Channel
@RobeDiNik
On this record: Growth · Engagement · Posts · Cite this entry
396subscribers
-1 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001254906097 |
|---|---|
| Type | Channel |
| Username | @RobeDiNik |
| Created | 26 December 2018 — measured — cross-checked against a third-party dataset (TGDataset) |
| First recorded | 6 August 2026 |
| Last confirmed live | 24 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 24 August 2026 |
| On Telegram | t.me/RobeDiNik |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 24 Aug 2026, 00:23 | 396 | -1 |
| 6 Aug 2026, 20:33 | 397 | no change |
| 6 Aug 2026, 20:23 | 397 | first reading |
12 posts held, back to 2 March 2025 — 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.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 12 posts for this entry, the most recent from 1 November 2025. An engagement rate over an empty window would be a number about nothing.
I've made a simulator for a betatron. an old type of particle accelerator that uses the electric field induced by a strong changing magnetic field to accelerate electrons to over 6MeV. the line is the trajectory of an electron and its color is the energy. the crazy thing is that you could potentially build this in your garage as it is cheap and easy to make (probably ~3000€ budget) and it would start pumping out ex…
my rtx4090 has been grinding for days, I think I'll buy a second one soon. the two charts show train and validation losses for two different models learning schematics. the purple one is attention based and generalizes way better. attention is all you need after all.
schematic file parser and schematic tokenizer to turn schematics into tensors tokenizers and embedding tables are the building blocks to bridge discrete data with their continuous representations.
I've put together 150000 schematics and then MANUALLY rated ALL of them (based on how much I liked them). I kept 128875 of them, probably the biggest curated schematic dataset ever created.
ho fatto un coso che compila xc32 (il compilatore per i pic32) versione PRO gratis https://github.com/nikisalli/Free_XC32_PRO_Compiler/ fanculo Microchip Technology Inc.
altra roba interessante: dalla matrice di correlazione tra dof pos e output action emerge che la posizione della tibia dipende molto da quella del femore più che da quella della tibia stessa, probabilmente la NN ha imparato una sorta di cinematica inversa dove decide la posizione del femore e la posizione della tibia viene calcolata da quella
ho fatto un pò di analisi sulla rete neurale del robot e sono emerse un po di robe interessanti 1) i primi layer fanno più attenzione alle velocità delle zampe e la posizione del target mentre gli ultimi sulla velocità del corpo del robot 2) il robot usa molto poco le feature del lidar probabilmente perchè la maggior parte dei punti sono lontani dalle zampe il che è uno spreco considerando che il lidar usa 25 delle…
si vede bene nella velocità come stalla mentre passa per il centro e dopo gli viene data un'altra spinta da dietro il Q è circa 1.1, che è ottimo perché devono esserci al massimo due semionde significative, una positiva e una negativa, altrimenti la terza semionda sarebbe positiva e tirerebbe indietro il proiettile
primo coilgun risonante
and obviously here's the coilgun's simulation and visualization the simulator and everything were written in c++ from scratch (no dependencies) as a pybind11 module to be fed to scipy.optimize to find the optimal coilgun design
I took the solenoid magnetic field formulation that reduces the computation to very efficient elliptic integrals from here: http://users.df.uba.ar/zanellaj/ft1_2011_1c/material_auxiliar/campo_solenoide_exacto_Derby.pdf
I've made a very small axis aligned solenoid magnetic simulator to optimize the design of a single stage elctromagnetic gun (commonly known as coilgun) in this run I've optimized the design of a passive single stage coilgun (timings are driven by the RLC circuit formed by the coil, parasitic resistance and capacitor) it reached an efficiency of 17% with the constraint of starting with 1kJ of energy and the peak out…
Showing the 12 most recent of 12 posts we hold for @RobeDiNik. 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 24 August 2026 — this entry's latest reading, not the date you are reading this.
“Robe che fa nik” (@RobeDiNik), 396 subscribers as measured 24 August 2026. Telegram Register, tgregister.com/channel/RobeDiNik.
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.