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",{"type":30,"tag":108,"props":4907,"children":4909},{"className":4908},[],[4910],{"type":36,"value":4911},"installer.exe",{"type":36,"value":4913}," 之後下載下來，執行安裝器就可以開始。",{"type":30,"tag":116,"props":4915,"children":4917},{"className":4916},[119,120,121,122,123,124,125],[4918,4919,4925,4926],{"type":36,"value":128},{"type":30,"tag":130,"props":4920,"children":4924},{"src":4921,"alt":4922,"className":4923},"\u002Fday_assets\u002Ffancontrol\u002F0.png","Fan Control GitHub Releases 頁面中的 installer.exe 下載項目",[134,135],[],{"type":36,"value":128},{"type":30,"tag":38,"props":4927,"children":4929},{"className":4928},[141,142,143,144,145,146],[4930],{"type":36,"value":4931},"到 Releases 的 Assets 找 installer.exe 下載",{"type":30,"tag":38,"props":4933,"children":4934},{},[4935],{"type":36,"value":4936},"如果你看到 .NET 版本差異，不用太緊張。簡單講，就是有些版本會依賴比較新的 .NET runtime，有些則是給舊環境使用。對大多數 Windows 10 \u002F Windows 11 使用者，我會優先選官方 release 頁上最推薦、最新的 installer；如果開不起來，再回頭看是否需要換 .NET 4.8 版本或補 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installer，因為比較接近一般人第一次嘗試的路線。",{"type":30,"tag":89,"props":5003,"children":5004},{},[],{"type":30,"tag":31,"props":5006,"children":5008},{"id":5007},"第一次打開先切繁體中文",[5009],{"type":36,"value":5007},{"type":30,"tag":38,"props":5011,"children":5012},{},[5013],{"type":36,"value":5014},"Fan Control 功能很多，英文介面不是不能用，但第一次設定時我會建議先到設定裡切語言。設定頁往下滑可以找到語言選項，裡面有繁體中文。",{"type":30,"tag":116,"props":5016,"children":5018},{"className":5017},[119,120,121,122,123,124,125],[5019,5020,5026,5027],{"type":36,"value":128},{"type":30,"tag":130,"props":5021,"children":5025},{"src":5022,"alt":5023,"className":5024},"\u002Fday_assets\u002Ffancontrol\u002F2.png","Fan Control 設定頁面中選擇繁體中文語言",[134,135],[],{"type":36,"value":128},{"type":30,"tag":38,"props":5028,"children":5030},{"className":5029},[141,142,143,144,145,146],[5031],{"type":36,"value":5032},"設定裡可以切換語言，往下找就能看到繁體中文",{"type":30,"tag":38,"props":5034,"children":5035},{},[5036],{"type":36,"value":5037},"切完之後回到主畫面，很多詞會比較好理解，像是控制、速度、曲線、感測器、校正等等。Fan Control 的強項就是把很多底層風扇控制拆成卡片和曲線，如果語言不熟，第一次看會有點像進到一個滿是旋鈕的控制台。",{"type":30,"tag":38,"props":5039,"children":5040},{},[5041],{"type":36,"value":5042},"我自己的習慣是：先不要急著改數值，先讓它掃描硬體，看它抓到哪些風扇控制、哪些 RPM 速度感測器、哪些溫度來源。不同主機板、GPU、風扇集線器和水冷控制器，能抓到的東西會不一樣。",{"type":30,"tag":89,"props":5044,"children":5045},{},[],{"type":30,"tag":31,"props":5047,"children":5049},{"id":5048},"自動校正風扇很重要",[5050],{"type":36,"value":5048},{"type":30,"tag":38,"props":5052,"children":5053},{},[5054],{"type":36,"value":5055},"Fan Control 不是只讓你手動拉百分比。官方文件裡有提到，風扇校正會找出這顆風扇的起轉點、停止點、最大最小速度，並建立 RPM 和百分比之間的關係。這對後面要用 RPM 模式或精準曲線很重要。",{"type":30,"tag":116,"props":5057,"children":5059},{"className":5058},[119,120,121,122,123,124,125],[5060,5061,5067,5068],{"type":36,"value":128},{"type":30,"tag":130,"props":5062,"children":5066},{"src":5063,"alt":5064,"className":5065},"\u002Fday_assets\u002Ffancontrol\u002F3.png","Fan Control 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1776。最明顯的差異是線條比較乾淨，臉部、頭髮、盔甲邊緣也比較能撐住大尺寸顯示。",{"type":30,"tag":116,"props":5736,"children":5738},{"className":5737},[119,120,121,122,123,124,125],[5739,5740,5746,5747],{"type":36,"value":128},{"type":30,"tag":130,"props":5741,"children":5745},{"src":5742,"alt":5743,"className":5744},"\u002Fday_assets\u002Fupscayl\u002FHE-man_upscayl_4x_digital-art-4x.png","使用 Upscayl Digital Art 模型放大 4 倍後的 He-Man 圖片",[134,135],[],{"type":36,"value":128},{"type":30,"tag":38,"props":5748,"children":5750},{"className":5749},[141,142,143,144,145,146],[5751],{"type":36,"value":5752},"Digital Art 模型 4 倍放大後：1800 x 1776",{"type":30,"tag":38,"props":5754,"children":5755},{},[5756],{"type":36,"value":5757},"但我也會提醒一點：AI 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倍後，檔案大小很容易變大，批次跑一整包素材之前最好先試幾張。",{"type":30,"tag":89,"props":5785,"children":5786},{},[],{"type":30,"tag":31,"props":5788,"children":5790},{"id":5789},"我會怎麼使用-upscayl",[5791],{"type":36,"value":5792},"我會怎麼使用 Upscayl",{"type":30,"tag":38,"props":5794,"children":5795},{},[5796],{"type":36,"value":5797},"我會把 Upscayl 放在「素材整理」流程裡，而不是把它當成最後一站。",{"type":30,"tag":38,"props":5799,"children":5800},{},[5801],{"type":36,"value":5802},"例如寫文章時，如果只有一張解析度偏低但內容重要的產品截圖，我會先用 Upscayl 放大，再看是否需要裁切、壓縮或轉成 WebP。做影片縮圖或社群圖時，如果手上只有小圖，也可以先放大再排版。",{"type":30,"tag":38,"props":5804,"children":5805},{},[5806],{"type":36,"value":5807},"但如果圖片本身是模糊照片，尤其是真人人臉，我不會期待它修到像相機重拍。Upscayl 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Apple SpeechAnalyzer API：平台內建 AI 會壓縮 wrapper 型產品空間",{"type":30,"tag":38,"props":48771,"children":48772},{},[48773,48774,48779],{"type":36,"value":48740},{"type":30,"tag":44,"props":48775,"children":48776},{},[48777],{"type":36,"value":48778},"Apple's new SpeechAnalyzer API, benchmarked against Whisper and its predecessor",{"type":36,"value":48780},"。文章把 Apple 新的 SpeechAnalyzer API 拿來和 Whisper 等方案比較，測試轉錄品質與平台整合。",{"type":30,"tag":38,"props":48782,"children":48783},{},[48784],{"type":36,"value":48785},"這類平台 API 很值得注意。過去許多工具是「包一層 Whisper」；一旦作業系統提供足夠好的本地語音 API，簡單 wrapper 的價值就會被壓縮。真正能留下來的產品，需要做更深的 workflow：會議記錄、說話者分離、專業詞彙、隱私策略、搜尋、摘要、剪輯、字幕與跨 app 整合。",{"type":30,"tag":38,"props":48787,"children":48788},{},[48789,48790],{"type":36,"value":48695},{"type":30,"tag":70,"props":48791,"children":48794},{"href":48792,"rel":48793},"https:\u002F\u002Fget-inscribe.com\u002Fblog\u002Fapple-speech-api-benchmark.html",[74],[48795],{"type":36,"value":48796},"Inscribe: Apple SpeechAnalyzer API 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fallback，會影響工具是否值得深度依賴。",{"type":30,"tag":38,"props":48821,"children":48822},{},[48823,48824],{"type":36,"value":48695},{"type":30,"tag":70,"props":48825,"children":48828},{"href":48826,"rel":48827},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uvlwz0\u002Fthis_is_why_we_need_local_models_and_opensource\u002F",[74],[48829],{"type":36,"value":48830},"Reddit: Local models and opensource harnesses",{"type":30,"tag":31,"props":48832,"children":48834},{"id":48833},"_6-企業轉向中國-open-weight-模型成本壓力正在改變模型選擇",[48835],{"type":36,"value":48836},"6. 企業轉向中國 open-weight 模型：成本壓力正在改變模型選擇",{"type":30,"tag":38,"props":48838,"children":48839},{},[48840],{"type":36,"value":48841},"LocalLLaMA 討論 Financial Times 報導：企業為了降低成本，開始轉向中國 open-weight models。另一則則提到 Zhipu founder 支持 open-source AI，並把開源 AI 放到全球安全辯論裡。",{"type":30,"tag":38,"props":48843,"children":48844},{},[48845],{"type":36,"value":48846},"這條線其實很重要。企業採用模型時，最後不只看 benchmark，也看總成本、部署自由度、資料邊界、供應商風險與政治\u002F合規壓力。open-weight 模型讓企業可以把推理放到自己的 infrastructure 上，但也會引發出口管制、安全治理與國際競爭的拉扯。",{"type":30,"tag":38,"props":48848,"children":48849},{},[48850,48851,48858,48860],{"type":36,"value":48695},{"type":30,"tag":70,"props":48852,"children":48855},{"href":48853,"rel":48854},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uvenf1\u002Fft_companies_turn_to_chinese_open_weight_models\u002F",[74],[48856],{"type":36,"value":48857},"Reddit: Companies turn to Chinese open weight models",{"type":36,"value":48859},"；",{"type":30,"tag":70,"props":48861,"children":48864},{"href":48862,"rel":48863},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uvbgvx\u002Fzhipu_founder_backs_opensource_ai_as_global\u002F",[74],[48865],{"type":36,"value":48866},"Reddit: Zhipu founder backs open-source AI",{"type":30,"tag":31,"props":48868,"children":48870},{"id":48869},"_7-e-waste-gpu-與-dual-rtx-6000本地-ai-的硬體路線正在分岔",[48871],{"type":36,"value":48872},"7. e-waste GPU 與 Dual RTX 6000：本地 AI 的硬體路線正在分岔",{"type":30,"tag":38,"props":48874,"children":48875},{},[48876,48878,48883],{"type":36,"value":48877},"HN 與 LocalLLaMA 都在討論 ",{"type":30,"tag":44,"props":48879,"children":48880},{},[48881],{"type":36,"value":48882},"benchmarking e-waste GPUs with modern workloads",{"type":36,"value":48884},"。有人測試退役 NVIDIA enterprise GPUs，想知道 P100、V100 這類便宜卡在現代 workload 中還有沒有價值。另一則 LocalLLaMA 分享 dual RTX 6000 跑 vLLM \u002F DeepSeek V4 Flash 的配置經驗。",{"type":30,"tag":38,"props":48886,"children":48887},{},[48888],{"type":36,"value":48889},"這代表本地 AI 硬體正在分成兩條路：一條是撿便宜 VRAM，用退役卡和舊平台把成本壓到最低；另一條是高階 workstation，用大 VRAM 和更穩定的驅動\u002F散熱換取可靠性。沒有單一正解，取決於你要的是實驗、長上下文、多人服務、coding agent，還是穩定 production-like serving。",{"type":30,"tag":38,"props":48891,"children":48892},{},[48893,48894,48901,48902],{"type":36,"value":48695},{"type":30,"tag":70,"props":48895,"children":48898},{"href":48896,"rel":48897},"https:\u002F\u002Fesologic.com\u002Fbenchmarking-tesla-gpus\u002F",[74],[48899],{"type":36,"value":48900},"Esologic: Benchmarking e-waste GPUs",{"type":36,"value":48859},{"type":30,"tag":70,"props":48903,"children":48906},{"href":48904,"rel":48905},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uvmon5\u002Fjoined_the_dual_rtx_6000_club\u002F",[74],[48907],{"type":36,"value":48908},"Reddit: Dual RTX 6000 setup",{"type":30,"tag":31,"props":48910,"children":48912},{"id":48911},"_8-fp4-attention-kernelscolibri-hy3-與-macbook-glm推理效率繼續靠底層工程推進",[48913],{"type":36,"value":48914},"8. FP4 attention kernels、Colibri Hy3 與 MacBook GLM：推理效率繼續靠底層工程推進",{"type":30,"tag":38,"props":48916,"children":48917},{},[48918],{"type":36,"value":48919},"LocalLLaMA 今天也有多則推理效率消息：B300 的 FP4 attention kernels 宣稱最高比 FA4 快 1.69x；Colibri streaming 被 port 到 Hy3，讓 Hy3 能在約 10GB RAM\u002FVRAM 級別運行；也有人分享 GLM 5.2 在 48GB RAM MacBook Pro M5 上以約 2 到 2.8 tok\u002Fs 跑起來。",{"type":30,"tag":38,"props":48921,"children":48922},{},[48923],{"type":36,"value":48924},"這些看起來零散，其實都在處理同一個問題：怎麼把更大的模型塞進更小、更便宜、更可控的硬體裡。模型能力之外，attention kernel、streaming、MoE loading、KV cache、RAM\u002FVRAM 分工才是本地 AI 真正可用的關鍵。",{"type":30,"tag":38,"props":48926,"children":48927},{},[48928,48929,48936,48937,48944,48945],{"type":36,"value":48695},{"type":30,"tag":70,"props":48930,"children":48933},{"href":48931,"rel":48932},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uvtf7h\u002Fnew_set_of_fp4_attention_kernels_for_b300\u002F",[74],[48934],{"type":36,"value":48935},"Reddit: FP4 attention kernels for B300",{"type":36,"value":48859},{"type":30,"tag":70,"props":48938,"children":48941},{"href":48939,"rel":48940},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uv8orn\u002Fcolibri_streaming_for_hy3_run_hy3_on_10gb_vram\u002F",[74],[48942],{"type":36,"value":48943},"Reddit: Colibri streaming for Hy3",{"type":36,"value":48859},{"type":30,"tag":70,"props":48946,"children":48949},{"href":48947,"rel":48948},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uvlhxl\u002Fglm_52_running_on_macbook_pro_m5_48_gb_ram_at\u002F",[74],[48950],{"type":36,"value":48951},"Reddit: GLM 5.2 on MacBook Pro M5",{"type":30,"tag":31,"props":48953,"children":48955},{"id":48954},"_9-gemma-4-跑進-godotllm-runtime-正往應用內嵌移動",[48956],{"type":36,"value":48957},"9. Gemma 4 跑進 Godot：LLM runtime 正往應用內嵌移動",{"type":30,"tag":38,"props":48959,"children":48960},{},[48961],{"type":36,"value":48962},"LocalLLaMA 有人展示把 Gemma 4 直接跑在 Godot 裡，只用 GDScript 和 Vulkan compute shaders，不依賴 llama.cpp、Python、server 或 GDExtension。作者也說這仍是實驗，重點是 GGUF loading、tokenization、sampling、KV cache 和 chat UI 都放進 Godot 專案。",{"type":30,"tag":38,"props":48964,"children":48965},{},[48966],{"type":36,"value":48967},"這是一個有趣方向：LLM 不一定永遠以 server 或 CLI 形式存在，也可能被嵌進遊戲引擎、創作工具、教育 app 或離線桌面應用。短期性能和維護成本會很硬，但長期來看，local inference runtime 進入應用內部，會讓 AI 功能更像原生能力，而不是外掛式 API。",{"type":30,"tag":38,"props":48969,"children":48970},{},[48971,48972],{"type":36,"value":48695},{"type":30,"tag":70,"props":48973,"children":48976},{"href":48974,"rel":48975},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uv66by\u002Fi_got_gemma_4_running_directly_inside_godot_using\u002F",[74],[48977],{"type":36,"value":48978},"Reddit: Gemma 4 inside Godot",{"type":30,"tag":31,"props":48980,"children":48982},{"id":48981},"_10-self-hosted-voice-for-agents語音成為-agent-harness-的下一層介面",[48983],{"type":36,"value":48984},"10. Self-hosted voice for agents：語音成為 agent harness 的下一層介面",{"type":30,"tag":38,"props":48986,"children":48987},{},[48988,48990,48995,48997,49003],{"type":36,"value":48989},"LocalLLaMA 有一則 ",{"type":30,"tag":44,"props":48991,"children":48992},{},[48993],{"type":36,"value":48994},"Self-hosted voice for any agent\u002Fharness of your choice",{"type":36,"value":48996},"，作者提到維護 ",{"type":30,"tag":108,"props":48998,"children":49000},{"className":48999},[],[49001],{"type":36,"value":49002},"tts-bench",{"type":36,"value":49004}," 和 blind voting arena，並希望讓 agent 在完成任務後能用自架語音通知使用者，甚至打電話。",{"type":30,"tag":38,"props":49006,"children":49007},{},[49008],{"type":36,"value":49009},"這個方向和前幾天的語音模型新聞接得上。當 agent 可以長時間跑任務，使用者不會一直盯著終端；語音通知、任務完成回報、口語摘要、call-back workflow 會變得實用。重點仍然是自架、可替換、低延遲與隱私，不是把所有語音資料再丟回雲端。",{"type":30,"tag":38,"props":49011,"children":49012},{},[49013,49014],{"type":36,"value":48695},{"type":30,"tag":70,"props":49015,"children":49018},{"href":49016,"rel":49017},"https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1uvq9ke\u002Fselfhosted_voice_for_any_agentharness_of_your\u002F",[74],[49019],{"type":36,"value":49020},"Reddit: Self-hosted voice for agents",{"type":30,"tag":31,"props":49022,"children":49024},{"id":49023},"_11-doomqlgpt-56-sol-協助做出-sqlite-遊戲引擎實驗",[49025],{"type":36,"value":49026},"11. 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