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Channel

Let's become testers

@be_tester

On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry

206subscribers

-2 since we began measuring on 7 August 2026

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

Register entry

Telegram ID-1001623599472
TypeChannel
Username@be_tester
CreatedBetween 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live24 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 24 August 2026
On Telegramt.me/be_tester

Growth

2062082077 August 2026 — 208 subscribers8 August 2026 — 208 subscribers24 August 2026 — 206 subscribers7 August 202624 August 2026
3 measurements spanning 16 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 206–208 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
24 Aug 2026, 06:25206-2
8 Aug 2026, 11:05208no change
7 Aug 2026, 20:22208first reading

Engagement

20 posts held, back to 3 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
15.5%
avg views ÷ 206 subscribers
Avg views / post
32.0
2 posts measured
Reaction rate
this channel exposes no reaction counts
Posts in window
2
of 20 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 4 August 2026
Posts held20 (3 July 20254 August 2026)
Views total64
Reactions total
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken8 Aug 2026, 11:05 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

6 reactions across 6 posts, in 4 distinct kinds. The most used accounts for 50.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
350.0%
116.7%
🆒116.7%
🔥116.7%

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 6 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 6reactions 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 3 July 2025 to 4 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

4 Aug 2026, 17:20 UTC26 viewsread 8 August 2026

Every pipeline costs a lot Anthropic’s API doesn’t block access immediately once your balance hits zero. Thus, a single mistake that goes unnoticed overnight could result in your AI token balance not only being depleted but also falling deep into the red. When your test launcher starts behaving like an unattended ATM, reactive monitoring is no longer enough—architectural discipline is required. Here are the strateg

28 Jul 2026, 17:10 UTC38 viewsread 8 August 2026

Why your LLM-as-a-Judge is "Too nice" (and how to fix it) When building LLM-as-a-judge pipelines, I've noticed a recurring failure mode that quietly destroys the reliability of evaluation metrics: the judge is simply too polite. Consider a real case. A customer complains about receiving red shoes instead of black ones. The bot responds warmly, apologizes, and offers a 10% discount coupon - while completely ignoring

21 Jul 2026, 17:21 UTC52 viewsread 8 August 2026

Why I Enjoy Working as an ML Evaluation Engineer After 20 Years of Experience in QA A lot of people ask how I feel in my new role as an ML evaluation engineer after 20 years in QA. Here is why I really like it: The shift forced me to abandon standard test automation patterns and rebuild my engineering toolkit around complex Python environments. Auxiliary tasks now include writing custom methods to manipulate remot

15 Jul 2026, 17:06 UTC61 viewsread 8 August 2026

⚡️Mentorpiece Vacy Index July 2026: Surprisingly, all the hiring this month is in the green zone Despite the annual summer lull, July unexpectedly saw a rise in the percentage of US companies with open job listings - even for roles like Manual QA. Here is what my custom-built IT hiring index reveals about hiring for Manual QA, Automation, and ML Evaluation engineers this month: READ MORE

8 Jul 2026, 15:28 UTC69 viewsread 8 August 2026

Will AI pass a code review? AI can do in an hour what used to take weeks. But would you stake your production environment on it without a Senior's review? 🚩 Probably not. "Working code" is no longer the benchmark. Maintainable code is. As we lean more heavily on AI agents, the core engineering question is shifting from "How do I write this?" to "How clean is this code?" Here is why an output that "just works" migh

29 Jun 2026, 17:01 UTC64 viewsread 8 August 2026

How a Short Word Can Turn Your AI Product into a Legal Nightmare In ML evaluation, particularly with the LLM-as-a-Judge approach, we frequently fall into the "halo effect" trap. When an AI model's response sounds authoritative and professional, the Judge automatically assigns it a high score, completely missing the actual semantic content. THE LAZY JUDGE TRAP Consider a tool summarizing legal contracts for non-law

24 Jun 2026, 17:21 UTC56 viewsread 8 August 2026

⚡️Mentorpiece Vacy Index June 2026: Classic Tech Role Hiring Drops, While Dedicated AI Remains Niche Unfortunately, the Tech Hiring Activity Index for June offers little good news. However, one compelling trend stands out: just two months ago, roughly 3x as many U.S. tech companies were hiring for classic manual QA compared to dedicated AI testing roles. That gap has already narrowed to 2:1. A closer look at the d

23 Jun 2026, 15:51 UTC47 viewsread 8 August 2026

Is the tech job market recovering or still collapsing? I built an AI-driven indicator to track the truth. The standard way to gauge tech hiring activity - counting active job postings - is fundamentally broken. The metric ignores three structural shifts happening simultaneously: • The Multiplier Collapse: When mid-size and large companies freeze hiring, they remove a multiplier effect from the market (one req at a

15 Jun 2026, 16:47 UTC62 viewsread 8 August 2026

Why It's Too Late to Learn Automation The idea for this post came to me during a regular meeting with my fellow mentors, SDETs from several international companies. We were discussing the future of the QA Automation market and reached some rather interesting conclusions. Let's remember why almost every manual tester once dreamed of breaking into automation: • More engaging tasks - essentially a hybrid of testing a

8 Jun 2026, 16:14 UTC59 views1 reactionsread 8 August 2026

How to Test AI Applications: The Grader's Ruler for LLM-as-a-judge When your LLM-as-a-Judge pipeline uses prompts like "rate this response as good, okay, or bad," you're essentially delegating your quality bar to whatever distribution dominated the judge's training data. A model trained on polite-but-unhelpful customer service text will happily score polite-but-unhelpful bot responses as "good." Consider a concrete

1

3 Jun 2026, 16:51 UTC68 viewsread 8 August 2026

How to Test AI Applications: The Gold Standard for LLM-as-a-judge Using LLM-as-a-judge without a gold standard is like asking a reviewer to grade an exam without the answer key - they'll fall back on their own memory, and in niche or professional domains, that memory hallucinates more than you'd expect. Consider a refund scenario: a customer complains about receiving the wrong color shoes. The bot apologizes and of

1 Jun 2026, 16:26 UTC57 viewsread 8 August 2026

How to Test AI Applications: Determinism vs. Probability Traditional QA, even when armed with AI tools, operates on a deterministic contract: if A + B is expected to be C, and the system returns anything other than C, it's a defect. Equivalence partitioning, pairwise testing, boundary analysis - all of it assumes reproducibility. Running the same test twice rarely adds information. That contract breaks the moment y

Showing the 12 most recent of 20 posts we hold for @be_tester. 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 — 675,587 of 1,603,376entries 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.

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.

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

“Let's become testers” (@be_tester), 206 subscribers as measured 24 August 2026. Telegram Register, tgregister.com/channel/be_tester.

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.