makes cloud software for managing apps and infrastructure, with tools like Meshery for Kubernetes, Kanvas for design and operations, Nighthawk for performance testing,

👍1

18 Aug 2026, 12:30 UTC555 views1 reactionsread 22 August 2026

#typescript #agents #claude_code #free #harness #harness_engineering #memory This is a desktop app that turns coding CLIs into a team of agents that work together on your computer, with your own clone acting as the boss. It helps you by letting many agents share memory, send messages, and handle tasks while you can watch, guide, and approve important actions, so work can keep moving even when you are away. https://

👍1

18 Aug 2026, 12:00 UTC519 viewsread 22 August 2026

#plsql This is an open-source radar project called AERIS-10. It is a low-cost 10.5 GHz phased array radar with two versions: one for 3 km and one for 20 km, using open hardware, FPGA processing, GPS/IMU support, and a Python control app. It helps you learn, build, test, or improve real radar systems with ready design files and code. https://github.com/NawfalMotii79/PLFM_RADAR

Showing the 12 most recent of 57 posts we hold for @githubtrending. 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

Citation-graph rank — 989,290 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.

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

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 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.

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.

Computer Science and Programming
@computer_science_and_programming · 140,868
Telegram ranks this channel #48 of 87 here — alongside 86 others — read 13 August 2026

This channel appears in 1 seed channel's Telegram-generated recommendation list 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 22 August 2026 — this entry's latest reading, not the date you are reading this.

“GitHub Trends” (@githubtrending), 11,300 subscribers as measured 22 August 2026. Telegram Register, tgregister.com/channel/githubtrending.

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.