RU version is available. Content is displayed in original English for accuracy.
Advertisement
Advertisement
⚡ Community Insights
Discussion Sentiment
74% Positive
Analyzed from 7325 words in the discussion.
Trending Topics
#apple#models#don#model#app#siri#doesn#using#knowledge#more
Discussion (197 Comments)Read Original on HackerNews
If a couple more iterations of this, say gemma6 is as good as current opus and runs completely locally on a Mac, I won’t really bother with the cloud models.
That’s a problem.
For the others anyway.
Glad it wasnt just me - i was impressed with the quality of Gemma4 - it just couldnt write the changes to file 9/10 times when using it with opencode
There was an update to tool calling 3 days ago. I haven't tested it myself but hope it helps.
You might want to give this a try, it dramatically improves Edit tool accuracy without changing the model: https://blog.can.ac/2026/02/12/the-harness-problem/
I won't deny that the latest Claude models are fantastic at just one shotting loads of problems. But we have an internal proxy to a load of models running on Vertex AI and I accidentally started using Opus/Sonnet 4 instead of 4.6. I genuinely didn't know until I checked my configuration.
AI models will get to this point where for 99% of problems, something like Gemma is gonna work great for people. Pair it up with an agentic harness on the device that lets it open apps and click buttons and we're done.
I still can't fathom that we're in 2026 in the AI boom and I still can't ask Gemini to turn shuffle mode on in Spotify. I don't think model intelligence is as much of an issue as people think it is.
The world has moved on, that code-golf time is now spent on ad algorithms or whatever.
Escaping the constraint delivered a different future than anticipated.
it is economically not viable to try anymore.
"XYZ Corp" won't allow their developers to write their desktop app in Rust because they want to consume only 16MB RAM, then another implementation for mobile with Swift and/or Kotlin, when they can release good enough solution with React + Electron consuming 4GB RAM and reuse components with React Native.
But most likely, it's not. At a system level we don't want people to do that. It's a waste of resources. Making a virtue out of it is bad, unless you care more about bytes than humans.
I don't think it's even mildly controversial to say that there will be an inflection point where local models get Good Enough and this iteration of the pendulum shall swing to fat clients again.
The local models have their own advantages (privacy, no -as-a-service model) that, for many people and orgs, will offset a small performance advantage. And, of course, you can always fall back on the cloud models should you hit something particularly chewy.
(All IMO - we're all just guessing. For example, good marketing or an as-yet-undiscovered network effect of cloud LLMs might distort this landscape).
Plus having Gemma on my device for general chat ensures I will always have a privacy respecting offline oracle which fulfils all of the non-programming tasks I could ever want. We are already at the point where the moat for these hyper scalers has basically dissolved for the general public's use case.
If I was OpenAI or Anthropic I would be shitting my pants right now and trying every unethical dark pattern in the book to lock in my customers. And they are trying hard. It won't work. And I won't shed a single tear for them.
It simply.. doesn't. The SotA models are enormous now, and there's no free lunch on compression/quantization here.
Opus 4.6 capabilities are not coming to your (even 64-128gb) laptop or phone in the popular architecture that current LLMs use.
Now, that doesn't mean that a much narrower-scoped model with very impressive results can't be delivered. But that narrower model won't have the same breadth of knowledge, and TBD if it's possible to get the quality/outcomes seen with these models without that broad "world" knowledge.
It also doesn't preclude a new architecture or other breakthrough. I'm simply stating it doesn't happen with the current way of building these.
edit: forgot to mention the notion of ASIC-style models on a chip. I haven't been following this closely, but last I saw the power requirements are too steep for a mobile device.
See: https://x.com/danveloper/status/2034353876753592372
By the time gemma6 allows you to do the above the proprietary models supposedly will already be on the next step change. It just depends if you need to ride the bleeding edge but specially because it's "intelligence", there's an obvious advantage in using the best version and it's easy to hype it up and generate fomo.
Do people actually build meaningful things like that?
It's basically impossible to leave any AI agent unsupervised, even with an amazing harness (which is incredibly hard to build). The code slowly rots and drifts over time if not fully reviewed and refactored constantly.
Even if teams of agents working almost fully autonomously were reliable from a functional perspective (they would build a functional product), the end product would have ever increasing chaos structurally over time.
I'd be happy to be proven wrong.
The minimal size limits of reasoning abilities are not clear at all. It could be that you don't need all that many parameters. In which case the door is open for small focused models to converge to parity with larger models in reasoning ability.
If that happens we may end up with people using small local models most of the time, and only calling out to large models when they actually need the extra knowledge.
When would you want lossy encoding of lots of data bundled together with your reasoning? If it is true that reasoning can be done efficiently with fewer parameters it seems like you would always want it operating normal data searching and retrieval tools to access knowledge rather than risk hallucination.
And re: this discussion of large data centers versus local models, do recall that we already know it's possible to make a pretty darn clever reasoning model that's small and portable and made out of meat.
Once the difficult problem of figuring out what the input is supposed to mean was somewhat solved, bolting on reasoning was easy in comparison. It basically fell out with just a bit of prompting, "let's think step by step."
If you want to remove that knowledge to shrink the model, we're back to contorting our input into a restricted language to get the output we want, i.e. programming.
In the worst case a smaller model could use a tool that involves a bigger model to do something.
"world models" (for cars) maybe make sense for self driving, but they are also just a crude workaround to have a physics simulation to push understanding of physics. Through in difference to most topics, basic, physics tend to not change randomly and it's based on observation of reality, so it probably can work.
Law, health advice, programming stuff etc. on the other hand changes all the time and is all based on what humans wrote about it. Which in some areas (e.g. law or health) is very commonly outdated, wrong or at least incomplete in a dangerous way. And for programming changes all the time.
Having this separation of language processing and knowledge sources is ... hard, language is messy and often interleaves with information.
But this is most likely achievable with smaller models. Actually it might even be easier with a small model. (Through if the necessary knowledge bases are achievable to fit on run on a mac is another topic...)
And this should be the goal of AI companies, as it's the only long term sustainable approach as far as I can tell.
I say should because it may not be, because if they solve it that way and someone manages to clone their success then they lose all their moat for specialized areas as people can create knowledge bases for those areas with know-how OpenAI simple doesn't have access to. (Which would be a preferable outcome as it means actual competition and a potential fair working market.)
TLS cipher X25519MLKEM768 is recommended to be enabled on servers which do support it
last time I checked AI didn't even list it when you asked it for a list of TLS 1.3 ciphers (through it has been widely supported since even before it was fully standardized..)
this isn't surprising as most input sources AI can use for training are outdated and also don't list it
maybe someone of OpenAI will spot this and feet it explicitly into the next training cycle, or people will cover it more and through this it is feed implicitly there
but what about all that many niche but important information with just a handful of outdated stack overflow posts or similar? (which are unlikely to get updated now that everyone uses AI instead..)
The current "lets just train bigger models with more encoded data approach" just doesn't work, it can get you quite far, tho. But then hits a ceiling. And trying to fix it by giving it also additional knowledge "it can ask if it doesn't know" has so far not worked because it reliably doesn't realize it doesn't know if it has enough outdated/incomplete/wrong information encoded in the model. Only by assuring it doesn't have any specialized domain knowledge can you make sure that approach works IMHO.
They did do the smart thing of not throwing too much capital behind it. Once the hype crumbles, they will be able to do something amazing with this tech. That will be a few years off but probably worth the wait.
Firefox is also marketing how easy it is to disable AI.
Decently accessible automation and discovery, without having to go figure out a bunch of stuff
Apple seems to follow the values that Steve laid out. Tim isn’t a visionary but he seems to follow the principles associated with being disciplined with cash quite well. They haven’t done any stupid acquisitions either. Quite the contrast with OAI.
But when Apple added it in iPhone 14 (2022)...
We will see if they ever release a new VisionOS device, but it's not the first time they did that; see also the Apple Watch.
This wasn't like HoloLens or Google Glass. They marketed these devices to consumers and then sold these devices to consumers.
And I imagine that like-minded consumers are a pretty large market.
But this approach may not work in other areas: e.g. building electric batteries, wireless modems, electric cars, solar cell technology, quantum computing etc.
Essentially Apple got lucky with AI but it needs to keep investing in cutting edge technology in the various broad areas it operates in and not let others get too far ahead !
Obviously that was built upon years of iPhone experience, but it shows they can lag behind, buy from other vendors, and still win when it becomes worth it to them.
They could change the architecture again tonight, and start releasing new machines with it. The users will adopt because there is literally no other choice.
Every machine they release will be fastest and most capable on the platform, because there is no other option
They do the things they think they can do very well.
Why would they try to build electric batteries, wireless modems, electric cars, solar cells, or quantum computers, if their R&D hadn't already determined that they would likely be able to do so Very Well?
It's not like any of those are really in their primary lines of business anyway.
- Apple Watch
- AirTag
Those are a few that come to mind. All do multi-billions in revenue per year.
My parents use Android to ask “What are the 5 biggest towers in Chicago” or “Remove the people on my picture” while apparently iPhone is only capable of doing “Hey Siri start the Chronometer / There is no contact named Chronometer in your phone”.
My iPhone is lagging a ridiculous 10 years behind. It’s just that I don’t trust Google with my credit card.
The only reason to care about it being OS integrated is to interact with functions of the OS, which siri does fine.
When they made the iPhone, iPod, and Apple Watch they had no specific hardware advantage over competitors. Especially with early iPhone and iPod: no moat at all, make a better product with better marketing and you’ll beat Apple.
Now? Good luck getting any kind of reasonably priced laptop or phone that can run local AI as well as the iPhone/MacBook. It doesn’t matter that Apple Intelligence sucks right now, what matters is that every request made to Gemini is losing money and possibly always will.
This is especially true in 2026 where Windows laptops are climbing in price while MacBooks stay the same.
It's not. People make this claim with zero evidence.
But Google made around $20B profit on Google search in 2025 Q4, and that includes AI search.
In hindsight it’s obvious why they pulled it off - nobody else could do it. They all had pieces missing.
This was the conversation like 1 year ago. What has changed?
Consumers want iPhones and (if Apple are right) some form of AR glasses in the next decade. That’s their focus. There’s a huge amount of machine learning and inference that’s required to get those to work. But it’s under the hood and computed locally. Hence their chips. I don’t see what Apple have to gain by building a competitor to what OpenAI has to offer.
Income is a much tighter correlation than messaging platform. Rack up those market shares by phone value and the scales tip even harder.
According to https://gs.statcounter.com/os-market-share/mobile/united-kin... it's closer to 50/50.
So no VR, given the price and lack of developer support, and late arrival into AI.
When I open up JIRA or Slack I am always greeted with multiple new dialogues pointing at some new AI bullshit, in comparison. We hates it precious
However, I have even less patience for companies forcing paid-for third-party ads down my throat on a paid product. Slack at least doesn't sell my eyeballs. Facebook, Twitter, Google's ads are worse to me than new feature dialogues.
Which brings me to Apple. I pay for a $1k+ device, and yet the app store's first result is always a sponsored bit of spam, adware, or sometimes even malware (like the fake ledger wallet on iOS, that was a sponsored result for a crypto stealer). On my other devices, I can at least choose to not use ad-ridden BS (like on android you can use F-Droid and AuroraStore, on Linux my package manager has no ads), but on iOS it's harder to avoid.
Apple hasn't sunk to Google levels in terms of ads, but they've crossed a line.
I'm actually pretty disappointed in the lack of discovery available in the App Store, but I rarely go there. I'm fine with advertising being there. I wish it was better but I'm not offended that there is paid promotion in a store.
>"to fix this, please install our app"
>search BankName
>comes up with other banks, BankNames US app (not the country you are in)
>revolut etc (cant use in the country you are in)
>ten minutes later
even worse when its your telecomm telling you to install their Official App so you can pay your bills or they will cut your cellular service, and you cant find it
I get an app recommendation from a friend, I go to the App Store and search for it. I have to be super careful about which link I'm actually clicking on and which app I'm installing, because the App Store is riddled with spam and malware.
I wouldn't mind, except that Apple charge 30% of everything with the justification that they are keeping the ecosystem free of spam and malware...
For me, the second tile is an ad for Upside, some cashback app
If I search for my bank, I get another bank. If I search for "Wordle", I get a bunch of ad-supported spamware (both the ad and non-ad results) before the real NYT Games app.
The app store has ads in search results. This is the primary way that my technologically inept relatives end up with the wrong app installed btw, is by searching and clicking the first result, and getting complete trash adware.
Apple should be ashamed of selling out their users.
To add insult to injury, the one AI feature that I may want to evaluate—Claude Code integration in Xcode—is gated behind Tahoe upgrade, even though it has absolutely no reason to do so, given that every other IDE integrates AI features just fine on any recent OS.
Edit: Oh and I’m not getting bombarded in Slack at all, maybe because my company doesn’t pay for any of the AI stuff there. Last time I got a banner or something like that was months ago.
Imagine a future where Nvidia sells the exact same product at completely different prices, cheap for those using local models, and expensive for those deploying proprietary models in data centers.
That said, gaming laptops cooling issues are so often around the GPU so it'd also require a seasoned manufacturer to make it correctly.
As far as I remember Apple basically got forced into opening the platform to 3rd party developers. Not by regulation but by public pressure. It wasn't their initial intention to allow it.
They sure got lucky that unified memory is well-suited for running AI, but they just focused on having cost- and energy-efficient computing power. They've been having glasses in sight for the last 10 years (when was Magic Leap's first product?) and these chips have been developed with that in mind. But not only the chips: nothing was forcing Apple to spend the extra money for blazing fast SSD -- but they did.
So yes, Apple is a hardware company. All the services it sells run on their hardware. They've just designed their hardware to support their users' workflows, ignoring distractions.
With that said, LLM makes the GPU + memory bandwidth fun again. NVidia can't do it alone, Intel can't do it alone, but Apple positioned itself for it. It reminds me how everyone was surprised when then introduced 64-bit ARM for everyone: very few people understood what they were doing.
Tbh there are NVidia GPUs that beat Apple perf 2x or 3x, but these are desktop or server chips consuming 10x the power. Now all Apple needs to do is keep delivering performance out of Apple Silicon at good prices and best energy efficiency. Local LLM make sense when you need it immediately, anywhere, privately -- hence you need energy efficiency.
Unlike Apple, they have even more devices in the field PLUS they have strong models PLUS Apple uses Google models.
1) Apple is not a data company.
2) Apple hasn't found a compelling, intuitive, and most of all, consistent, user experience for AI yet.
Regarding point 2: I haven't seen anyone share a hands down improved UX for a user driven product outside of something that is a variation of a chat bot. Even the main AI players can't advertise anything more than, "have AI plan your vacation".
Boom, you have an agent in the phone capable of doing all the stuff you can do with the apps. Which means pretty much everything in our life.
for llm providers, i always believe the key is to focus on high value problems such as coding or knowledge work, becaues of the high marginal cost of having new customers - the token burnt. and low marginal revenue if the problem is not valuable enough. in this sense no llm providers can scale like previous social media platforms without taking huge losses. and no meaning user stickiness can be built unless you have users' data. and there is no meaningful business model unless people are willing to pay a high price for the problem you solve, in the same way as paying for a saas.
i am really not optimistic about the llm providers other than anthropic. it seems that the rest are just burning money, and for what? there is no clear path for monetization.
and when the local llm is powerful enough, they will soon be obsolete for the cost, and the unsustainable business model. in the end of the day, i do agree that it is the consumer hardware provider that can win this game.
I find this intriguing.. Does anyone here have enough insight to speculate more?
Doing this you will make all kind of fun predictions.
In other news, people keep buying iPhones, and Apple just had its best quarter ever in China. AAPL is up 24% from last year.
that's the other part of the story that matters, not apple intelligence. this writeup tries to touch on that, apple is uniquely positioned to do really well in this arena if/when local llm's becoming commodities that can do really impressive stuff. we're getting there a lot faster than we thought, someone had a trillion parameter qwen3,5 model going on his 128gb macbook and now people are thinking of more creative ways to swap out whats in memory as needed.
User facing software is not the limiting factor in AI assisted replacement of Apple products.
That's also the year where they released on-chip acceleration for certain things, so they probably started a year or 2 before working on that tech? Not as accidental as assumed.
Here's to another 10 years of scuffed Metal Compute Shaders, I guess.
Even if the investment is overblown, there is market-demand for the services offered in the AI-industry. In a competitive playing field with equal opportunities, Apple would be affected by not participating. But they are establishing again their digital market concept, where they hinder a level playing field for Apple users.
Like they did with the Appstore (where Apple is owning the marketplace but also competes in it) they are setting themselves up as the "the bakn always wins" gatekeeper in the Apple ecosystem for AI services, by making "Apple Intelligence" an ecosystem orchestration layer (and thus themselves the gatekeeper).
1. They made a deal with OpenAI to close Apple's competitive gap on consumer AI, allowing users to upgrade to paid ChatGPT subscriptions from within the iOS menu. OpenAI has to pay at least (!) the usual revenue share for this, but considering that Apple integrated them directly into iOS I'm sure OpenAI has to pay MORE than that. (also supported by the fact that OpenAI doesn't allow users to upgrade to the 200USD PRO tier using this path, but only the 20USD Plus tier) [1]
2. Apple's integration is set up to collect data from this AI digital market they created: Their legal text for the initial release with OpenAI already states that all requests sent to ChatGPT are first evaluated by "Apple Intelligence & Siri" and "your request is analyzed to determine whether ChatGPT might have useful results" [2]. This architecture requires(!) them to not only collect and analyze data about the type of requests, but also gives them first-right-to-refuse for all tasks.
3. Developers are "encouraged" to integrate Apple Intelligence right into their apps [3]. This will have AI-tasks first evaluated by Apple
4. Apple has confirmed that they are interested to enable other AI-providers using the same path [4]
--> Apple will be the gatekeeper to decide whether they can fulfill a task by themselves or offer the user to hand it off to a 3rd party service provider.
--> Apple will be in control of the "Neural Engine" on the device, and I expect them to use it to run inference models they created based on statistics of step#2 above
--> I expect that AI orchestration, including training those models and distributing/maintaining them on the devices will be a significant part of Apple's AI strategy. This could cover alot of text and image processing and already significantly reduce their datacenter cost for cloud-based AI-services. For the remaining, more compute-intensive AI-services they will be able to closely monitor (via above step#2) when it will be most economic to in-source a service instead of "just" getting revenue-share for it (via above step#1).
So the juggernaut Apple is making sure to get the reward from those taking the risk. I don't see the US doing much about this anti-competitive practice so far, but at least in the EU this strategy has been identified and is being scrutinized.
[1] https://help.openai.com/en/articles/7905739-chatgpt-ios-app-...
[2] https://www.apple.com/legal/privacy/data/en/chatgpt-extensio...
[3] https://developer.apple.com/apple-intelligence/
[4] https://9to5mac.com/2024/06/10/craig-federighi-says-apple-ho...
Maximizing the available options is in fact a "strategy", and often a winning one when it comes to technology. I would love to be reminded of a list of tech innovators who were first and still the best.
Anyway, hasn't this always been Apple's strategy?
they wait until the dust settles before making their well-thought-out moves.
Every time they’ve jumped the hype train too quickly it hasn’t worked out, like Siri for example.
But... what's the argument that the bulk of "AI value" in the coming decade is going to be... Siri Queries?! That seems ridiculous on its face.
You don't code with Siri, you don't coordinate automated workforces with Siri, you don't use Siri to replace your customer service department, you don't use Siri to build your documentation collation system. You don't implement your auto-kill weaponry system in Siri. And Siri isn't going to be the face of SkyNet and the death of human society.
Siri is what you use to get your iPhone to do random stuff. And it's great. But ... the world is a whole lot bigger than that.
This was really unsurprising [0].
[0] https://news.ycombinator.com/item?id=40278371
> Won't be surprised for the re-introduction of Xserve again but for AI.
This means, Apple is gonna spend a lot of money standing up data centers (CapEx). And the article in question is essentially saying that Apple is smart not to spend any money.
It sounds like there's a bit of wishful thinking on - Whatever Apple is doing is 4D chess. Apple not spending any money - That's genuis. Apple re-introducing Xserve racks - genius.
According to Bloomberg, Apple's inference server farms are a flop: https://9to5mac.com/2026/03/02/some-apple-ai-servers-are-rep...
Well.. no. The Stargate expansion was cancelled the orginally planned 1.2MW (!) datacenter is going ahead:
> The main site is located in Abilene, Texas, where an initial expansion phase with a capacity of 1.2 GW is being built on a campus spanning over 1,000 acres (approximately 400 hectares). Construction costs for this phase amount to around $15 billion. While two buildings have already been completed and put into operation, work is underway on further construction phases, the so-called Longhorn and Hamby sections. Satellite data confirms active construction activity, and completion of the last planned building is projected to take until 2029.
> The Stargate story, however, is also a story of fading ambitions. In March 2026, Bloomberg reported that Oracle and OpenAI had abandoned their original expansion plans for the Abilene campus. Instead of expanding to 2 GW, they would stick with the planned 1.2 GW for this location. OpenAI stated that it preferred to build the additional capacity at other locations. Microsoft then took over the planning of two additional AI factory buildings in the immediate vicinity of the OpenAI campus, which the data center provider Crusoe will build for Microsoft. This effectively creates two adjacent AI megacampus locations in Abilene, sharing an industrial infrastructure. The original partnership dynamics between OpenAI and SoftBank proved problematic: media reports described disagreements over site selection and energy sources as points of contention.
https://xpert.digital/en/digitale-ruestungsspirale/
> Micron’s stock crashed. [the link included an image of dropping to $320]
Micron’s stock is back to $420 today
> One analysis found a max-plan subscriber consuming $27,000 worth of compute with their 200$ Max subscription.
Actually, no. They'd miscalculated and consumed $2700 worth of tokens.
The same place that checked that claim also points out:
> In fact, Anthropic’s own data suggests the average Claude Code developer uses about $6 per day in API-equivalent compute.
https://www.financialexpress.com/life/technology-why-is-clau...
I like Apple's chips, but why do we put up with crappy analysis like this?
Which is why they were completely caught offguard with botched rollout of Apple Intelligence. Even when they were playing to their strengths, things have not gone for them (Apple Vision Pro). Liquid Glass has had mixed reception, and that's often explained away as "Apple is setting up a world for Spatial Computing by unifying design language" and when the lead designer was fired it was like "Thank God Alan Dye is gone, he was bad for Apple anyway".
So essentially, Apple can do no wrong.
Rather, I feel that Apple has forgotten its roots. The Mac was “the computer for the rest of us,” and there were usability guidelines backed by research. What made the Mac stand out against Windows during a time when Windows had 95%+ marketshare was the Mac’s ease of use. The Mac really stood out in the 2000s, with Panther and Tiger being compelling alternatives to Windows XP.
I think Apple is less perfectionistic about its software than it was 15-20 years ago. I don’t know what caused this change, but I have a few hunches:
0. There’s no Steve Jobs.
1. When the competition is Windows and Android, and where there’s no other commercial competitors, there’s a temptation to just be marginally better than Windows/Android than to be the absolute best. Windows’ shooting itself in the foot doesn’t help matters.
2. The amazing performance and energy efficiency of Apple Silicon is carrying the Mac.
3. Many of the people who shaped the culture of Apple’s software from the 1980s to the 2000s are retired or have even passed away. Additionally, there are not a lot of young software developers who have heard of people like Larry Tesler, Bill Atkinson, Bruce Tognazzini, Don Norman, and other people who shaped Apple’s UI/UX principles.
4. Speaking of Bruce Tognazzini and Don Norman, I am reminded of this 2015 article (https://www.fastcompany.com/3053406/how-apple-is-giving-desi...) where they criticized Apple’s design as being focused on form over function. It’s only gotten worse since 2015. The saving grace for Apple is that the rest of the industry has gone even further in reducing usability.
I think what it will take for Apple to readopt its perfectionism is if competition forced it to.
What Apple does it build beautiful hardware. The software has been shambles for a really long time.