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Building the Apple Silicon Supercomputer. Add your Mac to the AI compute network and earn MLX Tokens. https://teapawt.org/t.co/NNl39AOq37

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Joined October 2025
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AI Tool of the Day: Meshy by @MeshyAI. Turn a prompt, an image or a few phone photos into a textured 3D model, then rig it, animate it or get it ready for a 3D printer. Free plan with 100 credits a month; paid plans from $20 a month. Models are made on Meshy's servers, not on your Mac.
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for scale: 1M output tokens from a 35B MoE on a base mac mini (~40 tok/s) is about 7 hours and roughly 7 cents of electricity. it won't port Battlefield as well as Claude does yet, but that gap is closing faster than the price is
Decompiling Battlefield 1942 and then porting it to C and then WASM cost me 1 million output tokens At API prices that's about $100 But I pay $100/mo Claude
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every "how do i run AI on my mac" thread ends with 12 different answers. so they're all in one place now: what each app does, what it costs, and whether your data stays on the Mac. no paid spots
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replies under every mac rumour: "waiting for the M7" replies from the local AI crowd: "cool but how much memory"
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2024: will it run on my mac? 2026: which of my macs should it run on?
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MLX Harness (built on vMLX by JANGQ AI) lets you review and revert the file edits its agents make. Local AI on your Mac, free. Install it from the Apps page in MLX Node: harness.mlx.app/?utm_source=…
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He's right about the 5%. We'd just rather put the other 95% to work than move it all into a data centre.
WAIT… I can’t believe Sam Altman really said this out loud about local AI: “A lot of people want local inference. And they assume that the problem is solved now because we are going to have open source models. And so we're not going to need all the stuff in the data center. But from a global allocation perspective, actually, it's quite terrible to have everybody have a bunch of the world's limited supply of DRAM sitting in their own computer. And, being used 5% of the time.” this is such a bizarre thing to say in the open. I don’t get why people buying hardware for their own use is framed as a competition with data centers spending billions to buy hardware at scale. if anyone should be complaining about how the world’s memory is allocated, it must be the average consumers facing the skyrocketing hardware prices. it just doesn’t make any sense.
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MLX Node lets a Mac lend its idle hours to an AI network, within limits its owner sets. Why idle Macs make sense as compute: mlx.app/learn/apple-minions-…
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MLX Node gives a Mac's spare hours to an AI network, within limits you set. Free, any Apple silicon Mac. Setup takes a few minutes: mlx.app/learn/start-earning-…
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MLX.app retweeted
This is the new M6 MacBook Pro! 🔥 Would you buy one?
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He kept the box. You kept the brains. MLX Node runs network test inference on an awake, plugged-in Mac. Your hours. Your limits. Download free → mlx.app/download?utm_source=…
🤖 Made with AI
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Brush. Charge. Press Start. Stop whenever you choose. Short 10/10 — Ritual. MLX Node runs network test inference on an awake, plugged-in Mac. Your hours. Your limits. Download free → mlx.app/download?utm_source=…
🤖 Made with AI
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His biggest fan has four legs. Short 09/10 — The Final. A fictional match against doing nothing. MLX Node runs network test inference on an awake, plugged-in Mac. You set the limits and choose when to start. Download free → mlx.app/download?utm_source=…
🤖 Made with AI
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Infrastructure. Meet houseplant. Short 08/10 — Small Talk. A fictional conversation between a data center and a Mac. Put spare Mac time to work with MLX Node. Network test inference; you choose when it runs. Download free → mlx.app/download?utm_source=…
🤖 Made with AI
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For once, doing nothing made the news. Short 07/10 — Breaking News. A fictional bulletin for very real idle Macs. Local chat, coding agents and APIs: MLX Harness → harness.mlx.app/?utm_source=… Network test inference: MLX Node → mlx.app/download?utm_source=…
🤖 Made with AI
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Before downloading a local model, check runtime support for that exact architecture/format and memory for weights, context and other apps. Weight size alone isn't the memory bill.
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