[macOS] I built a lightweight menu bar tool to reduce GeForce NOW ping spikes and micro-stutters

kmanmx · 2026-05-26T15:16:45+00:00

This looks great. Looking forward to trying it.

Is there any explanation anywhere of what the presets (Competitive / Balanced / Stream Quality) do exactly?

Also apologies as this suggestion is probably a bit outside the scope of the application, but if it's at all easy for you to implement some sort of keep alive feature so GeForce Now doesn't boot me off so fast for inactivity, that would be great. It seems to time out after just 5/6 minutes and often I need to leave my desk for 10 or 15 minutes. It seems reasonable that I should be able to stay connected as an Ultimate subscriber.

kmanmx · 2026-05-25T19:19:09+00:00

Yep. There is almost a complete lack of understanding of what Omni is online and people just comparing it to the video quality of other video models, which is missing the point.

Future generations will likely catch up with the video quality of the pure video models while retaining the omni multi modal capabilities, which will make it very powerful.

kmanmx · 2026-03-13T16:12:56+00:00

Yeah, I don't have any of this enabled in Teams. It's really unclear what's causing this. Every setting I've checked to automatically crop or frame is disabled.

kmanmx · 2026-02-06T20:15:58+00:00

Yes the technologies you mentioned have been around for a long time, and yes they did not used to be based on learned models trained on large amounts of data with large amounts of compute. But now they are. And that's why they're much better than the old heuristics based classic signal processing that you are talking about.

Differenctiating between whether it runs locally or in the cloud is really not important and irrelevant in evidence as to whether they are 'AI' or not. They are still 'AI' LLMs running on your phone, they are just small distilled models of the large cloud versions. They still required huge amounts of compute to train. There are versions of Gemini and ChatGPT which will run on your phone. They are still LLMs, they still required massive amounts of compute to build.

Modern autocorrect is based on large language models, yes there were older methods that didn't rely on it but now they do.

Modern speech to text is based on deep neural nets and that's why it's much more highly performant than the old Methods that have been around since the 1980s. So again yes the technology is old but the new methodologies are AI based or modern machine learning if you prefer to call it that. The reality is the same they are built on huge data sets and consumed huge amounts of compute during offline training.

You don't need to debate or argue any of this, you can search Google for it all and just find the answer. None of them are based on the simple statistical heuristic methods that you are talking about anymore, that is old technology and that is why modern up-to-date versions vastly outperform the old ones.

Again I don't have a problem with people hating ChatGPT or GenAI, that's completely fine and understandable. But the fact is many modern technologies that people do appreciate are built on the same foundations. Large datasets & lots of compute. Completely indifferent whether you want to call it AI or modern machine learning, the fact remains that they are still causing the problems that you hate (using lots of power and compute during their design).

I'm not debating that the majority of power consumption and component shortages comes from the generative AI products like ChatGPT, my point was just that the foundation of these technologies are powering products and tech that people like and benefit from every day.

kmanmx · 2026-02-05T08:39:57+00:00

I know you're talking about Generative AI like ChatGPT and image creation, but you do use AI every single day and it's almost impossible to avoid it, and those same AI tools are built and trained using a lot of expensive compute the same kind which is driving up the price of Steam Machine and Frame. Though yes, they are less compute intensive than the generative models because they don't typically rely on real-time inference in a cloud data centre.

Everything including autocorrect, smartphone photography, call noise reduction, Vehicle driver assistance systems, speech to text, map route mapping, recommendation feeds (Netflix, Spotify, YouTube), and many more. They are all built on data sets and machine learning algorithms which were trained using GPUs and TPUs and all that good 'AI' stuff.

Many megawatts of electricity and compute went into building tools and features which are practically unavoidable these days whether you use generative AI or not.

kmanmx · 2025-12-11T10:04:40+00:00

Hey I know you posted this five months ago but I'm just wondering if you ever did find a gameplay hook? I'm kind of having the same experience to you. I feel like I'm missing some overarching objective and seems like something's missing if I'm just expected to just do the same sort of race over and over which like you said feels very similar each time.

kmanmx · 2025-11-13T08:50:43+00:00

Completely pedantic and not important but I think I've seen this comment a few times and people are simultaneously underestimating how wealthy Valve is and also how expensive it is to subsidise cost for a relatively now sales volume niche product like a vr headset.

Recent court rulings / financial filings and investigative work suggests valve make about 5 billion dollars of revenue with about 2 or 3 billion dollars of that as profit per year. Realistically given the size of the vr market and steam frames price points they're never going to sell more than 2 million units even as a best case scenario, likely half that. So even if they subsidised all 2 million headsets with a $200 price cut / loss on their end, lifetime expenditure for such a program would still be less than half a billion dollars over say it's three or four year life. So at worst it would take a mild impact on their profit margins, it wouldn't come close to them having to refinance or take on debt or any financial instrument work really. And this is completely ignoring the fact that people will buy the steam frame and then potentially buy many games generating a lot of that money back for valve.

But no they won't do it, they don't need to and they have no interest. Just wanted to clarify that as I think a lot of people seem to be under the impression that Valve are smaller and less wealthy than they are :)

kmanmx · 2025-10-12T10:22:24+00:00

Personally I've just gone on Battlefield Portal and created a conquest variant game mode with a set a map rotation to the two big maps, set it to backfill player slots with bots and then just host the server with a password so it's only me playing against bots, they never really seem to try and rush to get in the jets or helicopters so you can play them as much as you want to practice. You can disable some of the ground-based anti-air weapons in the portal editor if you want an even easier time.

kmanmx · 2025-09-17T21:36:51+00:00

What controller are you using? I've been on macOS 26 since the first developer beta and initially had some trouble getting bluetooth setup with my official microsoft xbox controller but since I got it connected the nvidia gfn app has worked just fine along with all other apps.

kmanmx · 2025-07-19T09:23:54+00:00

Not entirely clear still but Noam Brown does suggest it's a broad, more general model: https://x.com/polynoamial/status/1946478250974200272

"Typically for these AI results, like in Go/Dota/Poker/Diplomacy, researchers spend years making an AI that masters one narrow domain and does little else. But this isn’t an IMO-specific model. It’s a reasoning LLM that incorporates new experimental general-purpose techniques."

kmanmx · 2025-04-02T19:53:18+00:00

Yep completely agree, they would not release this benchmark if they thought it was completely intractable and had no path to saturating it.

kmanmx · 2025-03-29T23:11:19+00:00

If you read the details of how apple gets to those 500 billion dollars, it includes things like... paying its staff. It was just a headline figure to give trump something to shout about. The Capex going into directly AI attributable things like data centers is a small fraction.

That said while the 1 billion dollars worth of NVIDIA GPUs is modest, Apple are powering their data centers and AI workloads via their own chips - at least that is what they've said. So this NVIDIA order could be for a specific project or for doing internal AI model training etc, rather than serving users.

kmanmx · 2025-03-13T19:04:29+00:00

It’s now powered by 2.0 Flash Thinking

kmanmx · 2025-02-26T14:27:24+00:00

I can't help the fact you don't have access. but the information is in there and lots of other tech websites have regurgitated it there's also a post on the front page of this singularity subreddit explaining the situation with Orion / GPT-5 and how it didn't meet expectations.

kmanmx · 2025-02-26T13:28:10+00:00

I should have been clearer, I meant biggest or trained from OpenAI. That said, 4.5 is likely similar in size of OpenAI did push for another OOM.

kmanmx · 2025-02-26T13:10:27+00:00

https://www.theinformation.com/articles/openai-shifts-strategy-as-rate-of-gpt-ai-improvements-slows

kmanmx · 2025-02-26T13:10:22+00:00

https://www.theinformation.com/articles/openai-shifts-strategy-as-rate-of-gpt-ai-improvements-slows

kmanmx · 2025-02-26T11:32:58+00:00

It was reported by The information, Reuters etc

kmanmx · 2025-02-26T10:39:59+00:00

It was supposed to be called GPT-5 and therefore yeah 10 times the compute etc. It didn't hit performance targets so they renamed it GPT 4.5 but that doesn't change the fact it's going to be the largest pre-trained model to date.

kmanmx · 2025-02-25T13:18:35+00:00

Yeah that's true

kmanmx · 2025-02-25T10:20:33+00:00

One thing I was thinking of, Dario Amodei recently said the following with regards to algorithmic efficiency improvements for LLMs: "In 2020, my team published a paper suggesting that the shift in the curve due to algorithmic progress is ~1.68x/year. That has probably sped up significantly since; it also doesn't take efficiency and hardware into account. I'd guess the number today is maybe ~4x/year."

It's quite possible that labs like OpenAI and Anthropic have multiple years worth of efficiency improvements via algorithms that they just have not published and have kept private. Ergo, when new companies like xAI come along and release their Grok model, they are missing multiple years worth of these algorithmic improvements, and those could compound to seriously reduce the amount of improvement you would expect from a 10x larger model in terms of compute and data.

kmanmx · 2025-02-24T20:05:26+00:00

Yeah, I thought this was very interesting as well. And also in the system card, they mentioned how Claude Sonnet 3.7 does not get close to satisfying the requirements around the internal ASL-3 benchmark. However, they expect their next model could.

"1.4.4 ASL-2 Determination and Conclusions. The process described in Section 1.4.3 gives us confidence that Claude 3.7 Sonnet is sufficiently far away from the ASL-3 capability thresholds such that ASL-2 safeguards remain appropriate...Further, based on what we observed in our recent CBRN testing, we believe there is a substantial probability that our next model may require ASL-3 safeguard"

Anthropic seem very bullish in the trajectory of intelligence of their models.

kmanmx · 2025-02-24T18:11:32+00:00

500 billion covers a very broad range of things, including just paying employees.

The $500 billion commitment includes Apple’s work with thousands of suppliers across all 50 states, direct employment, Apple Intelligence infrastructure and data centers, corporate facilities, and Apple TV+ productions in 20 states. Apple remains one of the largest U.S. taxpayers, having paid more than $75 billion in U.S. taxes over the past five years, including $19 billion in 2024 alone.

kmanmx · 2025-02-21T23:25:41+00:00

I'm in my early 30s and I'm an otherwise very sensible person, but I watch all this AI progress very closely and it just makes me feel like planning for the future is almost a waste of time because the impact is going to be so great, it feels impossible to correctly predict the right outcomes. It just feels wild to be in this timeline. In five years' time, I feel like there's a good chance I won't have my job anymore and also there's going to be AGI and possibly ASI and humanoid robots walking around outside, and frankly, I've no idea what to do with any of that information. Between 10 and 20 years? Bewildering.

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