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Experimenting with Local LLMs on macOS
- mg 1y agoIs anyone working on software that lets you run local LLMs in the browser? In theory, it should be possible, shouldn't it? The page could hold only the software in JavaScript that uses WebGL to run the neural net. And offer an "upload" button that the user can click to select a model from their file system. The button would not upload the model to a server - it would just let the JS code access it to convert it into WebGL and move it into the GPU. This way, one could download models from HuggingFace, store them locally and use them as needed. Nicely sandboxed and independent of the operating system.
- SparkyMcUnicorn 1y agoYes. MLC's inference engine runs on WebGPU/WASM. https://github.com/mlc-ai/web-llm-chat https://github.com/mlc-ai/web-llm-chat https://github.com/mlc-ai/mlc-llm https://github.com/mlc-ai/mlc-llm https://github.com/mlc-ai/web-llm https://github.com/mlc-ai/web-llm
- mg 1y agoYeah, something like that, but without the WebGPU requirement. Neither FireFox nor Chromium support WebGPU on Linux. Maybe behind flags. But before using a technology, I would wait until it is available in the default config. Lets see when browsers will bring WebGPU to Linux.
- SparkyMcUnicorn 1y agoThis should be what you're looking for. It doesn't utilize the GPU, but WebGL support is in the TODOs. https://github.com/ngxson/wllama https://github.com/ngxson/wllama https://huggingface.co/spaces/ngxson/wllama https://huggingface.co/spaces/ngxson/wllama
- simonw 1y agoFirefox Nightly on macOS now supports WebGPU, and the documentation says the Linux build supports it too.
- coip 1y agoHave you seen/used the webGPU spaces? https://huggingface.co/docs/transformers.js/en/guides/webgpu https://huggingface.co/docs/transformers.js/en/guides/webgpu eta: its predecessor was using webGL
- mg 1y agoWebGPU is not yet available in the default config of Linux browsers, so WebGL would have been perfect :)
- samsolomon 1y agoIs Open WebUI something like you are looking for? The design has some awkwardness, but overall it's incorporated a ton of great features. https://openwebui.com/ https://openwebui.com/
- mg 1y agoNo, I'm looking for an html page with a button "Select LLM". After pressing that button and selecting a local LLM from disk, it would show an input field where you can type your question and then it would use the given LLM to create the answer. I'm not sure what OpenWebUI is, but if it was what I mean, they would surely have the page live and not ask users to install Docker etc.
- bravetraveler 1y agoIt's both what you want and not; the chat/question interface is as you describe, lack-of-installation is not. The LLM work is offloaded to other software, not the browser. I would like to skip maintaining all this crap, though: I like your approach
- Jemaclus 1y agoYou should install it, because it's exactly what you just described. Edit: From a UI perspective, it's exactly what you described. There's a dropdown where you select the LLM, and there's a ChatGPT-style chatbox. You just docker-up and go to town. Maybe I don't understand the rest of the request, but I can't imagine a software where a webpage exists and it just magically has LLMs available in the browser with no installation?
- craftkiller 1y agoIt doesn't seem exactly like what they are describing. The end-user interface is what they are describing but it sounds like they want the actual LLM to run in the browser (perhaps via webgpu compute shaders). Open WebUI seems to rely on some external executor like ollama/llama.cpp, which naturally can still be self-hosted but they are not executing INSIDE the browser.
- adastra22 1y agoYou don’t need a browser to sandbox something. Easier and more performant to do GOU pass through to a container or VM.
- 01HNNWZ0MV43FF 1y agoContainer or VM is a bigger commitment. VMs need root and containers need Docker group and something like docker-compose or a shell script or something. idk it's just like, do I want to run to the store and buy a 24-pack of water bottles, and stash them somewhere, or do I want to open the tap and have clean drinking water
- adastra22 1y agoNeither of requirements are true on recent OS versions. Users have had the ability to make containers or VMs without special privileges for a very long time now.
- mudkipdev 1y agoIt was done with gemma-3-270m, I hope someone will post a link to it below
- vavikk 1y agoNot browser but Electron. For the browser you would have to run a local nodejs server and point the browser app to use the local API. I use electron with nodejs and react for UI. Yes I can switch models.
- vonneumannstan 1y agoThis one is pretty cool. Compile the gguf of an OSS LLM directly into an executable. Will open an interface in the browser to chat. Can also launch an OpenAI API style interface hosted locally. Doesn't work quite as well on Windows due to the executable file size limit but seems great for Mac/Linux flavors. https://github.com/Mozilla-Ocho/llamafile https://github.com/Mozilla-Ocho/llamafile
- generalizations 1y agoThis is an in-browser llamacpp implementation: https://github.com/ngxson/wllama https://github.com/ngxson/wllama And related is the whisper implementation: https://ggml.ai/whisper.cpp/ https://ggml.ai/whisper.cpp/
- simonw 1y agoTransformers.js (https://huggingface.co/docs/transformers.js/en/index https://huggingface.co/docs/transformers.js/en/index) is this. Some demos (should work in Chrome and Firefox on Windows, or Firefox Nightly on macOS and Linux): https://huggingface.co/spaces/webml-community/llama-3.2-webgpu https://huggingface.co/spaces/webml-community/llama-3.2-webg... loads a 1.24GB Llama 3.2 q4f16 ONNX build https://huggingface.co/spaces/webml-community/janus-pro-webgpu https://huggingface.co/spaces/webml-community/janus-pro-webg... loads a 2.24 GB DeepSeek Janus Pro model which is multi-modal for output - it can respond with generated images in addition to text. https://huggingface.co/blog/embeddinggemma#transformersjs https://huggingface.co/blog/embeddinggemma#transformersjs loads 400MB for an EmbeddingGemma demo (embeddings, not LLMs) I've collected a few more of these demos here: https://simonwillison.net/tags/transformers-js/ https://simonwillison.net/tags/transformers-js/ You can also get this working with web-llm - https://github.com/mlc-ai/web-llm https://github.com/mlc-ai/web-llm - here's my write-up of a demo that uses that: https://simonwillison.net/2024/Nov/29/structured-generation-smollm2-webgpu/ https://simonwillison.net/2024/Nov/29/structured-generation-...
- mg 1y agoThis might be a misunderstanding. Did you see the "button that the user can click to select a model from their file system" part of my comment? I tried some of the demos of transformers.js but they all seem to load the model from a server. Which is super slow. I would like to have a page the lets me use any model I have on my disk.
- simonw 1y agoOh sorry, I missed that bit. I got Codex + GPT-5 to modify that Llama chat example to implement the "load from local directory" pattern. It appears to work. First you'll need to grab the checkout of the local model (~1.3GB): git lfs install git clone https://huggingface.co/onnx-community/Llama-3.2-1B-Instruct-q4f16 Then visit this page: https://static.simonwillison.net/static/2025/llama-3.2-webgpu/ https://static.simonwillison.net/static/2025/llama-3.2-webgp... - in Chrome or Firefox Nightly. Now click "Browse folder" and select the folder you just checked out with Git. Click the confusing "Upload" confirmation (it doesn't upload anything, just opens those files in the current browser session). Now click "Load local model" - and you should get a full working chat interface. Code is here: https://github.com/simonw/transformers.js-examples/commit/cdebf4128c6e30414d437affd4b13b6c9c79421d https://github.com/simonw/transformers.js-examples/commit/cd... Here's the full Codex session that I used to build this: https://gist.github.com/simonw/3c46c9e609f6ee77367a760b5ca01bd2 https://gist.github.com/simonw/3c46c9e609f6ee77367a760b5ca01... I ran Codex against the https://github.com/huggingface/transformers.js-examples/tree/main/llama-3.2-webgpu https://github.com/huggingface/transformers.js-examples/tree... folder and prompted: > Modify this application such that it offers the user a file browse button for selecting their own local copy of the model file instead of loading it over the network. Provide a "download model" option too. Then later: > Build the production app and then make it available on localhost somehow And: > Uncaught (in promise) Error: Invalid configuration detected: both local and remote models are disabled. Fix by setting `env.allowLocalModels` or `env.allowRemoteModels` to `true`. And: > Add a bash script which will build the application such that I can upload a folder called llama-3.2-webgpu to http://static.simonwillison.net/static/2025/llama-3.2-webgpu/ http://static.simonwillison.net/static/2025/llama-3.2-webgpu... and http://static.simonwillison.net/static/2025/llama-3.2-webgpu/index.html http://static.simonwillison.net/static/2025/llama-3.2-webgpu... will serve the app (Note that this doesn't allow you to use any model on your machine, but it proves that it's possible.)
- paulirish 1y agoBeyond all the wasm/webgpu approaches other folks have linked (mostly in the transformers.js ecosystem), there's been a standardized API brewing since 2019: https://webmachinelearning.github.io/webnn-intro/ https://webmachinelearning.github.io/webnn-intro/ Demos here: https://webmachinelearning.github.io/webnn-samples/ https://webmachinelearning.github.io/webnn-samples/ I'm not sure any of them allow you to select a model file from disk, but that should be entirely straightforward.
- daoboy 1y agoI'm running Hermes Mistral and the very first thing it did was start hallucinating. I recently started an audio dream journal and want to keep it private. Set up whisper to transcribe the .wav file and dump it in an Obsidian folder. The plan was to put a local llm step in to clean up the punctuation and paragraphs. I entered instructions to clean the transcript without changing or adding anything else. Hermes responded by inventing an intereview with Sun Tzu about why he wrote the Art of War. When I stopped the process it apologized and advised it misunderstood when I talked about Sun Tzu. I never mentioned Sun Tzu or even provided a transcript. Just instructions. We went around with this for a while before I could even get it to admit the mistake, and it refused to identify why it occurred in the first place. Having to meticulously check for weird hallucinations will be far more time consuming than just doing the editing myself. This same logic applies to a lot of the areas I'd like to have a local llm for. Hopefully they'll get there soon.
- simonh 1y agoIt’s often been assumed that accuracy and ‘correctness’ would be easy to implement on computers because they operate on logic, in some sense. It’s originality and creativity that would be hard, or impossible because it’s not logical. Science Fiction has been full of such assumptions. Yet here we are, the actual problem is inventing new heavy enough training sticks to beat our AIs out of constantly making stuff up and lying about it. I suppose we shouldn’t be surprised in hindsight. We trained them on human communicative behaviour after all. Maybe using Reddit as a source wasn’t the smartest move. Reddit in, Reddit out.
- smallmancontrov 1y agoPre-training gets you GPT-3, not InstructGPT/ChatGPT. During fine-tuning OpenAI (and everyone else) specifically chose to "beat in" a heavy bias-to-action because a model that just answers everything with "it depends" and "needs more info" is even more useless than a model that turns every prompt into a creative writing exercise. Striking a balance is simply a hard problem -- and one that many humans have not mastered for themselves.
- HankStallone 1y ago
- coffeecoders 1y agoI agree that it's kind of magical that you can download a ~10GB file and suddenly your laptop is running something that can summarize text, answer questions and even reason a bit. The trick is balancing model size vs RAM: 12B–20B is about the upper limit for a 16GB machine without it choking. What I find interesting is that these models don't actually hit Apple's Neural Engine, they run on the GPU via Metal. Core ML isn't great for custom runtimes and Apple hasn't given low-level developer access to the ANE afaik. And then there is memory bandwidth and dedicated SRAM issues. Hopefully Apple optimizes Core ML to map transformer workloads to the ANE.
- GeekyBear 1y ago> Hopefully Apple optimizes Core ML to map transformer workloads to the ANE. If you want to convert models to run on the ANE there are tools provided: > Convert models from TensorFlow, PyTorch, and other libraries to Core ML. https://apple.github.io/coremltools/docs-guides/index.html https://apple.github.io/coremltools/docs-guides/index.html
- ls-a 1y agoI thought Apple MLX can do that if you convert your model using it https://mlx-framework.org/ https://mlx-framework.org/
- GeekyBear 1y agoIt does indeed, and is more modern than Core ML.
- woadwarrior01 1y agoMLX does not support the ANE. https://github.com/ml-explore/mlx/issues/18 https://github.com/ml-explore/mlx/issues/18
- elpakal 1y agoYes it does. That’s just an issue with stale and incorrect information. Here are the docs https://opensource.apple.com/projects/mlx/ https://opensource.apple.com/projects/mlx/
- noja 1y agoI really like On-Device AI on iPhone (also runs on Mac): https://ondevice-ai.app https://ondevice-ai.app in addition to LM Studio. It has a nice interface, with multiple prompt integration, and a good selection of models. Also the developer is responsive.
- LeoPanthera 1y agoBut it has a paid recurring subscription, which is hard to justify for something that runs entirely locally.
- noja 1y agoI am using it without one so far. But if they continue to develop it I will upgrade.
- gazpachotron 1y ago[dead]
- OvidStavrica 1y agoBy far, the easiest (open source/Mac) is with Pico AI Server with Witsy for a front end: https://picogpt.app/ https://picogpt.app/ https://apps.apple.com/us/app/pico-ai-server-llm-vlm-mlx/id6738607769?mt=12 https://apps.apple.com/us/app/pico-ai-server-llm-vlm-mlx/id6... Witsy: https://github.com/nbonamy/witsy https://github.com/nbonamy/witsy ...and you really want at least 48G RAM to run >24B models.
- punitvthakkar 1y agoSo far I've not run into the kind of use cases that local LLMs can convincingly provide without making me feel like I'm using the first ever ChatGPT from 2022, in that they are limited and quite limiting. I am curious about what use cases the community has found that work for them. The example that one user has given in this thread about their local LLM inventing a Sun Tzu interview is exactly the kind of limitation I'm talking about. How does one use a local LLM to do something actually useful?
- segmondy 1y agoThe same way you use a cloud LLM.
- oblio 1y agoI think the point was that for example for programming, people perceive state of the art LLMs as being net positive contributors, at least for mainstream programming languages and tasks, and I guess local LLMs aren't net positive contributors (i.e. an experienced programmer can build the same thing at least as fast when using an LLM).
- segmondy 1y agoI know this is false, DeepSeekv3.1, GLM4.5, KimiK2-0905, Qwen-235B are all solid open models. Last night, I vibed rough 1300 lines of C server code in about an hour. 0 compilation error, ran without errors and got the job done. I want to meet this experienced programmer that can knock out 1300 lines of C code in an hour.
- JumpCrisscross 1y agoI don't think we're anywhere close to running cutting-edge LLMs on our phones or laptops. What may be around the corner is running great models on a box at home. The AI lives at home. Your thin client talks to it, maybe runs a smaller AI on device to balance latency and quality. (This would be a natural extension for Apple to go into with its Mac Pro line. $10 to 20k for a home LLM device isn't ridiculous.)
- bigyabai 1y ago> $10 to 20k for a home LLM device isn't ridiculous. At that point you are almost paying more than the datacenter does for inference hardware.
- JumpCrisscross 1y ago> At that point you are almost paying more than the datacenter does for inference hardware Of course. You and I don't have their economies of scale.
- bigyabai 1y agoThen please excuse me for calling your one-man $10,000 inference device ridiculous.
- JumpCrisscross 1y ago> please excuse me for calling your one-man $10,000 inference device ridiculous It’s about the real price of early microcomputers. Until the frontier stabilizes, this will be the cost of competitive local inference. Not pretending what we can run on a laptop will compete with a data centre.
- simonw 1y agoPlenty of hobbies are significantly more expensive than that.
- a-dub 1y agoollama is another good choice for this purpose. it's essentially a wrapper around llamacpp that adds easy downloading and management of running instances. it's great! also works on linux!
- frontsideair 1y agoOllama adding a paid cloud version made me postpone this post for a few weeks at least. I don't object them to make money, but it was hard to recommend a tool for local usage and make the first instruction to go to settings and enable airplane mode. Luckily llama.cpp has come a long way and was at a point that I could easily recommend as the open source option instead.
- Olshansky 1y ago+1 to LM Studio. Helped build a lot of intuition. Seeing and navigating all the configs helped me build intuition around what my macbook can or cannot do, how things are configured, how they work, etc... Great way to spend an hour or two.
- deepsquirrelnet 1y agoI also like that it ships with some cli tools, including an openai compatible server. It’s great to be able to take a model that’s loaded and open up an endpoint to it for running local scripts. You can get a quick feel for how it works via the chat interface and then extend it programmatically.
- techlatest_net 1y ago[dead]
- jftuga 1y agoI have a macbook air M4 with 32 GB. What LM Studio models would you recommend for: * General Q&A * Specific to programming - mostly Python and Go. I forgot the command now, but I did run a command that allowed MacOS to allocate and use maybe 28 GB of RAM to the GPU for use with LLMs.
- DrAwdeOccarim 1y agoI adore Qwen 3 30b a3b 2507. Pretty easy to write an MCP to let us search the web with Brave API key. I run it on my Macbook Pro M3 Pro 36 GB.
- theshrike79 1y agoWhat are you running it on that lets you connect tools to it?
- DrAwdeOccarim 1y agoLM Studio. I just vibe code the nodeJS code.
- balder1991 1y agoYou’ll certainly find better answers on /r/LocalLlama in Reddit for this.
- frontsideair 1y agoThis is the command probably: sudo sysctl iogpu.wired_limit_mb=184320 Source: https://github.com/ggml-org/llama.cpp/discussions/15396 https://github.com/ggml-org/llama.cpp/discussions/15396
- jerryliu12 1y agoMy main concern with running LLMs locally so far is that it absolutely kills your battery if you're constantly inferencing.
- seanmcdirmid 1y agoIt really does. On the other hand, if you have a power outlet handy, you can inference on the plane even without a net connection.
- floweronthehill 1y agoI believe local llms are the future. It will only get better. Once we get to the level of even last year's state of the art I don't see any reason to use chatgpt/anthropic/other. We don't even need one big model good at everything. Imagine loading a small model from a collection of dozens of models depending on the tasks you have in mind. There is no moat.
- nomel 1y agoSecure/private cloud compute seems to be the obvious future, to me.
- root_axis 1y agoIt's true that local LLMs are only going to get better, but it's not clear they will become generally practical for the foreseeable future. There have been huge improvements to the reasoning and coding capabilities of local models, but most of that comes from refinements to training data and training techniques (e.g. RLHF, DPO, CoT etc), while the most important factor by far remains the capability to reduce hallucinations to comfortable margins using the raw statistical power you get with massive full-precision parameter counts. The hardware gap between today's SOTA models and what's available to the consumer are so massive that it'll likely be at least a decade before they become practical.
- deleted 1y ago[deleted]
- tolerance 1y agoDEVONThink 4’s support for local models is great and could possibly contribute to the software’s enduring success for the next 10 years. I’ve found it helpful for summarizing documents and selections of text, but it can do a lot more than that apparently. https://www.devontechnologies.com/blog/20250513-local-ai-in-devonthink https://www.devontechnologies.com/blog/20250513-local-ai-in-...
- KolmogorovComp 1y agoThe really though spot is finding a good model for your use case. I’ve a 16Gb MB and have been paralyzed by the many options. I’ve settle for a quantisied 14B Qwen for now, but no idea if this is a good idea.
- frontsideair 1y ago14B Qwen was a good choice, but it became outdated a bit and seems like the new version of 4B surpassed it in benchmarks somehow. It's a balancing game, how slow a token generation speed can you tolerate? Would you rather get an answer quick, or wait for a few seconds (or sometimes minutes) for reasoning? For quick answers, Gemma 3 12B is still good. GPT-OSS 20B is pretty quick when reasoning is set to low, which usually doesn't think longer than one sentence. I haven't gotten much use out of Qwen3 4B Thinking (2507) but at least it's fast while reasoning.
- grim_io 1y agoIt's a crazy upside-down world where the Mac Studio M3 Ultra 512GB is the reasonable option among the alternatives if you intend to run larger models at usable(ish) speeds.
- Damogran6 1y agoOddly, my 2013 MacPro (Trashcan) runs LLMs pretty well, mostly because 64Gb of old school RAM is, like, $25.
- frontsideair 1y agoI'm interested in this, my impression was that the newer chips have unified memory and high memory bandwidth. Do you do inference on the CPU or the external GPU?
- Damogran6 1y agoI don't, I'm a REALLY light user. smaller LLMs work pretty well. I used a 40gb LLM and it was _pokey_, but it worked, and switching them is pretty easy. This is a 12 core Xeon with 64Gb RAM...my M4 mini is....okay with smaller LLMs, I have a Ryzen 9 with a RTX3070ti that's the best of the bunch, but none of this holds a candle to people that spend real money to experiment in this field.
- tpae 1y agoCheck out Osaurus - MIT Licensed, native, Apple Silicon–only local LLM server - https://github.com/dinoki-ai/osaurus https://github.com/dinoki-ai/osaurus
- colecut 1y agothank you
- jasonjmcghee 1y agoI think the best models around right now that most people can fit some quantization on their computer if it's a apple silicon Mac or gaming PC would be: For non-coding: Qwen3-30B-A3B-Instruct-2507 (or the thinking variant, depending on use case) For coding: Qwen3-Coder-30B-A3B-Instruct --- If you have a bit more vram, GLM-4.5-Air or the full GLM-4.5
- all2 1y agoNote that Qwen3 and Deepseek are hobbled in Ollama; they cannot use tools as the tool portion of the system prompt is missing. Recommendation: use something else to run the model. Ollama is convenient, but insufficient for tool use for these models.
- theshrike79 1y agoCould you give a recommendation that works instead of saying what doesn't work?
- jokoon 1y agoI am still looking for a local image captioner, any suggestion which are the 3 easiest to use?
- DrAwdeOccarim 1y agoMinstral small 3.2 Q4_K_M and Gemma 3 12b 4 bit are amazing. I run both in LM Studio on a Macbook Pro M3 Pro with 36GB of RAM.
- jokoon 1y agocan I call it from the command line?
- DrAwdeOccarim 1y agoYes. LM Studio acts like an OAi endpoint when you turn the server on.
- TYPE_FASTER 1y agoAlso see https://github.com/Mozilla-Ocho/llamafile https://github.com/Mozilla-Ocho/llamafile.
- lawxls 1y agoWhat is the best local model for cursor style autocomplete/code suggestions? And is there an extension for vs code which can integrate local model for such use?
- kergonath 1y agoI have been playing with the continue.dev extension for vscodium. I got it to work with Ollama and the Mistral models (codestral, devstral and mistral-small). I did not go much further than experimenting yet, but it looks promising, entirely local and mostly open source. And even then, it’s much further than I got with most other tools I tried.
- jus3sixty 1y agoAn awful lot of Monday morning quarterback CEOs are here running their mouths about what Tim Cook should do or what they would do. Chill out with the extremely confident ignorance. Tim Cook brought Apple to a billion dollars in free cash he doesn’t need to ride the hype train. Also let’s not forget they are first and foremost designers of hardware and the arms race is only getting started.
- SLWW 1y agoThe use of the word "emergent" is concerning to me. I believe this to be an... exaggeration of the observed effect. Depending on the perspective and the knowledge of the domain, this might seem to some ad emergent, however we saw equally interesting developments with more complex Markov chaining given the sheer lack of computational resources and time. What we are observing is just another step up that ladder, another angle to enumerate and pick the best token next in the sequence given the information revealed by the proceeding words. Linguistics is all about efficient, lossless data-transfer. While it's "cool" and very surprising.. I don't believe we should be treating it as somewhere between a spell-checker and a sentient being. People aren't simple heuristic models, and to imply these machines are remotely close is woefully inaccurate and will lead to further confusion and disappointment in the future.
- wer232essf 1y ago[flagged]
- wer232essf 1y ago[flagged]
- saagarjha 1y agoYour bot is broken my guy
- wer232essf 1y ago[flagged]
- atentaten 1y agoEvery blog post or article about running local LLMs should include something about which hardware was used.
- frontsideair 1y agoGood point, let me add a quick note.
- linux2647 1y agoUnrelated but I really enjoyed the wavy text effect on “opinions” in the first paragraph
- frontsideair 1y agoThank you, it was the integral part of the whole post!
- curtisszmania 1y ago[dead]
- balder1991 1y agoAs someone who sometimes downloads random models to play around on my 16GB Mac Mini, I like his suggestions of models. I guess these are the best ones for their sizes if you get down to 4 or 5 worth keeping.
- coldtea 1y ago>I also use them for brain-dumping. I find it hard to keep a journal, because I find it boring, but when you’re pretending to be writing to someone, it’s easier. If you have friends, that’s much better, but some topics are too personal and a friend may not be available at 4 AM. I mostly ignore its responses, because it’s for me to unload, not to listen to a machine spew slop. I suggest you do the same, because we’re anthropomorphization machines and I’d rather not experience AI psychosis. It’s better if you don’t give it a chance to convince you it’s real. I could use a system prompt so it doesn’t follow up with dumb questions (or “YoU’Re AbSoLuTeLy CoRrEcT”s), but I never bothered as I already don’t read it. Reads like someone starting to get their daily drinks, already using them for "company" and fun, and saying "I'm not an alcoholic, I can quit anytime".
- anArbitraryOne 1y agoI still don't think MacOS is such a great idea
- cchance 1y ago#1 thing they need to do is open up ANE for developers to properly access