Show HN: Apfel – The free AI already on your Mac

The big question is whether Apple can keep shipping new models constantly.

AFAIK the current model is on par with with Qwen-3-4B, which is from a year ago [0]. There's a big leap going from last year Qwen-3-4B to Qwen-3.5-4B or to Gemma 4.

Apple model is nice since you don't need to download anything else, but I'd rather use the latest model than to use a model from a year ago.

https://machinelearning.apple.com/research/apple-foundation-...

I like the approach of running everything locally. I'm strongly of the opinion that the privacy angle for local models is going to keep getting stronger and more relevant. The amount of articles that come out about accidents happening because of people handing too much context to cloud models the more self reinforcing this will become.

I’ve seen several projects like this that offer a network server with access to these Apple models. The danger is when they expose that, even on a loop port, to every other application on your system, including the browser. Random webpages are now shipping with JavaScript that will post to that port. Same-origin restrictions will stop data flow back to the webpage, but that doesn’t stop them from issuing commands to make changes.

Some such projects use CORS to allow read back as well. I haven’t read Apfel’s code yet, but I’m registering the experiment before performing it.

Now this is a development I like.

With the Claude bug, or so it is known, burning through tokens at record speed, I gave alternative models a try and they're mostly ... interchangeable. I don't know how easy switching and low brand loyalty and fast markets will play out. I hope that local LLMs will become very viable very soon.

Thank you for making it open source!

Submitted a PR to prevent its installation on macos versions older than Tahoe(26), since I was able to install it on my older macos 15, but it aborted on execution.

https://github.com/Arthur-Ficial/homebrew-tap/pull/1

Started using this earlier this week. I built a backtesting benchmark tool to compare a mix of frontier and open-source models on a fairly heavy data analysis workflow I’d been running in the cloud.

The task is basically predicting pricing and costs.

Apple’s model came out on top—best accuracy in 6 out of 10 cases in the backtest. That surprised me.

It also looks like it might be fast enough to take over the whole job. If I ran this on Sonnet, we’re talking thousands per month. With DeepSeek, it’s more like hundreds.

So far, the other local models I’ve tried on my 64GB M4 Max Studio haven’t been viable - either far too slow or not accurate enough. That said, I haven’t tested a huge range yet.

I'm a Linux user who wanted exactly this but for Linux — so I ended up building it myself. It's called TalkType, it runs Whisper locally for offline speech-to-text. The privacy angle was a big reason I went local from the start — I didn't want my voice being sent to anyone's server. Nice to see the same idea getting traction on Mac.

Tempted to write a grammarly-like underline engine that flags writing mistakes across all apps and browser. Fully private grammarly alternative without even bundling an LLM!

Local AIs are the future in times of limited resources. This could be the beginning of something big. I like that Apple opens up like this. Hopefully more to come.

AFM models are very impressive, but they’re not made for conversation, so keep your expectations down in chat mode.

> Apple locked it behind Siri. apfel sets it free

This doesn't feel truthful, it sounds like this tool is a hack that unlocks something. If I understand it correctly, it's using the same FoundationModels framework that powers Apple Intelligence, but for CLI and OpenAI compatible REST endpoint. Which is fine, just the marketing goes hard a bit.

> Runs on Neural Engine

Also unsure if this runs on ANE, when I tried Apple Intelligence I saw that it ran on the GPU (Metal).

1. Hugely non-deterministic: repeat queries give vastly different responses. 2. Often returns incorrect and inconsistent results even for mathematical queries. 3. Often the responses include unwanted highlighting or presentation markup. 4. Defaults to German decimal notation.

> Referenced from: <32818E2F-CB45-3506-A35B-AAF8BDDFFFCE> /opt/homebrew/Cellar/apfel/0.6.25/bin/apfel (built for macOS 26.0 which is newer than running OS)

This actually looks really neat. I'll have to bookmark this for whenever I'm dragged kicking and screaming into the abomination that is "Tahoe."

Interesting. How does this foundational model compares with other LLMs?

I hacked this together last fall to let you use Apple Foundation Models with llm: https://github.com/btucker/llm-apple . To enable that I built python bindings with Claude Code: https://github.com/btucker/apple-foundation-models-py

Unfortunately, I found the small context window makes the utility pretty limited.

Nice! The example should imo say

apfel -o json "Translate to German: apple" | jq .content

This is great. A few questions come to mind, I need to go look up. Is the model an OpenAI one or home grown for Apple. And can I still use it if Siri is disabled?

Just a small thing about the website: your examples shift all the elements below it on mobile when changing, making it jump randomly when trying to read.

Would really love to see a web api standard for on device llms. This could get us closer. Some in-browser language model usage could be very powerful. In the interim maybe a little protocol spec + a discovery protocol used with browser plugins, web apps could detect and interface with on-device llms making it universally available.

As an experiment I built a prototype chatbot app that uses the built-in LLM. It’s got a small context window, but is surprisingly capable and has tool-calling support. Without too much effort I was able to get it to fetch weather data, fetch and summarise emails, read and write reminders and calendar events.

This is similar to something I was playing around with last month-- basically just a CLI for accessing the foundational models.

https://github.com/ehamiter/afm

It's really handy for quick things like "what's the capital of country x" but for coding, I feel that it is severely limited. With such a small context it's (currently) not great for complicated things.

love the simple website and typography. AI design or you? tasteful and fast animations. nice work and thanks for sharing!

Just discovered iOS shortcuts has a native action called “use model” that lets you use local, Apple cloud, or ChatGPT— before that I would have agreed with the author about being locked behind Siri (natively)

For those who don't know, 'Apfel' is the German word for Apple.

What's the easiest way to use it with on-device voice model for voice chat?

4,096 token context window is pretty limiting. That's roughly 3,000 words — fine for "summarize this paragraph" but not enough for anything that needs real context. Still, zero cost and fully local is hard to beat for quick throwaway tasks. Does it handle streaming or is it request-response only?

Anyone tried using this as a sub-agent for a more capable model like Claude/Codex?

Does the local LLM have access to personal information from the Apple account associated with the logged-in user? Maybe through a RAG pipeline or similar? Just curious if there are any risks associated with exposing this in a way that could be exploited via CORS or through another rogue app querying it locally.

This is cool!

I have been using Apple’s built-in system LLM model for the last 7 or 8 months. I like the feature that if it needs to, it occasionally uses a more powerful secure private cloud model. I also write my own app to wrap it.

How does this model compare against other local models like Qwen run through LMStudio?

Is this for Tahoe only? I’m still clutching onto Sequoia

Any know if these only installed on Tahoe? I'm running Sequoia still and get an error about model not found.

On a similar bent, I recently discovered Handy (cross-platform) which is very well implemented local voice input: https://handy.computer/ ... serious finger saver and ideal for LLM conversations

Digging into this, found Apple’s release notes for the Foundation Model Service

https://developer.apple.com/documentation/Updates/Foundation...

They released an official python SDK in March 2026:

https://github.com/apple/python-apple-fm-sdk

Looks like a nice wrapper around the APIs. Extremely oversold landing page, very marketing heavy for what it is. You can actually make nice looking landing pages that are about 10% the size of this and more straightforward, rather than some mimicry of a SaaS that's trying desperately to sell you something. Makes it easier for you to review the content for factuality too, and heck you couldn't even take ownership of some of the voice.

Hard to know what to do with this. I'm interested in the project and know others who would be, but I feel like shit after being slopped on by a landing page and I don't wish to slop on my friends by sharing it with them. I suppose the github link is indeed significantly better, I'll share that.

It’s a very small model but I’ve been playing with it for some time now I’m impressed. Have we been sleeping on Apple’s models?

Imagine they baked Qwen 3.5 level stuff into the OS. Wow that’d be cool.

I like the idea and the clarity to explain the usage, my question would be: what kind of tasks it would be useful for?

Really like demo cli tools description. Are they limited by the context window as well? What’s your experience with log file sizes?

How much storage does it take up?

  apfel "How many shopping days until Christmas?"

  apfel "How many shopping days until Christmas?  Today is April 3rd."

So, there are 0 shopping days left until Christmas, but since Christmas is already past, there are no shopping days left.

Yep, feels like Siri.

Tahoe+ only

You have to enable Apple Intelligence so that's a hard no from me. I'll stick to LM Studio and gpt-oss/qwen. Very cool project though.

BoltAI also does this, but a CLI tool is nice.

It’s a nice LLM because it seems fairly decent and it loads instantly and uses the CPU neural engine. The GPU is faster but when I run bigger LLMs on the GPU the normally very cool M series Mac becomes a lap roaster.

It’s a small LLM though. Seems decent but it’s also been safety trained to a somewhat comical degree. It will balk over safety at requests that are in fact quite banal.

This is pretty cool. My bet is that we have more LLMs running locally when its possible, either thru "better hardware as default" or some new tech that can run the models on commodity hardware (like apple silicon / equivalent PC setup).

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