Telegram RegisterThe public register of Telegram

Channel

CODER_OFF

@coder_off_blog

On this record: Growth · Engagement · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry

647subscribers

-2 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1002491075200
TypeChannel
Username@coder_off_blog
CreatedBetween 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live13 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 13 August 2026
On Telegramt.me/coder_off_blog

Growth

6476496486 August 2026 — 649 subscribers6 August 2026 — 649 subscribers13 August 2026 — 647 subscribers6 August 202613 August 2026
3 measurements spanning 7 days, net -2. 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 647–649 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
13 Aug 2026, 03:17647-2
6 Aug 2026, 17:31649no change
6 Aug 2026, 03:18649first reading

Engagement

19 posts held, back to 22 July 2025the 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 · 30 days
700.4%
avg views ÷ 647 subscribers
Avg views / post
4,530
2 posts measured
Reaction rate
0.188%
reactions ÷ views · ER floor
Posts in window
2
of 19 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 19 July 2026
Posts held19 (22 July 202519 July 2026)
Views total9,063
Reactions total17
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken6 Aug 2026, 17:31 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

296 reactions across 19 posts, in 10 distinct kinds. The most used accounts for 26.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍7726.0%
🔥7224.3%
😁5719.3%
🤣5719.3%
217.09%
🐳41.35%
31.01%
👏31.01%
🤝10.338%
🫡10.338%

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 19 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 296reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 19 most recent posts we hold, published 22 July 2025 to 19 July 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

19 Jul 2026, 20:35 UTC≈8,550 views13 reactionsread 6 August 2026
Photo

The internet has accumulated a lot of knowledge in the form of PDFs. Some of these are digital-native PDFs, while others are just wrappers around images. Until now, there hasn't been much demand for parsing these PDFs in a structured way. Document parsing has become much more popular in recent years. We wanted to leverage all that accumulated data to train our LLMs. This fueled interest in building OCR systems that

👍9🔥4

19 Jul 2026, 19:58 UTC513 views4 reactionsread 6 August 2026

https://www.linkedin.com/posts/bnhop_we-run-our-coval-stt-benchmark-every-30-minutes-ugcPost-7483956231968018432-cVqB/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAzP3TIB4pvnEUAvi_ABR6D5qCr_LxAIk0s Btw folks, follow her to get interesting Voice AI related stats weekly. She is a Ex-Tesla, and currently building a coval.ai, voice ai eval solution.

3🔥1

1 Jun 2026, 21:37 UTC897 views8 reactionsread 6 August 2026

https://www.youtube.com/watch?v=xOP1PM8fwnk One of the best explanations of how current Video Gen models are built. This is very interesting space to be in. You learn how H264\265 codecs are built. How to leverage the hybrid of Autoregressive and Diffusion models to build a time series of images. (Video is series of images over time + Audio). The video quickly touches FFT (Fast Fourie Transform) as well. FFT is a f

🔥62

5 May 2026, 20:36 UTC≈1,210 views8 reactionsread 6 August 2026

https://www.youtube.com/watch?v=kYkIdXwW2AE If you are aware, Yann LeCoun has left Meta and actively researching/developing and investing in other startups that are trying to move frontier in NON-LLM directions. Yann isinvesting / betting on World Models (Generic name). He thinks LLMs are not the best proxies of the way Human intelligence works. On this video, Yann is sharing his thoughts on JEPE models. Problems o

👍53

18 Apr 2026, 04:14 UTC≈1,380 views49 reactionsread 6 August 2026
Photo

AGI is here folks! Holly cow! Claude Opus 4.7 did it!

😁44👍5

3 Mar 2026, 11:49 UTC≈2,320 views22 reactionsread 6 August 2026
Photo

So true. There is not much MOAT in software anymore. I bet we are gonna see lots of Software companies start adding Hardware products to create a defensibility very soon.

👍22

10 Feb 2026, 22:04 UTC≈1,940 views25 reactionsread 6 August 2026
Video

My laziness hit a new rock bottom :). Now Claude Code is reading its output out loud:). So I don't have to read small text out of my screen all the time :). Local TTS https://github.com/ktaletsk/claude-code-tts#

🤣19🔥4👍2

8 Feb 2026, 23:04 UTC≈1,390 views31 reactionsread 6 August 2026
Video

I became overly dependant on Claude Code lately. I appreciate the fact that Claude Code shows a horizontal green indefinit progress bar at the top of my terminal, but that is so easy to miss the point when ClaudeCode is done with my task or it wants my input to go forward. Apparently you can use hooks to play sound :). So, what I did was, I asked claude the following: Change my claude hook sound effects to the war

🤣30👍1

2 Feb 2026, 07:38 UTC≈1,930 views5 reactionsread 6 August 2026

STEP-3.5-FLASH: Breaking the Speed vs. Intelligence Trade-off StepFun released a model with 196 billion parameters, but only 11 billion activate per token. This is a sparse Mixture-of-Experts model. The model is performed pretty well on SWE-Bench. People are claiming that to be GPT-4 level quality which is pretty good based on my experience. Its throughput is 300 tokens per second on modern hardware. Compare that

🔥5

31 Dec 2025, 03:49 UTC≈1,470 views28 reactionsread 6 August 2026
Video

We are living in a world where - Resumes are generated by GenAI, - Applications are submitted by AI agents - Replies are written by AI agents :)

😁13🤣8👍42🔥1

21 Dec 2025, 23:42 UTC≈1,760 views7 reactionsread 6 August 2026

https://www.youtube.com/watch?v=-Tgc_9uYJLI Thisis a big leap forward in On-Device / Edge infrence. This model can work directly on your devices or browser. Our Agents @Numeo have to have lots of skills/instructions. This comes with its challanges. You probably have heard about "Curs of Instructions" paper that explains why having too much instructions is bad. At Numeo we have been doubling on building our Agents i

🔥43

Showing the 12 most recent of 19 posts we hold for @coder_off_blog. 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 — 77,331 of 1,336,469entries 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.

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.

Mentions

Named by 3 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.

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 13 August 2026 — this entry's latest reading, not the date you are reading this.

“CODER_OFF” (@coder_off_blog), 647 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/coder_off_blog.

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.