
Joachim
335 posts

Joachim
@crtmaster
Data scientist & A.I. researcher | CRT / console / computer collector | fan of old and new video games
Switzerland Katılım Mart 2020
363 Takip Edilen77 Takipçiler

@iH8c05m4og9I6oh Just build or buy an MZ80K SD, see this github repo: github.com/yanataka60/MZ8…
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@RCAVictorCo Great! Yes, I am pretty sure we have at least one system in the collection that uses bubble memory.
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@crtmaster I'll let you know later. Do you have a bubble system motherboard?
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@debugjunkie Do you still have the PDFs? This way I would not have to scan them manually as well.
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@gamingretroUK Was that UV frame on the real Vectrex running with the normal 12V power supply or something weaker or a dimmer? The vectors seem really bright compared to the overlay glow.
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@sirotenko_m @fchollet I think I used this actually in 2012 for a simple CNN!
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@fchollet Here's another one, even earlier implementation of CNN on CUDA (with matlab wrapper) share.google/N470SOcJY3ZQAl…
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@bradtheilman Then you should look into the crtgaming community and/or watch this cool new video about the recently re-surfaced biggest CRT ever made: youtu.be/JfZxOuc9Qwk

YouTube
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@stunty999 @Voultar Some retrocomputing restoration channels suggest (after retrobrighting) using Aerospace Protectant 303 followed by coating with Renaissance wax polish.
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@Voultar every single retrobright i saw, return to yellow in a year or two unfortunatly.
Is this something different?
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@FremenHlide Hi. Will have to see if I have time over the holidays for this. I did not change anything hardware-wise compared to the original build plan, hence maybe some optimization in terms of voltage level would be advised.
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@crtmaster Hi, do you think you can add you PR ? I'm also curious how you handle the fact MOTOR pin is 12V unlike MZ-700 5V.
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@retroaccess What about non-EU countries like Switzerland, will you still ship to them?
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@xAD_nIGHTFALL A good reminder! Mine that I got from your last batch at the time also still waits to be installed in my intellivision 😅
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Mattel Intellivision with ZOE RGB rev 2.0 | nIGHTFALL Blog / RetroComputerMania.com nightfallcrew.com/01/11/2024/mat…

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@Prophesee_ai You could add this paper about text2events: ieeexplore.ieee.org/abstract/docum…
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💥Explore our NEW Research Library, a fast-growing database of 100+ academic papers. Get inspired today by top research advancing computer vision with Event-based Vision!
#eventbasedcamera #metavision #metavisiontechnologies #research #eventbasedvision #particlesizemonitoring
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Not every foundation model needs to be gigantic. We trained a 1.5M-parameter neural network to control the body of a humanoid robot. It takes a lot of subconscious processing for us humans to walk, maintain balance, and maneuver our arms and legs into desired positions. We capture this “subconsciousness” in HOVER, a single model that learns how to coordinate the motors of a humanoid robot to support locomotion and manipulation.
We trained HOVER in NVIDIA Isaac, a GPU-powered simulation suite that accelerates physics by 10,000x faster than real time. To put the number in perspective, the robots undergo 1 year of intense training in a virtual “dojo”, but take only ~50 minutes of wall clock time on one GPU card. The neural net then transfers zero-shot to the real world without finetuning.
HOVER can be *prompted* for various types of high-level motion instructions that we call “control modes”. To name a few:
- Head and hand poses: can be captured by XR devices like Apple Vision Pro.
- Whole-body poses: via MoCap or RGB camera.
- Whole-body joint angles: Exoskeleton.
- Root velocity command: Joysticks.
What HOVER enables:
- A unified interface for us to control the robot using whichever input devices are convenient at hand.
- An easier way to collect whole-body teleoperation data for training.
- An upstream Vision-Language-Action model to provide motion instructions, which HOVER translates to low-level motor signals at high frequency.
HOVER supports any humanoid that can be simulated in Isaac. Bring your own robot, and watch it come to life!
It's a big teamwork from NVIDIA GEAR Lab and collaborators: 🧵
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@RetroTechDreams This was THE program to use for so much cover art of techo & trance releases in the 90s.
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@sainingxie Very interesting work! Do you expect these methods will give an extra speed up to training of text-to-video diffusion models? Given they are especially compute hungry but often leverage pretrained image encoder parts.
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Representation matters.
Representation matters.
Representation matters, even for generative models.
We might've been training our diffusion models the wrong way this whole time. Meet REPA: Training Diffusion Transformers is easier than you think! sihyun.me/REPA/(🧵1/n)

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After getting myself confused one too many times on Sony PVM speaker model numbers I decided to write up a full guide on them: crtdatabase.com/faq/sony-crt-p…
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@CharlesMartinet Hard to overstate what fantastic change in terms of 3D control this game provided 28 years ago. Thank you @CharlesMartinet for lending your voice to this great Mario installment!
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@Consoles4You Great news! Will you also sell the analog output add-on board?
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@Lord_Arse Immersive story, and such cool gravity shifts and scale changes. 😎
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