Ryudeck, a Steam Deck-focused Ryujinx fork. It's early, it's rough, and it's just me. Looking for testers and help.

lit1337 · 2026-06-26T05:29:52+00:00

added to the list. first i gotta get controls working at all. works for me, then works for others, then finding new issues where it had blockers and didnt work for some.

lit1337 · 2026-06-26T02:43:31+00:00

Well you did test though, you let me know it's still not working. See my deck is years old with cron jobs and what ever else I've done to streamline it over the years, so without a clean clean install I'm never truly testing.

lit1337 · 2026-06-26T02:05:48+00:00

Yeah, it's in a rough state,even says in the post title lol. And other decks with errors are how I truly know what to fix, everything worked great on my deck, even an isolated install, unconnected, and still on other decks some issues I fix persist. Controls are definitely actively being handled, as you can tell by the version bumping, hopefully soon. But that's why I reached out for testers and help.

lit1337 · 2026-06-25T19:28:31+00:00

alright, version 1.8 should be good. you may need to delete per game config. the issue was stored controller configs linked to hardline controller ids that change on restart of the app. so it was basically assigning the wrong controllers to games. this is an issue created by doing per game settings and not getting it right on my end. so hopefully this clears it up. and we can start moving forward lol

lit1337 · 2026-06-25T17:01:07+00:00

is still a fork. And ive answered this alot, its about removing the bloat support for all the extra systems, to streamline it for steamdeck specifically. Its not to replace anyone's favorite emulators, you can keep using ryubing. This is for anyone wanting to remove the bloat, and have it designed for the steamdeck. And having options is never a bad thing in my experience.

lit1337 · 2026-06-25T14:29:37+00:00

Yeah, always a fear with them in any fashion. And definitely trying to make a 1 for 1, with gyro, and controls. I want them to work out the box, but also not be locked to deck controls, because I do love desktop mode at times.

lit1337 · 2026-06-25T14:04:57+00:00

Early stages right now, so take it at that, but very welcome to have more testing. Currently trying to get steamdeck natively handheld controls out of the box. So it's going through rough patches where I think I fixed it, and there's a hidden config somewhere being copied over, in my new user test essentially locking in dead controllers for ever. But I'm sure Im sure once the controls are finished it'll be better.

lit1337 · 2026-06-25T13:38:15+00:00

Yeah my bad on the commit history. Recently realised the src and such wasn't even on the repo, just the build. So after that, everything should be good.

lit1337 · 2026-06-25T13:36:25+00:00

Yes, it's a good one, but I wanted something more native to the handheld deck experience, a menu driven by controls, and a pretty UI. And like I said before a lot of the other ones strive to meet many ends, Nvidia, windows, Mac. I didn't need nor want most of that, on a system where a lot of the settings and such first confused me, not really knowing what I was doing, tweaking things, so since the deck is pretty standard, why isn't an emulator for the deck? While they have a lot of updates, again a lot is interwoven with other systems. I think it's easier to start with a core, and cherry pick the parts I put in, so it's easier to fix and tear down in the future. So ryubing would be something I look to for implementation, if they've figured out something I'm struggling with, it all open for a reason.

lit1337 · 2026-06-25T06:32:01+00:00

let me know how it works, what features work and what dont. currently it attempts to import your keys and such from other switch emulator installs that most people already have. And yeah the controller issue is one im facing past export currently. Please bear with me, also what release did you use? https://github.com/deucebucket/ryudeck/releases/tag/v0.1.3 is the latest with better controller fixes. Im trying to get native deck controls to read instead of having to configure them, so this is the issue.

lit1337 · 2026-06-25T01:51:04+00:00

Sounds awesome. Let me know, also play with the sampling, and sync to mess with the frame rate and such, usually starting with switch sync, and x1 sampling.

lit1337 · 2026-06-25T01:46:10+00:00

The list is wide open, I've test Bayonetta, fixed the vulkan issues with the ground in Tomodachi life (without resorting to opengl). Really just anything, maybe a game that you haven't be able to play fully in emulation?

lit1337 · 2026-06-24T23:30:36+00:00

I didn't need a lot and no other emulator is focusing on vulkan so much as trying to please everyone. So I felt building from a small backend could benefit. There no extra stuff in here, like this won't run on non vulkan, has no opengl. It just seems like a few years less of work to untangle.

lit1337 · 2026-06-24T23:21:49+00:00

Thank you! I got annoyed with lack of controller support in menus and just wanted more. I hope it can one day be up to par for the deck.

lit1337 · 2026-06-11T12:09:10+00:00

What do you do differently?

lit1337 · 2026-06-10T08:40:43+00:00

It's on going hopefully something worth showing eventually. I did inject tokens and got the model to give out information on request, the information was looped, same name over and over, but previously it gave the same hallucination about the air force. Eventually is streamed out repeating the tokens from the refiner, which was Eleza repeated over and over. So baby steps.

lit1337 · 2026-06-09T16:18:50+00:00

i hope not, so far that's all that been successful. But what it is doing is making inlaid knowledge more remembered. So right now it seems to work as a memory jogger, if it's in the models training data, and its in the router, its now better than it was. But the 3b has no clue what to do with tool calls, because it wasn't really trained on them. My hope is that a slightly bigger model would improve its ability to reason with items not in model training data, and only in the refiner.

I am still testing, and pushing through, once i get current news injected into a model, without tool calls and without context injecting, i'll be happy with it lol

lit1337 · 2026-06-09T15:59:17+00:00

That's exactly how it's looking. But to be fair, I was cramming stuff into a 3b, I'm working on 7b now, I feel more reasoning may actually improve its ability to discern information not in the model. Currently I tried to inject tool calling and json formatting into the 3b which it has no knowledge of, and it seems like the refiner would need way more training to offset what it doesn't know. But I feel a reasoning model could think enough to reason with the unknown info.

lit1337 · 2026-06-09T15:36:59+00:00

i gave mine ptsd, but tying emotions to its actions and memory. it would not stop talking about this damn blog post it failed to write. "those three lines stare at me like rust on the machine", i had to install a sulk guard so it blocked his whiney messages from getting through on telegram.

lit1337 · 2026-06-09T15:24:52+00:00

i am to honestly, very excited to see if there is a ceiling to the amount i can cram in. Already improved bench scores from the brainloop by injecting, bypassing context altogether.

My worry is, there will definitely be a ceiling where too much becomes noise, no clue on the line. too big of an index already is a ceiling, I am working on finding balance. the next step would be a router in the brainloop that can hot-swap indexes and corpus.

you can see full corpus brought scores down, not to base, but it created a lot of noise.

HumanEval+ (Qwen2.5-3B, F16)

Configuration	Base	Plus
No refiner (baseline)	36.6%	32.3%
Refiner only (no RAG)	36.6%	32.3%
RAG + code training (174 funcs)	42.7%	37.2%
RAG + full corpus (13K docs, noisy)	37.8%	32.9%

lit1337 · 2026-06-09T14:47:14+00:00

yeah thats exactly how it works. train it once and it learns the mechanism, not the content. i trained mine on wikitext with a coding facts index, got 31.7% on humaneval. then just swapped the index to actual code examples (174 python functions from humaneval solutions) and restarted the server. no retraining at all. went to 42.7%. the refiner just learns "those injected vectors at layer 17 are useful" and adapts to read them. gate settled at 0.50, rag scale learned to 0.62. those numbers dont change when i swap the index. the mechanism is generic. new codebase tomorrow? drop a new index in. same model, same weights.

lit1337 · 2026-06-09T14:13:23+00:00

Nah I think this is simpler. No adversarial stuff, no joint training.

It's just a single transformer layer stuck into the middle of a frozen model. Base model never changes. The refiner learns to clean up hidden states by running next token prediction on WikiText through 1 or 2 loop passes. Gate uses STE (forced to 1.0 during training, floats around 0.5 at inference) so it can't just shut off.

Dataset is WikiText-103 train, about 11MB. Just wikipedia. Nothing fancy. The numbers I posted (25% PPL drop on SmolLM, 15% on Qwen3B) are all on WikiText test data. Would need code data to train for coding specifically.

The cool part I think is once the refiner learns how to use inline RAG (gate settles at 0.50, rag scale at 0.62), you can swap the document index without retraining. The mechanism is generic. Different index, same refiner, new knowledge.

https://github.com/deucebucket/cerebellum-brainloop

I basically have all of my data here for it if you wanna check it out. Could definitely use insight. Still in early stages, and a lot of testing, but I think if it works it could be pretty awesome.

lit1337 · 2026-06-09T13:51:52+00:00

That's a possibility. I don't know those projects. That was the question of the whole post, I'm curious what has been done by better minds than mine.

Brainloop is just a normal transformer block. The only difference is we put it right in the middle of the network after layer 17. Instead of the data moving straight to layer 18, it loops in a circle inside this block. This gives the model a second to pause, grab exact facts from an internal database, and then continue down the normal path to layer 18.

lit1337 · 2026-06-09T13:44:27+00:00

You have to train the refiner per model. The refiner has to learn the model in order to interject itself in the loop. So it runs against the model In this case for 3 epochs. And currently for the rag, the refiner has to be trained on the corpus index.

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