This is just prompting an LLM and just dumping it on the site (which is clearly what is happening here, all the articles show the same signs of AI output, no human writing, no style, as far as I can tell).
If this is the level of care that goes into news articles, then we're doomed. What will ultimately happen is that AI summarizes AI articles, which got summarized from another AI article, which got summarized from another AI article, .. and after enough rewriting all facts will be gone from articles. I don't care to read this slop, and I'm shocked people are so readily accepting this new state of affairs.
This article is all fluff because real benne marketing. If they mentioned that a 4B model on an iPhone 16 drains 15% of the battery for a single long prompt and triggers hard thermal throttling after 20 seconds, nobody would be clicking on headlines about "commercial viability" fwiw
I ran several Gemma 4 quants on my 24gb mac mini, and with proper context size tuning they're quick enough I guess, but I would really love to see them working well on an iphone with 2/3gb of ram...
As someone said we live in a strange but amazing era, where although it has never been easier to be deceived, but its _also_ much easier to uncover said deception especially on the internet.
Or at least think you've uncovered deception. It's not clear to me yet that any of these "AI detectors" are reliable, and if they are, it's just an arms race.
LLM output doesn't have the variety of human output, since they operate in fixed fashion - statistical inference followed by formulaic sampling.
Additionally, the statistics used by LLMs are going be be similar across different LLMs since at scale its just "the statistics of the internet".
Human output has much more variety, partly because we're individuals with our own reading/writing histories (which we're drawing upon when writing), and partly because we're not so formulaic in the way we generate. Individuals have their own writing styles and vocabulary, and one can identify specific authors to a reasonable degree of accuracy based on this.
It's a bit like detecting cheating in a chess tournament. If an unusually high percentage of a player's moves are optimal computer moves, then there is a high likelihood that they were computer generated. Computers and humans don't pick moves in the same way, and humans don't have the computational power to always find "optimal" moves.
Similarly with the "AI detectors" used to detect if kids are using AI to write their homework essays, or to detect if blog posts are AI generated ... if an unusually high percentage of words are predictable by what came before (the way LLMs work), and if those statistics match that of an LLM, then there is an extremely high chance that it was written by an LLM.
Can you ever be 100% sure? Maybe not, but in reality human written text is never going to have such statistical regularity, and such an LLM statistical signature, that an AI detector gives it more than a 10-20% confidence of being AI, so when the detector says it's 80%+ confident something was AI generated, that effectively means 100%. There is of course also content that is part human part AI (human used LLM to fix up their writing), which may score somewhere in the middle.
I noticed the inference is routed through the gpu rather than the Apple neural engine. Google’s engineers likely gave up on trying to compile custom attention kernels for Apple’s proprietary tensor blocks iirc. While Metal is predictable and easy to port to, it drains the battery way faster than a dedicated NPU. Until they rewrite the backend for the ANE, this is just a flashy tech demo rather than a production-ready tool
I made this offline pocket vibe coder using Gemma 4 (works offline once model is downloaded) on an iPhone. It can technically run the 4B model but it will default to 2B because of memory constraints.
It writes a single TypeScript file (I tried multiple files but embedded Gemma 4 is just not smart enough) and compiles the code with oxc.
You need to build it yourself in Xcode because this probably wouldn't survive the App Store review process. Once you run it, there are two starting points included (React Native and Three.js), the UX is a bit obscure but edge-swipe left/right to switch between views.
Unfortunately Apple appears to be blocking the use of these llms within apps on their app store.
I've been trying to ship an app that contains local llms and have hit a brick wall with issue 2.5.2
For those who would like an example of its output, I'm currently working through creating a small, free (cc0, public domain) encyclopedia (just a couple of thousand entries) of core concepts in Biology and Health Sciences, Physical Sciences, and Technology. Each entry is being entirely written by Gemma 4:e4b (the 10 GB model.) I believe that this may be slightly larger than the size of the model that runs locally on phones, so perhaps this model is slightly better, but the output is similar. Here is an example entry:
Strangely, it is super fast on my 16 Plus, but with longer messages it can slow down a LOT, and not because of thermal throttling. I wish I could see some diagnostic data.
I’m pretty excited about the edge gallery ios app with gemma 4 on it but it seems like they hobbled it, not giving access to intents and you have to write custom plugins for web search, etc. Does anyone have a favorite way to run these usefully? ChatMCP works pretty well but only supports models via api.
I just installed Google Ai Edge Gallery on my iPhone 16 pro, here are the results of the first benchmark with GPU, Prefill Tokens=256, Decode Tokens=256, Number of runs: 3. Prefill Speed=231t/s, Decode Speed=16t/s, Time to First Token=1.16s, First init time=20s
Offline or not, I'm sure Google uploads every keystroke, phone orientation, photo, WiFi endpoints and your shoe size when you interact with it. To enhance your experience.
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Threat: JS/Agent.RDW trojan
I feel like UX and API design are very under explored.
What are the possibilities of an Android or iOS device where the OS is centered around a locally running LLM with an API for accessing it from apps, along with tools the LLM can call to access data from locally running apps? What’s the equivalent of the original Mac OS?
Do apps disappear and there’s just a running dialog with the LLM generating graphical displays as needed on demand?
Would love to see a show down of performance on iPhone vs Googles Tensor G5, which in my experience the G5 is 2 full generations behind performance wise.
does anyone know of a decent but low memory or low parameter count multilingual model (as many languages as possible), that can faithfully produce the detailed IPA transcription given a word in a sentence in some language?
I want to test a hypothesis for "uploading" neural network knowledge to a user's brain, by a reaction-speed game.
187 comments
The pattern "It's not mere X — it's Y", occurs like 4 times in the text :v
The problem with the article is the complete lack of details. No benchmarks on the iPhone capable models. No details, whatsoever.
Human or LLM - the article is a whole lot of nothing.
"It's not just X – it's Y." Slop. "You're absolutely right!" Slop. "And this is key –" Slop. "This is a nuanced topic." Slop.
https://www.youtube.com/watch?v=vrP-_T-h9YM
If this is the level of care that goes into news articles, then we're doomed. What will ultimately happen is that AI summarizes AI articles, which got summarized from another AI article, which got summarized from another AI article, .. and after enough rewriting all facts will be gone from articles. I don't care to read this slop, and I'm shocked people are so readily accepting this new state of affairs.
My favorite: couldn't even prove the author is a real person. They all found no record!
> :v
I guess I found the millennial. I haven't seen that in so long!
>_>
At this point relying on their judgement is beyond folly.
Sorry for making you snort and shake your head in amusement :D
https://old.reddit.com/r/ChatGPT/comments/13mft8s/apparently...
LLM output doesn't have the variety of human output, since they operate in fixed fashion - statistical inference followed by formulaic sampling.
Additionally, the statistics used by LLMs are going be be similar across different LLMs since at scale its just "the statistics of the internet".
Human output has much more variety, partly because we're individuals with our own reading/writing histories (which we're drawing upon when writing), and partly because we're not so formulaic in the way we generate. Individuals have their own writing styles and vocabulary, and one can identify specific authors to a reasonable degree of accuracy based on this.
It's a bit like detecting cheating in a chess tournament. If an unusually high percentage of a player's moves are optimal computer moves, then there is a high likelihood that they were computer generated. Computers and humans don't pick moves in the same way, and humans don't have the computational power to always find "optimal" moves.
Similarly with the "AI detectors" used to detect if kids are using AI to write their homework essays, or to detect if blog posts are AI generated ... if an unusually high percentage of words are predictable by what came before (the way LLMs work), and if those statistics match that of an LLM, then there is an extremely high chance that it was written by an LLM.
Can you ever be 100% sure? Maybe not, but in reality human written text is never going to have such statistical regularity, and such an LLM statistical signature, that an AI detector gives it more than a 10-20% confidence of being AI, so when the detector says it's 80%+ confident something was AI generated, that effectively means 100%. There is of course also content that is part human part AI (human used LLM to fix up their writing), which may score somewhere in the middle.
https://github.com/blixt/pucky
It writes a single TypeScript file (I tried multiple files but embedded Gemma 4 is just not smart enough) and compiles the code with oxc.
You need to build it yourself in Xcode because this probably wouldn't survive the App Store review process. Once you run it, there are two starting points included (React Native and Three.js), the UX is a bit obscure but edge-swipe left/right to switch between views.
https://pastebin.com/ZfSKmfWp
Seems pretty good to me!
This is not meant as a criticism, but people should be aware of their limitations.
Threat found This web page may contain dangerous content that can provide remote access to an infected device, leak sensitive data from the device or harm the targeted device. Threat: JS/Agent.RDW trojan
What are the possibilities of an Android or iOS device where the OS is centered around a locally running LLM with an API for accessing it from apps, along with tools the LLM can call to access data from locally running apps? What’s the equivalent of the original Mac OS?
Do apps disappear and there’s just a running dialog with the LLM generating graphical displays as needed on demand?
> edge AI deployment
Isn't the "edge" meant to be computing near the user, but not on their devices?
qwen3-coder-next uses a lot less since it seems to only activate ~3B parameters at a time.
My guess is that this is still close to tech demo, and a lot of performance is left on the table.
I want to test a hypothesis for "uploading" neural network knowledge to a user's brain, by a reaction-speed game.
I think this should be flagged.