Channel
@wixeng
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
367subscribers
-6 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001157698401 |
|---|---|
| Type | Channel |
| Username | @wixeng |
| Created | 8 June 2021 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 8 August 2026 |
| Last confirmed live | 1 November 2026 |
| Measurements held | 9 |
| Confirmed unchanged | 1 time, most recently 1 November 2026 |
| On Telegram | t.me/wixeng |
Technology — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 22 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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 the other topics.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 1 Nov 2026, 07:38 | 367 | -1 |
| 25 Oct 2026, 04:37 | 368 | +2 |
| 17 Oct 2026, 06:56 | 366 | -1 |
| 10 Oct 2026, 11:17 | 367 | -2 |
| 17 Sept 2026, 20:37 | 369 | -1 |
| 30 Aug 2026, 07:07 | 370 | -2 |
| 22 Aug 2026, 23:55 | 372 | -1 |
| 8 Aug 2026, 06:18 | 373 | no change |
| 7 Aug 2026, 14:40 | 373 | first reading |
20 posts held, back to 17 June 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 page 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 22 July 2026. An engagement rate over an empty window would be a number about nothing.
Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
74 reactions across 19 posts, in 7 distinct kinds. The most used accounts for 39.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ⚡ | 29 | 39.2% | |
| 💯 | 17 | 23.0% | |
| 👍 | 15 | 20.3% | |
| 🔥 | 5 | 6.76% | |
| 🏆 | 4 | 5.41% | |
| 🦄 | 3 | 4.05% | |
| 🤩 | 1 | 1.35% |
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 74 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 17 June 2026 to 22 July 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.
What happens when AI starts generating, evolving, and deploying code on behalf of users - at Wix scale? The existing developer platform was no longer enough. In this talk, Gilad Bison and Dor Chaouat will show how one ambitious AI project pushed Wix to rethink the entire lifecycle of cloud-based development. Through the architecture of Dev Machine, Wix Hosting, Code Store, IDE as a Platform, and Wix CLI, you’ll le…
⚡1💯1
In our latest Wix Engineering newsletter, you’ll learn how AI agents can catch and fix their own mistakes, how TDD can guide AI-written software, why larger context windows may reduce accuracy and increase costs, and how agents reason across code, logs, databases, and documentation. You’ll also explore the hidden economics of AI coding tools and the engineering skills that matter most when AI writes the code. Read h…
⚡2👍1🔥1
Production issues. Technical debt. Migrations. New features. When everything competes for 👀 attention, developers lose focus, make more mistakes, and burn out. In this great talk, Dalia Simons presents a simple weekly rotation between Guardians, Renovators, and Creators - that reduces context switching and protects deep work. Watch to learn how clearer ownership can help teams stay focused, move faster, and ship b…
💯2👍1
If AI writes the code, what’s left for engineers? 🤔 That shift changes the job. The real advantage is no longer just writing code, but asking better questions, understanding tradeoffs, owning the outcome, and developing the taste to know what should be built. Tom Enden’s article explores the engineering skills that matter when code is no longer the hardest part. Don't miss this one: https://www.wix.engineering/pos…
⚡2👍2💯1
The more rules you add to an AI agent, the more fragile 🚩 it can become. Prompts get longer. Context gets heavier. And the agent still ignores constraints, repeats mistakes, or produces output that looks impressive - until it hits production. Yoav Abrahami proposes a different architecture: Feedback-Driven Agents. Instead of expecting perfection on the first try, deterministic validators catch structural errors, i…
⚡2🏆1💯1
Real incidents. Real lessons. A stronger 💪 production culture. In this great session "How to Win in Production" by Guy Nahum, we break down the failure patterns behind real incidents - and the practices that helped us at Wix reduce noise, improve on-call, and learn without blame. You'll learn practical rules for better production - and a clearer view of where AI can responsibly help. Watch: https://youtu.be/CaNuUu…
⚡2👍1💯1
What can a classic TDD book 📙 teach us about building software with AI? Growing Object-Oriented Software, Guided by Tests by Steve Freeman and Nat Pryce was written long before coding agents became part of everyday development - but its core ideas may be more relevant than ever. The real challenge with AI-written software is not generating code. It’s knowing whether the code reflects the right intent, handles the r…
⚡2🔥2🤩1
Channel photo updated
Your AI writes code ⚡ faster than ever. So why is shipping still the 🚩 hardest part? Because AI doesn't fail at writing code. It fails at understanding context and proving it's correct. At our next meetup (TLV) we'll tackle the two biggest engineering challenges of AI-first development: 🧠 How do you give AI the context your team has - but your repository doesn't? ⚙️ How do you redesign CI - when AI agents generate…
💯2
How do you build a remote browser platform that scales to 🚀 massive parallel execution - without becoming the bottleneck itself? That's exactly the challenge David Gahnassia and his team tackled while building the remote browser architecture behind Sled3, our next-generation E2E testing framework. In this great talk, you'll get an inside look at the production architecture - from Kubernetes and the proxy layer to s…
⚡4👍1
Why do AI agents 🚩 fail in production? Because the answer rarely lives in a single repository. In his new article, Guy Bary shares how we built Wix Octocode - an AI research engine that connects code, logs, databases, documentation, and organizational knowledge to uncover the real root cause before taking action. You'll learn why search isn't enough for modern AI agents, how multi-agent architectures improve resea…
⚡2
AI coding breaks 🚩 down not because of bad prompts, but because it forgets your project's decisions. Yoav Abrahami's Design-Log Methodology fixes this by giving AI a permanent, versioned history of architectural choices - keeping it aligned, reducing drift, and turning it into a true design partner instead of just a code generator. Watch his great session "Stop Prompting. Start Logging: Breaking the Context Wall in…
⚡2👍1
Showing the 12 most recent of 20 posts we hold for @wixeng. 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.
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.
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 1 November 2026 — this entry's latest reading, not the date you are reading this.
“Wix Engineering” (@wixeng), 367 subscribers as measured 1 November 2026. Telegram Register, tgregister.com/channel/wixeng.
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.