With respect, this doesn’t make any sense. If you want a joystick controller, just buy an Xbox controller that everything’s compatible with anyway?
The trackpads shine when one needs to emulate a mouse/kb in non-controller games; a nightmare with joysticks.
My sister still has a working one that she treats like a religious artifact, as it’s the best way to play mouse/KB games from the sofa.
I see why they discontinued them though. They need custom configs for most games, and I think most people don’t like that much tweaking.
A lot, but less than you’d think! Basically a RTX 3090/threadripper system with a lot of RAM (192GB?)
With this framework, specifically: https://github.com/ikawrakow/ik_llama.cpp?tab=readme-ov-file
The “dense” part of the model can stay on the GPU while the experts can be offloaded to the CPU, and the whole thing can be quantized to ~3 bits average, instead of 8 bits like the full model.
That’s just a hack for personal use, though. The intended way to run it is on a couple of H100 boxes, and to serve it to many, many, many users at once. LLMs run more efficiently when they serve in parallel. Eg generating tokens for 4 users isn’t much slower than generating them for 2, and Deepseek explicitly architected it to be really fast at scale. It is “lightweight” in a sense.
…But if you have a “sane” system, it’s indeed a bit large. The best I can run on my 24GB vram system are 32B - 49B dense models (like Qwen 3 or nemotron), or 70B mixture of experts (like the new Hunyuan 70B).
DeepSeek, now that is a filtered LLM.
The web version has a strict filter that cuts it off. Not sure about API access, but raw Deepseek 671B is actually pretty open. Especially with the right prompting.
There are also finetunes that specifically remove China-specific refusals. Note that Microsoft actually added saftey training to “improve its risk profile”:
https://huggingface.co/microsoft/MAI-DS-R1
https://huggingface.co/perplexity-ai/r1-1776
That’s the virtue of being an open weights LLM. Over filtering is not a problem, one can tweak it to do whatever you want.
Grok losing the guardrails means it will be distilled internet speech deprived of decency and empathy.
Instruct LLMs aren’t trained on raw data.
It wouldn’t be talking like this if it was just trained on randomized, augmented conversations, or even mostly Twitter data. They cherry picked “anti woke” data to placate Musk real quick, and the result effectively drove the model crazy. It has all the signatures of a bad finetune: specific overused phrases, common obsessions, going off-topic, and so on.
…Not that I don’t agree with you in principle. Twitter is a terrible source for data, heh.
Nitpick: it was never ‘filtered’
LLMs can be trained to refuse excessively (which is kinda stupid and is objectively proven to make them dumber), but the correct term is ‘biased’. If it was filtered, it would literally give empty responses for anything deemed harmful, or at least noticably take some time to retry.
They trained it to praise hitler, intentionally. They didn’t remove any guardrails. Not that Musk acolytes would know any different.
Also a crime. Not just a great game in their niche, but a long history of them.
Never underestimate Phil Spencer.
Reads bit on dictator-propping via propaganda in Sudan.
Nods. Yep, that sounds like my government alright. And Big Tech. Cries inside.
Veers off to “Ukraine Proxy War” with no reference to ChatGPT as promised in the headline.
Sighs. Closes tab.
OK, while in principle this looks bad…
This is (looking it up) like an experienced engineer’s salary in Peru, in line with some other professions.
It’s reasonable to compensate a president, and for the expectation to not be coming in rich/connected enough to not need a salary. Nor for them to broker power for personal wealth, all as long as other offices and reasonably compensated too.
It avoids perverse incentives, doesn’t seem excessive and TBH is probably a drop in the Peruvian govt’s budget.
…iOS forces uses Apple services including getting apps through Apple…
Can’t speak to the rest of the claims, but Android practically does too. If one has to sideload an app, you’ve lost 99% of users, if not more.
It makes me suspect they’re not talking about the stock systems OEMs ship.
Relevant XKCD: https://xkcd.com/2501/
deleted by creator
Pro is 120hz.
But they are expensive as heck. I only got the 16 Plus because its a carrier loss leader, heh.
And wouldn’t fix some of my other quibbles with iOS’s inflexibility. My ancient jailbroken iPhone 5 was more customizable than now, and Apple is still slowly, poorly implementing features I had a decade ago. It’s mind boggling, and jailbreaking isn’t a good option anymore.
My last Android phone was a Razer Phone 2, SD845 circa 2018. Basically stock Android 9.
And it was smooth as butter. It had a 120hz screen while my iPhone 16 is stuck at 60, and I can feel it. And it flew through some heavy web apps I use while the iPhone chugs and jumps around, even though the new SoC should objectively blow away even modern Android devices.
It wasn’t always this way; iOS used to be (subjectively) so much faster that it’s not even funny, at least back when I had an iPhone 6S(?). Maybe there was an inflection point? Or maybe it’s only the case with “close to stock” Android stuff that isn’t loaded with bloat.
Random aside, I switched from Android to iOS a year ago. I miss Android already.
The UI is more convoluted an clunky than iOS from years ago, just as uncustomizable, and performs shockly bad on heavy webpages on a brand new 16+. It’s got no freaking RAM, no sd card slot. Some free FOSS apps are nonexistant or paid only.
Security and OOTB privacy is better and app support is generally better, but that’s about it? I’d probably keep an iPhone around to bank on when I eventually switch…
One thing about Anthropic/OpenAI models is they go off the rails with lots of conversation turns or long contexts. Like when they need to remember a lot of vending machine conversation I guess.
A more objective look: https://arxiv.org/abs/2505.06120v1
https://github.com/NVIDIA/RULER
Gemini is much better. TBH the only models I’ve seen that are half decent at this are:
“Alternate attention” models like Gemini, Jamba Large or Falcon H1, depending on the iteration. Some recent versions of Gemini kinda lose this, then get it back.
Models finetuned specifically for this, like roleplay models or the Samantha model trained on therapy-style chat.
But most models are overtuned for oneshots like fix this table or write me a function, and don’t invest much in long context performance because it’s not very flashy.
What @mierdabird@lemmy.dbzer0.com said, but the adapters arent cheap. You’re going to end up spending more than the 1060 is worth.
A used desktop to slap it in, that you turn on as needed, might make sense? Doubly so if you can find one with an RTX 3060, which would open up 32B models with TabbyAPI instead of ollama. Some configure them to wake on LAN and boot an LLM server.
Yes, but its clearly a building block of Meta’s LLM training effort, and part of a pattern.
One implication I didn’t mention, and don’t have hard proof I can point to, is garbage in garbage out. Meta let AI slop and human garbage proliferate on Facebook, squandering basically the biggest advantage (besides cash) they have. It’s often speculated that, as it turns out, Twitter and Facebook training data is kinda crap.
…And they’re at it again. Zuckerberg pours cash into corporate trash and get slop back. It’s an internal disaster, like their own divisions.
On the other side, it’s often thought that Chinese models are so good for their size/compute because they’re ahem getting data from the Chinese government, and don’t need to worry about legal issues.
The research community already knows this.
Llama 4 (Meta’s flagship ‘AI’ project) was as bad release. That’s fine. This is interative research; not every experiment works out.
…But it was also a messy and dishonest one.
The release was pushed early and full of bugs. They lied about its performance, especially at long context, going so far as to game Chat Arena with a finetune. Zuckerberg hyped the snot out of it, to the point I saw ads for it on Axios.
Instead of Meta saying they’ll do better, they said they’re reorganizing their divisions to focus on ‘applications’ instead of fundamental research, aka exactly the wrong thing. They’ve hermmoraged good researchers and kept AI bros, far as I can tell from the outside.
Every top LLM trainer has controversies. Just recently Qwen (Alibaba) closed off their top base models just to spite Deepseek, so they can’t distill them. Deepseek is almost certainly training on Google Gemini traces. Google hoards their best research for API models and has chased being sycophantic like ChatGPT. X’s Grok is a joke, and muddied by Musk’s constant lies about, for instance, open sourcing it. Some great outfits like 01ai (the Yi series) faded into the night.
…But I haven’t seen self-destruction quite like Meta’s. Especially considering the ‘f you’ money and GPU farm they have. They’re still pushing interesting research now, but the trajectory is awful.
ChatGPT (last time I tried it) is extremely sycophantic though. Its high default sampling also leads to totally unexpected/random turns.
Google Gemini is now too.
And they log and use your dark thoughts.
I find that less sycophantic LLMs are way more helpful. Hence I bounce between Nemotron 49B and a few 24B-32B finetunes (or task vectors for Gemma) and find them way more helpful.
…I guess what I’m saying is people should turn towards more specialized and “openly thinking” free tools, not something generic, corporate, and purposely overpleasing like ChatGPT or most default instruct tunes.
Not everyone’s a big kb/mouse fan. My sister refuses to use one on the HTPC.
Hence I think that was its non-insignificant niche; couch usage. Portable keyboards are really awkward and clunky on laps, and the steam controller is way better and more ergonomic than an integrated trackpad.
Personally I think it was a smart business decision, because of this:
No one’s going to buy a steam-branded Xbox controller, but making it different does. And I think what killed it is that it wasn’t plug-and-play enough, eg it didn’t work out of the box with many games.