Channel
@clickhelp_technicalwritingblog
On this record: Growth · Engagement · Reactions · Posts · Cite this entry
1,268subscribers
-13 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001373408807 |
|---|---|
| Type | Channel |
| Username | @clickhelp_technicalwritingblog |
| Created | Between 1 March 2018 and 31 July 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 27 August 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 27 August 2026 |
| On Telegram | t.me/clickhelp_technicalwritingblog |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 27 Aug 2026, 01:03 | 1,268 | -3 |
| 24 Aug 2026, 09:03 | 1,271 | -2 |
| 17 Aug 2026, 14:55 | 1,273 | -3 |
| 14 Aug 2026, 07:06 | 1,276 | -2 |
| 10 Aug 2026, 16:51 | 1,278 | -3 |
| 7 Aug 2026, 22:46 | 1,281 | no change |
| 7 Aug 2026, 20:36 | 1,281 | first reading |
20 posts held, back to 15 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 | 20 (15 April 2026 – 6 August 2026) |
| Views total | 212 |
| Reactions total | 4 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 22:46 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.
33 reactions across 19 posts, in 3 distinct kinds. The most used accounts for 60.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 20 | 60.6% | |
| 🔥 | 11 | 33.3% | |
| ❤ | 2 | 6.06% |
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 33reactions 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 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.
New in ClickHelp this August 👇 Readers can now plug their own AI agent (Claude, Cursor, ChatGPT, Gemini) straight into your published docs — the Published Docs MCP Server reads your current content and answers only from what's actually there, not a guess or an outdated training snapshot. Search also got smarter: AI Overview answers questions right in the results, and you can keep asking follow-ups without leaving s…
👍1
Another one on content reuse — this time about snippet management. Short version: if the same warning or install step is copy-pasted across 20 topics, and every product change means editing all 20 by hand — that's the sign it should be a snippet instead. We go through the difference between variables (values: version number, email, company name) and snippets (blocks: procedures, warnings, legal text), what's actual…
❤2
Technical documentation has a text problem — not too little text, but text doing jobs it's not good at. Describing a multi-tab configuration wizard in prose takes 200 words and still leaves users guessing what the screen looks like. A 90-second screencast answers the same question and gets out of the way. The article breaks down when each format earns its place: - Screencasts work when the workflow is short, stabl…
🔥1
Claude writes well. But without access to your actual data, it's writing in a vacuum. MCP fixes that. One server, and any compatible client (Claude, Cursor, Windsurf) can reach your tools and data without a custom connector for each one. We wrote a step-by-step guide to building a local MCP server in Python: — project setup with uv — a working tool with @mcp.tool() in 30 lines — local testing with MCP Inspector bef…
🔥2
Docs aren't just read by people anymore. So we tested how AI handles them. We ran a quick test: gave ChatGPT a link to one of our documentation pages and asked it to summarize what it was about. It opened the link, read the page, and answered in seconds. No extra steps. Small result, but a good reminder — docs today get opened by more than just people. Search engines, AI assistants, link previews in Slack and Link…
👍1
DITA is powerful. But does your team actually need it? Topics, maps, reuse, single-source publishing — it all works. Just comes with an XML editor, a publishing engine, a CCMS, and months of setup. For a 3-person team with one product: almost always overkill. For 20+ authors, five languages, and regulatory requirements: almost always worth it. Between "Word doc chaos" and "full enterprise CCMS" there's a whole spe…
👍2
Ever nodded along when someone said "send me the BRD" — and then quietly Googled what that actually means? New article breaks down five core product documents: MRD, BRD, PRD, Tech Spec, and User Stories. What each one is, who writes it, when it appears — and how they all connect in a real SaaS product example. Plus: three scenarios at the end (startup, agile team, enterprise) so you know exactly which docs your sit…
👍1
📢 New in ClickHelp: multiple documentation sites, one portal If you publish docs for more than one product, brand, or audience, this one's for you. You can now run several independent, public-facing documentation sites from a single ClickHelp portal. Authors manage everything centrally — but each site appears as a fully separate website to readers, with its own custom domain, branding, home page, and set of publicat…
👍1🔥1
Regular expressions are one of the most powerful tools for technical writers — yet often underused. They can turn hours of manual editing into seconds: - bulk replace patterns, - clean markup, - extract data, - and automate repetitive documentation tasks. If you work with large docs, APIs, or structured content, regex is a skill worth mastering. 👉 Read the full guide here
👍1
📄 Google Docs works great when documentation is small. A few guides. A few contributors. A few updates. But things change when documentation grows across products, teams, languages, and publishing channels. Content gets duplicated, updates become harder to track, and maintaining consistency turns into a challenge. At that point, you start needing a more structured way to manage content. In our latest article, we c…
👍2
Something shifted in how technical writers think about video, and it's mostly because the editing part got a lot less painful. AI tools like Descript, Synthesia, HeyGen, and Guidde are making it possible to go from a rough screen recording to a polished, captioned, multilingual video, without a production team or a full day of editing. That changes what's realistic for documentation teams. Voiceovers, captions, ava…
👍2
AI agents are no longer just “search tools for documentation.” Something more interesting is happening: they’re starting to actually interact with documentation systems, not just pull answers out of them. MCP (Model Context Protocol) is what makes this possible. It gives AI agents a structured way to connect to a documentation portal — so they can use context, access information more intelligently, and work with th…
🔥2
Showing the 12 most recent of 20 posts we hold for @clickhelp_technicalwritingblog. 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.
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 27 August 2026 — this entry's latest reading, not the date you are reading this.
“Technical Writing Blog” (@clickhelp_technicalwritingblog), 1,268 subscribers as measured 27 August 2026. Telegram Register, tgregister.com/channel/clickhelp_technicalwritingblog.
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.