Channel
@gradientdude
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
2,632subscribers
-1 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001312118296 |
|---|---|
| Type | Channel |
| Username | @gradientdude |
| Created | 1 September 2018 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/gradientdude |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 11:14 | 2,632 | -3 |
| 10 Aug 2026, 06:06 | 2,635 | +2 |
| 7 Aug 2026, 00:00 | 2,633 | no change |
| 6 Aug 2026, 14:46 | 2,633 | first reading |
15 posts held, back to 1 May 2022 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof 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 15 posts for this entry, the most recent from 9 April 2024. An engagement rate over an empty window would be a number about nothing.
Measured directly from 5 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.
379 reactions across 15 posts, in 10 distinct kinds. The most used accounts for 55.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🔥 | 211 | 55.7% | |
| 👍 | 85 | 22.4% | |
| ❤ | 58 | 15.3% | |
| 🎉 | 11 | 2.90% | |
| 👏 | 4 | 1.06% | |
| 🤯 | 4 | 1.06% | |
| 😍 | 3 | 0.792% | |
| 🏆 | 1 | 0.264% | |
| 🤩 | 1 | 0.264% | |
| 🥰 | 1 | 0.264% |
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 15 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 379reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 15 most recent posts we hold, published 1 May 2022 to 9 April 2024, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
🎸 Suno v3 prompt engineering guide Go to the app homepage, create tab. There's the simple mode (which will generate a song and lyrics, but without the tricks below), and the custom mode wiht more contorl. We tap the second, of course. Now we see a prompt and lyrics window. 1. Workflow. First generation is a max of 2 minutes. Usually, it can include an intro, verse, and chorus (maybe more if you have a high tempo). …
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Suno v3 - The best txt2music model Recently released Suno v3 is the absolute best txt2music and txt2audio model ever. Suno v3 is capable of generating actually interesting 2-minute songs in one go (or even potentially indefinitely long ones with the continue function). And yes, precisely songs! Because it also generates vocals, which have been greatly upgraded in the last version. So to put it in perspective, Suno v…
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The paper also shows that SD3-Turbo is better than Midjourney 6 in both image quality and prompt alignment, which is surprising 🫥. Looking forward to the weights release to do a reality check! @gradientdude
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⚡️SD3-Turbo: Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation Following Stable Diffusion 3, my ex-colleagues have published a preprint on SD3 distillation using 4-step, while maintaining quality. The new method – Latent Adversarial Diffusion Distillation (LADD), which is similar to ADD (see post about it in @ai_newz), but with a number of differences: ️↪️ Both teacher and student…
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I'm getting back to juicy posts in english! @gradientdude
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Staff Research Scientist: Personal Update I have some exciting news that I'd like to share with you! On Monday, I was promoted to E6, which means I am now a Staff Research Scientist at Meta GenAI. This was made possible thanks to the significant impact and scope of a Generative AI project that I proposed, led, and completed last year. The project is not yet public, so I can't share details about it right now. Befo…
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Today I will be presenting our CVPR2023 poster "Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model". Learn how to synthesize full body motion based on 3 known points only (head and wrists)! ❱❱ Detailed post about the paper. Come to chat with me today at 10:30-12:30 PDT, poster #46 if you are at CVPR. @gradientdude
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Demo of our Avatars Grow Legs model that synthesizes the full 3D body motion based on sparse tracking inputs from the head and the wrists. More details are in the paper. @gradientdude
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🦿Avatars Grow Legs I'm thrilled to share with you my latest research paper (CVPR 2023)! This was a joint effort with my intern at Meta Reality Labs before our team transitioned to GenAI. Our innovative method, dubbed Avatars Grow Legs (AGRoL), aims to control a 3D avatar's entire body in VR without the need for extra sensors. Typically in VR, your interaction is limited to a headset and two handheld joysticks, leav…
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I will be presenting our VisCo Grids paper at NeurIPS tomorrow at 11:00-13:00 in Hall J. Feel free to come to poster #527 to learn more details if you are attending the conference! @gradientdude
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VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids The goal of this work it show that network-free grid-based implicit representations can achieve INR (Implicit Neural Representation)-level 3D reconstructions when incorporating suitable priors. VisCo Grids is a grid-based surface reconstruction algorithm that incorporates well-defined geometric priors: Viscosity and Coarea. The viscosity loss, i…
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Image Inpainting: Partial Convolution vs Gated Convolution Let’s talk about some essential components of the image inpainting networks - convolutions. #fundamentals It is common in image inpainting model to feed a corrupted image (with some parts masked out) to the generator network. But we don’t want the network layers to rely on empty regions when features are computed. There is a straightforward solutions to t…
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Showing the 12 most recent of 15 posts we hold for @gradientdude. 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 — 459,816 of 1,176,251entries 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.
Names
Channels on the register whose handles appear in this channel's posts.
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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“Gradient Dude” (@gradientdude), 2,632 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/gradientdude.
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.