Channel
@AIphysical
On this record: Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
16subscribers
+2 since we began measuring on 9 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1003625788861 |
|---|---|
| Type | Channel |
| Username | @AIphysical |
| Created | Between 1 December 2025 and 31 May 2026— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 12 August 2026 |
| Last confirmed live | 30 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 30 August 2026 |
| On Telegram | t.me/AIphysical |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 30 Aug 2026, 03:28 | 16 | +1 |
| 24 Aug 2026, 03:38 | 15 | +1 |
| 12 Aug 2026, 02:02 | 14 | no change |
| 9 Aug 2026, 17:30 | 14 | first reading |
16 posts held, back to 29 July 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. It is computed over the 1 of 12 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 3 August 2026 |
|---|---|
| Posts held | 16 (29 July 2026 – 3 August 2026) |
| Views total | 98 |
| Reactions total | 0 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 02:02 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.
Measured directly from 8 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.
At Mirador 3D, we are building a Unity plugin for large Gaussian Splatting scenes using PlayCanvas .SSOG format, designed to stream millions of splats across multiple platforms . We have also integrated Meta XR hand tracking and gesture-based locomotion on standalone Meta Quest 3, while working within a limited active-splat budget. Our next steps are to validate performance on Meta Quest 3, further optimize streami…
Finally finished a complete implementation of Gaussian Splats that behave like a 3D mesh in Unity. They can cast and receive shadows. Receive or cast lighting. Integrate completely with any mesh in scene. They can even be raytraced. Cast reflections. Use Ambient occlusion. They even support complex shader effects by using #Amplify shaders. They will be integrated into my new Deckard Render Studio. They can be enhanc…
Showing off some more dexterous skills with grippers. 🦾 Watch Gemini Robotics 2 x Franka Robotics 's FR3 Duo in action. Learn more about the release here: https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/ Source: https://www.linkedin.com/posts/nicolas-heess-5600922b8_showing-off-some-more-dexterous-skills-with-ugcPost-7489709302006390784-m8Dt
Source: https://www.linkedin.com/posts/peterokiokpa_xiaomi-technology-introduces-robotics-1-foundation-ugcPost-7483542671874060288-UXtH
Xiaomi Technology Introduces Robotics-1 Foundation Model Trained on 100,000+ Hours of Real-World Robot Data. Xiaomi has unveiled Xiaomi-Robotics-1 (XR-1), a new vision-language-action (VLA) foundation model designed to bring large-scale AI scaling laws to robotics. The model was pre-trained on more than 100,000 hours of real-world manipulation data spanning over 1,700 different environments, including homes, commer…
Photo, posted without a caption
Teleoperation is the most underrated part of robotics. Everyone talks about autonomy. Reality? A human is still behind a surprising number of robots. Teleop does two things: • It keeps robots running when autonomy fails. • It creates the data that trains the next generation of AI. The wild part? There's still no standard way to do it. VR. Leader-follower arms. Exoskeletons. Handheld grippers. Every setup records…
https://fsstudio.com/teleoperation-data-is-the-1-fuel-that-teaches-robots-the-work/
Source: https://www.linkedin.com/posts/peterokiokpa_telekinesis-is-bringing-reinforcement-learning-ugcPost-7482719448248967168-4tYA
Telekinesis Is Bringing Reinforcement Learning to Its Agentic OS. The Suman Pal led physical AI startup has announced that Reinforcement Learning (RL) is coming to its Agentic OS, with an early demonstration showing a humanoid robot learning robust locomotion and stair climbing using Proximal Policy Optimization (PPO). The upcoming RL stack will include support for PPO, distributed training across multiple simulati…
Source: https://www.linkedin.com/posts/waleedshaarani_im-excited-to-announce-we-are-coming-out-ugcPost-7488643168045846529-lrd3
I’m excited to announce we are coming out of stealth today! I've been working on this for the past couple of months quietly and its been very hard to contain my excitement around it. I’d like to take a moment to introduce Creativ AI, the SQL Layer for Physical and Visual AI. Here’s the problem we couldn’t stop thinking about: The world has more than a billion cameras out there and now it’s exponentially growing w…
Showing the 12 most recent of 16 posts we hold for @AIphysical. 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 — 369,877 of 1,627,068entries 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.
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 30 August 2026 — this entry's latest reading, not the date you are reading this.
“Physical AI” (@AIphysical), 16 subscribers as measured 30 August 2026. Telegram Register, tgregister.com/channel/AIphysical.
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.