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 8 August 2026 and assigned it the closest of 31 fixed categories, at 96% 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.
Growth
32 measurements spanning 43 days, net -79,177. 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 669,535–772,466 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 32
Measured (UTC)
Subscribers
Change
19 Sept 2026, 15:37
681,412
-4,248
17 Sept 2026, 04:01
685,660
-2,825
15 Sept 2026, 06:17
688,485
-3,111
13 Sept 2026, 15:54
691,596
-3,497
11 Sept 2026, 20:37
695,093
-3,236
9 Sept 2026, 12:59
698,329
-7,003
6 Sept 2026, 01:56
705,332
-4,551
3 Sept 2026, 20:17
709,883
-2,520
2 Sept 2026, 09:43
712,403
-3,150
1 Sept 2026, 07:25
715,553
-1,393
31 Aug 2026, 07:37
716,946
-2,790
30 Aug 2026, 04:56
719,736
-2,212
29 Aug 2026, 04:44
721,948
-1,579
28 Aug 2026, 02:33
723,527
-1,201
27 Aug 2026, 04:15
724,728
-1,634
26 Aug 2026, 01:23
726,362
-1,873
25 Aug 2026, 04:28
728,235
-1,702
23 Aug 2026, 23:35
729,937
-3,583
22 Aug 2026, 10:26
733,520
-2,870
21 Aug 2026, 02:33
736,390
first reading
Engagement
20 posts held, back to 18 July 2025 — the reader has reached the start of this channel’s public history, so this is the full archive Telegram still exposes. Read across 100 pages of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 15 February 2026. An engagement rate over an empty window would be a number about nothing.
What this channel posts
Photos
12
Links
36
Lifetime counters from Telegram’s own channel header, read 24 September 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.
Reaction mix
461 reactions across 19 posts, in 37 distinct kinds. The most used accounts for 29.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
135
29.3%
👍
109
23.6%
🔥
44
9.54%
⚡
23
4.99%
❤🔥
14
3.04%
🍓
14
3.04%
👏
13
2.82%
🎉
12
2.60%
💋
12
2.60%
🥰
9
1.95%
🎄
8
1.74%
🤡
8
1.74%
✍
7
1.52%
💯
7
1.52%
☃
5
1.08%
🤔
5
1.08%
🌚
4
0.868%
👎
4
0.868%
🕊
3
0.651%
😁
3
0.651%
17 further kinds
22
4.77%
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 20 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 461 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 18 July 2025 to 15 February 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.
Telegram Stars
Stars received
303
across the posts below
Posts paid on
13
of 19 we hold a reading for · 68%
Most on one post
124
single highest reading
A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @gusarich_thoughts. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.
Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.
This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.
Counted over the 20 most recent posts we hold for this entry, published 18 July 2025 to 15 February 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.
When I wrote that post earlier, I realized that this would become obsolete very soon once new models are released. So I decided to make a live tier list of frontier LLMs and keep it updated with my opinions on the latest models that I've used myself.
It is accessible here, and I'll make sure to keep updating it every time I have tested a new frontier model well enough to have a proper opinion on it.
It's important …
Things got too easy with AI
AI provides incredible value to me and to many other people in our daily lives and work. It's now possible to do more, better, and faster than ever. But I realized that the progression of AI capabilities outpaced the progression of my goals.
I recently started thinking in retrospect about what I was doing and what I was thinking about in the past months, and it feels like I didn't really…
I gave Codex its own Mac Mini
I was playing around with Codex CLI a lot over the holidays, and apart from making it run 30 instances of itself as "subagents" (actually just doing codex exec runs in background terminals) I also decided to buy a fresh Mac Mini and give it to Codex.
I quickly implemented a pretty simple setup, that consists of a Codex caller that controls Codex process and that forces it to run in an …
TON Vanity
Meet the new blazingly fast vanity address generator for TON smart contracts!
The previous state-of-the-art solution was released 3 years ago and hasn't improved much since. We at TON Studio decided to develop a completely new solution from scratch, following the usage patterns the previous solution introduced.
Optimizations in both the smart contract and the kernel lead to extreme generation speedups. …
My personal opinion based on experience:
* GPT-5.1 has the best instruction following, strong agentic capabilities, and very good skills in math, coding, and problem solving.
* GPT-5.1-Codex-Max has worse general capabilities than GPT-5.1, but is noticeably better for large and complex coding tasks.
* Opus 4.5 has the best implicit intent understanding, very good instruction following and agentic capabilities, but…
What LLM to use today?
Many major releases occurred in the past weeks. The current frontier consists of models that a couple of months ago were only rumors. And they are great.
OpenAI has GPT-5.1 and GPT-5.1-Codex-Max; Anthropic has Opus 4.5; Google has Gemini 3 Pro. I'm often working with code, and therefore I need a good coding model. I look at coding benchmarks, like SWE-bench, but scores there differ by just a …
There is nothing out-of-distribution
AI turned out to be very simple if you think about it. You just make a model that works with something generic, and feed as much training data as you can into it. The generic data I mean here is text. People had writing for thousands of years and the whole world is built on it — we write, we speak, we read, and we listen our entire lives. It's so deep in our brains that it's hard…
There is no singularity
When mentioning singularity, people often think of some "point" in time when AI progress starts to speed up exponentially very quickly with no human control and it all kind of converges to infinity and we don't know what will happen the second after. And I myself had a similar picture in my head too, until recently.
I was thinking that predicting anything after 2027 is impossible because of …
👍15❤5⚡3🔥2🤡2👏1🙏1
Showing the 12 most recent of 20 posts we hold for @gusarich_thoughts. 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.
Stars beside a post are paid reactions — Telegram Stars, bought with money and spent on that post. They are a different unit from reactions and are never added to them, here or anywhere else on this page.
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.
Domains linked from posts
7 domains this channel’s own posts have linked to, measured by scanning the post bodies themselves — not the channel’s description, which is the separate Declared links section below when this entry has one. Appearing here is not a claim about who runs the linked site or why the channel linked to it; an advertisement, a news citation and a malicious link all leave the same kind of row.
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 19 September 2026 — this
entry's latest reading, not the date you are reading this.
“Gusarich's thoughts” (@gusarich_thoughts), 681,412 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/gusarich_thoughts.
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.
