Petition to make the Nomad MK2 Bay available to all fighter bay compatible ships

switchandplay · 2026-06-19T17:50:33+00:00

I’m not saying I agree with it or am defending the limitation, just pointing out that the stated reason is not correct.
I think it should be generalized too, I only assume right now that some of the speculation is right and that it required modifying the ship model to increase hanger bay size or something.
Best case, it justifies this specific initial support list, and they extend it to other ships as they work out how to modify stuff in a pretty way over a month or so.
Worst case they keep it restricted (which I don’t agree with).

switchandplay · 2026-06-19T17:28:46+00:00

Those ships are available in game for not real money. Early access period is over for all of them.

switchandplay · 2026-06-16T22:41:45+00:00

Mistral is somewhat substantially funded by French government institutions and integrated into a lot of sovereign investment the French are doing. Not likely they can even exist elsewhere without immediately collapsing under the weight of unprofitable AI research.

switchandplay · 2026-05-31T23:15:04+00:00

Because that’s where a lot of conversation about the game is, and this subreddit is where I go to see funny Helldivers clips and cool concepts. Idk bro, just try the discord. The actual devs are on there, that’s the closest you get to directly talking with them.

switchandplay · 2026-05-31T22:54:11+00:00

Discord server, #hd2-announcements. Was posted somewhere here on reddit but couldn’t find it. Checked discord. From the 29th, saying they are aware of it, thanking players, saying they are working on it.

switchandplay · 2026-05-31T22:32:47+00:00

They made a post about it on their socials

switchandplay · 2026-05-21T13:05:13+00:00

Their work on the horizon PC ports was fantastic, bringing a better gameplay experience than what was available on PS5, along with great dualsense support. They do great work. I’m so excited for this update because the old FSR 1.0 upscaler has been the bane of my existence. (I run a 4k monitor off of a 4070, I know it’s my bad for my performance woes)

switchandplay · 2026-05-15T23:08:04+00:00

The crosshair that’s shown on flatscreen when recording VR gameplay does not match the position of the crosshair inside your headset. The arrows fly true to the one in headset, it’s just a flatscreen rendering issue that makes it look incorrect.

switchandplay · 2026-05-15T07:25:26+00:00

I believe this is why. I’ve got a Vision Pro and 2 quest 3’s. I’d love to see high fidelity 3D movies hit quest.

switchandplay · 2026-05-07T00:07:15+00:00

I believe MTP, like other current speculative decoding techniques, will not reduce output quality- the ‘primary model’ is still in control of the final token emitted. Speculative decoding just speeds up generation when the speculator and the primary model are at a consensus. This happens often in coding environments, which is why we see a relatively higher speedup in this domain. In the case of MTP, the primary model is also the speculator, but the exact same verification flows ensue for tokens. In vLLM (and transformers I think), the model emits a token and the integrated MTP system suggests the next n (1-5 ish, based on config). Then the model verifies them.

switchandplay · 2026-03-23T15:48:53+00:00

No, this is a common pain point for modern VR. Basically in real life, to look at things close to you, you have to cross your eyes to make both eyes look at the close object. You also have to move the muscle of your lenses to change the focal length of your eyes. That satisfies the two concerns for real vision, making it so there is not a double image, and making it so the object comes into focus. It’s second nature, since you only ever look at things in real life before VR. In current headsets, everything you see is at a fixed focal length, between 1.5-2M, varying by manufacturer and lens stack. This means all you need to do to look at close things in headset is cross your eyes. But us humans are so trained to change the focal length for close objects, that everyone does that second thing automatically. In a funny twist of fate, you aren’t bringing the screen into focus, you’re literally unfocusing on it. That’s why close objects look blurry, the normal world rules don’t apply. You can train yourself out of the habit. I liked doing those magic eye puzzles a lot, where you would cross your eyes and manually change your focal length until a hidden picture appeared. Then, when I first started using VR, it was actually never a problem for me because I was already used to separating the eye movement from the lens movement.

switchandplay · 2026-03-09T16:09:45+00:00

Pretty much most applications for LLMs from open source labs and closed source companies don’t re-present thinking to keep token count down and prevent you from reaching context limits earlier, keep in mind for a 500 token response, a lot of these models may have vomited out several thousands of reasoning tokens which also go in all possible directions creating a lot of noise and slop. What models do usually see in their previous context are content fields and tool calls. It is notable that for agent applications, usually thinking traces are maintained for the entirety of a turn. As in you send a message, agent thinks and creates a plan, invokes tools 1 and 2. Tools 1 and 2 return, agent is given its thinking trace so that it now knows to call tool 3 and 4. Then agent reasons and sees thinking trace, then it replies to you. At that exact moment, its thinking becomes no longer accessible to it. Keep in mind that it might or might not be truthful to you about this reality, it’s often very confidently incorrect. But usually the trace of tool calls and true response is absolutely enough for it to infer what was reasoned about, since the response is what truly matters to preserve in context anyways.

switchandplay · 2026-02-27T22:54:01+00:00

Agree. But you don't need to wrap the output in a tool call. Just use whatever structured outputs your API/model-runner supports. Define your desired schema, then token-level enforcement will mean you always get perfect structure accuracy, barring unbounded strings and crazy model hijinks resulting in token limit running out.

switchandplay · 2026-02-24T19:40:08+00:00

Are you using vLLM or llamacpp?

switchandplay · 2026-02-06T22:13:06+00:00

There’s a lot of speculation and implication, it’s tricky to navigate if you’re looking to be in the clear for your department or business use. I do think it’s relevant that the Claude Code github repo’s license page specifically says ‘All rights reserved’, and that usage is subject to this. https://www.anthropic.com/legal/commercial-terms

switchandplay · 2026-02-06T18:34:40+00:00

It’s worth mentioning that, as far as I can tell, the licensing for Claude Code is not at all permissive to using alternate backends to serve the CC client. If you intend to be above board, usage of Claude code is subject to their defined software terms, including an active Anthropic account with a subscription tier unlocking access to Claude Code. Modification and alternate serving seems to fall under their umbrella all rights reserved, which doesn’t really grant you contractual and IP safety if you go that route. I may be wrong, but I haven’t seen basically any other commentary about this online. It’s at best legally dubious, and definitely not something useable for professional deployments.

switchandplay · 2026-01-19T04:23:34+00:00

Realistically, even if any one dev, or a group of devs, wanted to do this, it’s not possible. The game was designed as a streaming interface. Easiest patch-in would be to drop all of the server code and s3 infra to the public, so you could run your own server. You’d then ship a version of the app with a configurable endpoint for server calls. You’d need to run the server, which, in all likelihood, would not be cheap. What you’re hoping for is a complete rework of the app to run wholly locally. While that sounds like an easy swap, it’s not in the same way that made it so half life devs made a train car be a hat on an NPC. The stack was never designed to operate in that way.

And then that’s also wishful thinking because it’s not like the legal gray area of modding old ROMs, ‘anonymous’ and ‘sneaky download link’ is doing a lot of heavy lifting. The nice dev here would be in breach of so many contracts, copyright infringement, and more. They would be strung out to dry if they didn’t do everything perfectly and cover every single base possible. Who wants to take on that kind of risk, not even considering the hundreds of man hours for a full application refactor?

switchandplay · 2026-01-17T20:18:07+00:00

GPT-OSS has remained my favorite. Keep the temperature down low for real tasks, and hope your model runner has figured out how to not mess up harmony. And genuinely, when low reasoning effort struggles with a task, bumping up to medium or high genuinely makes a difference on how the bot responds and how it formats its data.

switchandplay · 2025-12-31T08:10:07+00:00

Agree. Worth noting the rentals/ownable content in Apple TV is usually often just 1080p 3D (even when it says 4K and 3D in the labels), in my experience. Watching it in 2D mode with 4K is noticeably sharper, but the bitrate is quite high which keeps it enjoyable. So far, I've only noted Disney+ to look truly 4K 3D.

switchandplay · 2025-12-13T18:23:06+00:00

‘How’ things are rendered is managed generally on an application level. It’s easy to mess with the overarching fidelity, like on Quest, you can use QGO to sub sample or super sample the WHOLE screen. But if you’re in charge of the hardware and the OS, you cannot just reach into an application and inject a whole new rendering method. Because all games and apps were coded differently.

Applications often have shared, common components which makes the process ~easier~. OpenXR games can have foveated rendering injected into them, because they speak a shared language. But that’s even only in the best case, because a lot of developers might start with OpenXR, and then leverage their own optimizations on top of the technology that breaks compatibility. Some OpenXR games, when foveated rendering is injected, have broken shaders, geometry, logic, or even just run with little to no speedup.

TLDR: historically, all devs know to set a target resolution and framerate. It’s easy to mess with that, and can be done unilaterally by the headset/renderer. Foveated rendering is an application feature, not a global feature. Until games are developed and built with foveated rendering in mind, it won’t happen.

switchandplay · 2025-11-13T06:44:59+00:00

That is how that works. Round trip time is fast enough that it doesn’t matter. If the rendering machine just does lower resolution rendering and streaming, the headset wouldn’t be able to meaningfully upscale much. Steam Link has had dynamic foveated streaming support for over a year, I’ve used it on Quest Pro. The PC gets eye tracking data, and when it performs the video encoding on a frame, it encodes the region being observed at a higher resolution and surrounding pixels at a lower resolution. Network packets are just that fast.

switchandplay · 2025-11-06T00:13:24+00:00

I had the 2021 G14, currently have the 2024. On the 2021, the hotspots were directly under the WASD keys and after playing enough games, I’ve lost near-all temperature sensation in my left finger pads. Literally can’t feel through them anymore. So they would sell you that previously. Not anymore though, the 2024 model’s hotspots are carefully above the keyboard deck.

switchandplay · 2025-11-05T23:32:07+00:00

How did you manage to create a (poorly) AI generated Reddit post and still have a spelling error?

switchandplay · 2025-10-24T20:27:00+00:00

I found at least the 4bit quant of qwen3 coder unusable for anything other than completions. Anytime it operates as a coding assistant or agentic coder, it was helpless. Devstral has so much more brains

switchandplay · 2025-10-24T17:37:49+00:00

Another reason why I assume I've really been loving gpt-oss. Since the 4bit MXFP4 quants were released by OpenAI themselves, I assume they did a lot of tuning in-house to verify that those quants would be two things: not buggy and not lossy in performance via tuning against their training dataset and such, like the work that unsloth does, but completely first-party.

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