Channel
@techleadbits
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
500subscribers
+0 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1002055893363 |
|---|---|
| Type | Channel |
| Username | @techleadbits |
| Description | About software development with common sense. Thoughts, tips and useful resources on technical leadership, architecture and engineering practices. Author: @nelia_loginova |
| Created | Between 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 6 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/techleadbits |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 6 Aug 2026, 18:46 | 500 | no change |
| 6 Aug 2026, 02:18 | 500 | first reading |
19 posts held, back to 29 April 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.
| Window | Rolling 30 days · latest post in window 6 August 2026 |
|---|---|
| Posts held | 19 (29 April 2026 – 6 August 2026) |
| Views total | 726 |
| Reactions total | 29 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 6 Aug 2026, 18:46 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.
Lifetime counters from Telegram’s own channel header, read 6 August 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.
108 reactions across 19 posts, in 3 distinct kinds. The most used accounts for 38.0% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 41 | 38.0% | |
| 🔥 | 41 | 38.0% | |
| ❤ | 26 | 24.1% |
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 108reactions 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 29 April 2026 to 6 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.
AI Adoption: Measuring Support Continuing the previous post, let's look at how AI adoption can be measured for support teams. The principles are exactly the same: cost, quality, and time. Here are the metrics I'd track: 🔸 Team Budget. Same as for development teams: the cost of the team in $, mandays, or FTEs. 🔸 AI Cost ($). How much does the team spend on AI, including autonomous agents if you're using them. 🔸 Inco…
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AI Adoption: Measuring Feature Development Let's assume your team has already completed AI adoption and now management expects to see positive business results. The good news is that AI doesn't magically change your business metrics. You don't need to invent new KPIs. You just need to track how your existing ones change (or finally start collecting them). I suggest focusing on quality metrics first, then gradually …
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AI Adoption: What to Measure First In the previous post, we looked at how DORA recommends measuring the ROI of AI adoption. But what if you don't know the revenue generated by your features or other business metrics? Yet you're still expected to show the effectiveness of AI adoption. Let's bring the DORA approach down to the engineering team level. I would split AI adoption into two phases: AI adoption itself and …
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J-curve adoption representation from DORA report. #ai #engineering #ai4sdlc
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ROI of AI-Assisted Development One of the biggest questions around AI adoption is what business value does it actually bring? I'm currently introducing AI into the SDLC across my teams, so measuring its real impact is something I'm really interested in. Back in February 2026, DORA published a dedicated report on this topic: ROI of AI-assisted Software Development report. Key takeaways: 🔸 Measure your baseline fir…
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Who Is Responsible for AI-Generated Code? "This thing works terribly." "Well... it was vibe coded." "Ah, that explains it." I hear conversations like this more and more often. The funny part is that as soon as someone says a feature was "vibe coded," expectations immediately drop. At least among developers 😃. But that raises an interesting question: who is actually responsible for that feature being released? The a…
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Akrites Project Over the past year, we've seen how much the industry depends on small open source projects. In many cases, software used by thousands of companies is maintained by one or two people working on it in their free time. The release of the Mythos and Fable models made the situation even worse. They demonstrated how many vulnerabilities AI can find across thousand of projects, putting the whole enterprise…
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A Few Words About Context New major model releases regularly promise bigger context windows. Sounds great until you realize it's mostly marketing. A bigger context window doesn't mean better results. It often means more data, more noise, and more AI slop. According to multiple studies, models effectively use only about 30–50% of their available context. For example, a model with a 200K-token context window may alre…
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"AI won't take your job. Someone using AI will." This quote caught my attention and made me watch A Leader’s Guide to Advanced Team Structures in an Agentic World from the recent AWS Summit Sydney. It's a very sobering talk on the current state of the industry, AI adoption, and the future of engineering roles. The central question of the talk is: "How should we build teams to work in this new AI world?" To answer …
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Project Hail Mary Technical books and articles are great, but sometimes my brain needs a break. Especially now, when AI is generating more and more new things to learn every day. One of my favorite ways to recharge is reading fiction, and I recently finished the very popular Project Hail Mary by Andy Weir. I'm not a big sci-fi fan, but I definitely enjoyed this book. Thanks to the recent movie adaptation, the story…
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How Anthropic Writes Skills Last week Anthropic published lessons learnt of how they build agent skills internally. It's quite interesting to read recommendations from the company that introduced the concept in the first place. Key ideas: 🔸 Don't be obvious. Model already knows how to code. A skill should provide instructions that change default agent behavior, not repeat the data the model was trained on. 🔸 Build …
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Agent Readiness Framework A few weeks ago I wrote that adopting coding agents requires strong engineering practices. Test stability, linting, documentation, security controls matter much more than a particular harness or model. Agent Readiness framework is an attempt to formalize these criteria for a particular repository and define how much autonomy can be safely delegated to agents. The framework evaluates repos…
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Showing the 12 most recent of 19 posts we hold for @techleadbits. 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 — 658,241 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.
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.
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.
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.
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 6 August 2026 — this entry's latest reading, not the date you are reading this.
“TechLead Bits” (@techleadbits), 500 subscribers as measured 6 August 2026. Telegram Register, tgregister.com/channel/techleadbits.
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.