{"slug": "core-ml", "name": "Core ML", "category": "dev-libs", "category_name": "Libraries and frameworks", "description": "", "recommenders": 2, "commenters": 4, "warners": 0, "score": 2.477, "rising": 0.67, "first": "2026-06-08", "latest": "2026-10-14", "why": "I use CoreML for models designed for other platforms. I port the models to it but it works for that without much trouble.", "why_is_quote": true, "has_verdict": false, "sentiment": {"recommend": 2, "neutral": 2, "compare": 0, "warn": 0}, "spark": [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1], "listed": false, "loved": [], "caveat": "", "short_quote": "I use CoreML for models designed for other platforms. 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There\u2019s a lot beyond LLMs. I suggest trying!", "context": "", "use_case": "running ML models", "switched_from": "", "story": "We turned EmbeddingGemma 2 into a speech-to-text model"}, {"id": 775316176, "item_id": 48457261, "url": "https://news.ycombinator.com/item?id=48457261", "by": "wahnfrieden", "date": "2026-06-08", "stance": "recommend", "quote": "I use CoreML for models designed for other platforms. I port the models to it but it works for that without much trouble.", "excerpt": "I use CoreML for models designed for other platforms. I port the models to it but it works for that without much trouble. MLX is not for end user deployment.", "context": "", "use_case": "running ported models on Apple", "switched_from": "", "story": "Apple Core AI Framework"}]}], "caveats": [], "who": [], "tags": [], "use_cases": ["running ml models", "production mobile ml", "on-device model inference", "running ported models on apple"], "compared": [{"slug": "mlx", "name": "MLX", "co": 2, "switched_from": 0, "switched_to": 0}], "makers": [], "threads": [{"url": "https://news.ycombinator.com/item?id=50069971", "title": "We turned EmbeddingGemma 2 into a speech-to-text model", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=49469786", "title": "Benchmarking Pocket-Scale Inference", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=49373456", "title": "Show HN: I trained a 125M model to autocomplete piano on-device", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=48449665", "title": "Apple Core AI Framework", "mentions": 1}], "sentiment_by_month": {"2025-11": {}, "2025-12": {}, "2026-01": {}, "2026-02": {}, "2026-03": {}, "2026-04": {}, "2026-05": {}, "2026-06": {"recommend": 1}, "2026-07": {}, "2026-08": {"neutral": 2}, "2026-09": {}, "2026-10": {"recommend": 1}}, "quotes": [{"id": 801343296, "item_id": 50083956, "url": "https://news.ycombinator.com/item?id=50083956", "by": "whstl", "date": "2026-10-14", "stance": "recommend", "quote": "It is very suitable and very straightforward for a crazy large amount of models!", "excerpt": "It is very suitable and very straightforward for a crazy large amount of models! I haven\u2019t even mentioned the ones we use at work! There\u2019s a lot beyond LLMs. I suggest trying!", "context": "", "use_case": "running ML models", "switched_from": "", "story": "We turned EmbeddingGemma 2 into a speech-to-text model"}, {"id": 791934240, "item_id": 49495890, "url": "https://news.ycombinator.com/item?id=49495890", "by": "HawtAds", "date": "2026-08-30", "stance": "neutral", "quote": "traditional transfer learning like Apple's Core.ML", "excerpt": "The benchmark is nice but it's very much biased towards flagships i.e. not very useful in practice if you are trying to ship production mobile apps. Apple historically is extremely stingy when it comes to RAM and they never bothered giving iPads and iPhones more ram until fairly recently (most likely because of ML demands). Your covid era 10th Gen iPads only have 4GB of RAM for the base models. The Android ecosystem ", "context": "", "use_case": "production mobile ML", "switched_from": "", "story": "Benchmarking Pocket-Scale Inference"}, {"id": 790000352, "item_id": 49375022, "url": "https://news.ycombinator.com/item?id=49375022", "by": "simedw", "date": "2026-08-20", "stance": "neutral", "quote": "I\u2019m using Core ML", "excerpt": "The biggest speed improvement came from changing the note representation when I switched to compound note events: roughly 5\u00d7 fewer autoregressive passes per note. For the current model I\u2019m using Core ML, which optimizes the kernels the first time you run it. 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