Channel
@architectureweekly
On this record: Growth · Engagement · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
2,997subscribers
+2 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001662725031 |
|---|---|
| Type | Channel |
| Username | @architectureweekly |
| Created | Between 1 December 2021 and 31 March 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 | 3 |
| Confirmed unchanged | 1 time, most recently 11 August 2026 |
| On Telegram | t.me/architectureweekly |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 11 Aug 2026, 11:37 | 2,997 | +1 |
| 8 Aug 2026, 04:20 | 2,996 | +1 |
| 7 Aug 2026, 20:16 | 2,995 | first reading |
20 posts held, back to 15 May 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 1 pageof 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 5 of 7 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 5 August 2026 |
|---|---|
| Posts held | 20 (15 May 2026 – 5 August 2026) |
| Views total | 6,921 |
| Reactions total | 22 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 20:16 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.
58 reactions across 15 posts, in 8 distinct kinds. The most used accounts for 41.4% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 24 | 41.4% | |
| ❤ | 21 | 36.2% | |
| 👍 | 5 | 8.62% | |
| 🤔 | 3 | 5.17% | |
| 🥴 | 2 | 3.45% | |
| 👎 | 1 | 1.72% | |
| 😱 | 1 | 1.72% | |
| 🤨 | 1 | 1.72% |
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 15 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 58reactions 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 15 May 2026 to 5 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.
Running a self-hosted LLM in Kubernetes with vLLM 👨💼 With the rise of cost for the LLMs and the privacy concerns more and more enterprises opt to run local models(and I am experimenting with them myself). Grab a guy how to setup an open-source LLM with Kubernetes! #llm #cloud #devops #architecture
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SOC2 demystified We recently obtained SOC2 certification for Supplied. Our customers frequently ask how secure their data is with us. Answering this very question in the detailed post #security #soc2
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We became so faster writing code, but do we ship more? Talking with Baruch Sadogursky about what exactly prevents us from unlocking true productivity, and it's not better agents. 👇 https://youtu.be/a_Kq18ufZzU
Clustering Billions of Products for Agentic Commerce with Catalog API 🤓 Shopify Catalog groups billions of listings without a common schema. It first matches products inside each store, then connects them across stores with a Universal Product Identifier (UPI). LLMs assign a structured label to every product. This enables consistent grouping, high precision, and better recall. AI searches based on Catalog data conve…
The State of Streaming to Apache Iceberg in July 2026: Every Path, Its Latency, and What to Do When Seconds Are Not Fast Enough 👨💼 In mid-2026, teams no longer ask, “Should we use Iceberg?” They ask, “How current can our Iceberg tables be?”. And this is where the main tradeoff relies dictating your data architecture and tools to go with. From tuned Flink to Kafka connect latency numbers varies from 30 seconds to 15…
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Architecture Weekly pinned «Most “AI agents” are workflows with an LLM inside. The real difference: who controls the flow? In my new video, I break down the five parts of a real agent—prompt, tools, state, memory and loop—plus the production essentials: tracing, guardrails and evals.…»
Most “AI agents” are workflows with an LLM inside. The real difference: who controls the flow? In my new video, I break down the five parts of a real agent—prompt, tools, state, memory and loop—plus the production essentials: tracing, guardrails and evals. Here's the link: https://youtu.be/SmSv_6bI5QM
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I bought a setup for running local LLMs. Grab the unpacking video! https://www.youtube.com/shorts/xcbmp1p06jM
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People go to the technical conferences and the only value they get are free snacks and some talks missing the true purpose of such events. I published a guide how to actually prepare the conferences and what to do there depending on your career aspirations. https://open.substack.com/pub/softwarearchitectureweekly/p/capturing-value-out-of-technical?r=1m9i62&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
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Stragglers, Not Failures: How Adaptive Hedged Requests Reduce p99 Latency by 74 Percent 🤓 In fan-out microservice architectures, the dominant cause of high p99 latency is stragglers — slow-completing requests rather than failures — because one straggler in a fan-out blocks the entire composite response. While retries are a solution for failed requests, the stragglers require a parallel request if slow response is de…
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The Inference Paradox: How Split-Brain LLMs Are Killing Your GPU ROI 🤓 LLM inference has a structural hardware mismatch: the prefill phase is compute-bound (processing all input tokens in a single forward pass) while decode is memory-bandwidth-bound (reading the full KV cache to emit one token per step), so coupling both phases on the same GPU means each permanently starves the other. Kubex's enterprise audits surfa…
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https://open.substack.com/pub/softwarearchitectureweekly/p/building-a-stripe-app-for-data-sync?r=1m9i62&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
Showing the 12 most recent of 20 posts we hold for @architectureweekly. 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 — 1,056,110 of 1,160,990entries 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.
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.
@architectureweekly 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.
“Architecture Weekly” (@architectureweekly), 2,997 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/architectureweekly.
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.