「on-device」の検索結果

iOS 26以降のアプリに、端末内で動く言語モデルを組み込むスキル。文章生成・要約、入力からのデータ抽出、独自処理の呼び出し、生成中の画面更新を扱います。

• オフラインの文章生成・要約
• 入力文から項目を抽出したいとき
• AIからアプリ独自の処理を呼ぶ

Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models, Core ML, MLX Swift, or llama.cpp. Use when choosing an Apple-local model runtime, building an Apple Intelligence chatbot or tool-calling feature, running an LLM on Apple Silicon, converting or compressing a Python model for Core ML, or comparing on-device inference backends. For Swift Core ML loading and prediction code, use the coreml skill.

日本語の概要は準備中です。原文の説明を表示しています。

Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.

日本語の概要は準備中です。原文の説明を表示しています。

Run MNN tests / benchmarks on host or real devices. Covers two parallel tracks — (1) the regression / CI suite (static checks, host-side tests, on-device Android arm64 matrix via ./test.sh + test_stages.json) and (2) one-command iOS real-device LLM benchmarking (prefill/decode tok/s, branch comparison). Use when the user asks to run the tests, run CI, smoke-test a build, verify a change on a device, benchmark on-device (Android or iPhone/iPad), or add / select / retune a test stage.

日本語の概要は準備中です。原文の説明を表示しています。

Patterns for running AI models locally in browsers using WebGPU, Transformers.js, WebLLM, and ONNX Runtime. Zero API costs, full privacy. Use when "on-device AI, browser AI, WebLLM, Transformers.js, WebGPU, edge inference, offline AI, client-side ML, ONNX web, " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

日本語の概要は準備中です。原文の説明を表示しています。

Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.

日本語の概要は準備中です。原文の説明を表示しています。

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

日本語の概要は準備中です。原文の説明を表示しています。

Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

日本語の概要は準備中です。原文の説明を表示しています。

Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes, or surface non-obvious bridges/related notes. macOS, on-device.

日本語の概要は準備中です。原文の説明を表示しています。

苹果FoundationModels框架用于设备上的LLM——文本生成、使用@Generable进行引导生成、工具调用，以及在iOS 26+中的快照流。

日本語の概要は準備中です。原文の説明を表示しています。

Apple FoundationModels 框架，用于设备端大语言模型（LLM）—— iOS 26+ 中的文本生成、配合 @Generable 的引导式生成、工具调用以及快照流式传输。

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user mentions Foundation Models review, on-device AI audit, LanguageModelSession issues, or @Generable/guardrail problems — including missing availability checks, manual JSON parsing, and guardrail handling.

日本語の概要は準備中です。原文の説明を表示しています。

Use when implementing, testing, or evaluating ANY Apple Intelligence, on-device AI, or speech-to-text feature. Covers Foundation Models, @Generable, LanguageModelSession, Tool protocol, eval suites, model-as-judge scoring, SpeechTranscriber, CoreML.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with camera, photos, audio, haptics, ShazamKit, on-device music analysis, face grouping and video highlights, screen recording, casting to non-AirPlay routes, SharePlay playback, the user's Apple Music library, lock-screen metadata, or CarPlay app design, templates, and navigation. Covers AVCaptureSession, AVFoundation, Core Haptics, MediaPlayer, MusicKit, NowPlaying, ScreenCaptureKit, DockKit.

日本語の概要は準備中です。原文の説明を表示しています。

Transcribe speech to text using Apple's Speech framework. Use when implementing live microphone transcription with AVAudioEngine, recognizing recorded audio files, handling speech and microphone authorization, choosing on-device vs server-backed SFSpeechRecognizer behavior, or adopting SpeechAnalyzer, SpeechTranscriber, DictationTranscriber, AssetInventory, and async result streams on iOS 26+.

日本語の概要は準備中です。原文の説明を表示しています。

Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.

日本語の概要は準備中です。原文の説明を表示しています。

Generates user-facing usage statistics, activity summaries, and personalized insights dashboards (weekly recaps, year-in-review, Spotify Wrapped-style). Use when user wants to show usage stats, activity insights, or shareable recap screens. Different from analytics-setup which sends data to a backend — this shows insights to the USER on-device.

日本語の概要は準備中です。原文の説明を表示しています。

Apple Intelligence skills for on-device AI features including Foundation Models, Visual Intelligence, App Intents, and intelligent assistants. Use when implementing AI-powered features.

日本語の概要は準備中です。原文の説明を表示しています。

On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.

日本語の概要は準備中です。原文の説明を表示しています。

Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.

日本語の概要は準備中です。原文の説明を表示しています。

Speech-to-text transcription via OpenAI Whisper. Supports two modes — Local CLI (no API key, runs on-device) and Cloud API (fast, scalable, requires OPENAI_API_KEY). Use when the user needs to transcribe audio files, translate speech, or convert audio to text.

日本語の概要は準備中です。原文の説明を表示しています。

Turn long videos & livestream VODs into viral vertical shorts, 100% locally — on-device transcription, LLM highlight detection, 9:16 reframe with karaoke captions, and a per-clip render-QA report. Use when the user asks to clip / cut / 切片 / 剪 a long video, podcast or stream replay into short clips, find highlights / 爆点 in a video, or transcribe a media file. Footage never leaves the machine.

日本語の概要は準備中です。原文の説明を表示しています。

Build, install, and visually verify the app on an Android emulator or device. Uses the Android CLI for agents (android) when available with a full mobile-mcp/adb fallback.

日本語の概要は準備中です。原文の説明を表示しています。