[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-大型語言模型":3},{"tag":4,"articles":9,"peer_article_count":77},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":8},"2c3f28d6-70b5-47a0-978a-9eb0f6320346","大型語言模型",1,null,[10,19,26,33,41,48,55,63,70],{"id":11,"slug":12,"title":13,"summary":14,"category":15,"image_url":16,"cover_image":16,"language":17,"created_at":18},"c1d71ae5-dabd-4778-8326-7645316004c2","meta-replacing-moderators-with-ai-to-cut-costs-zh","Meta 用 AI 取代審核員，省錢先上","Meta 正把內容審核交給大型語言模型，部分類別的人工作業可能砍掉 90% 以上。這篇整理它怎麼省錢、風險在哪裡，以及和其他平台的差異。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782653576451-arn6.png","zh","2026-06-28T13:32:29.737246+00:00",{"id":20,"slug":21,"title":22,"summary":23,"category":15,"image_url":24,"cover_image":24,"language":17,"created_at":25},"84609d0a-d6a7-4228-a5cc-e1170725e28e","llama-cpp-vs-vllm-benji-mo-xing-yin-qing-zen-me-xuan-zh","llama.cpp vs vLLM：本機模型引擎怎麼選","這篇比較 llama.cpp 和 vLLM，幫你判斷是要用 CPU 友善、適合單人本機推理的方案，還是適合多使用者、高併發服務的 GPU 推理引擎。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1782087478586-22tr.png","2026-06-22T00:17:31.282164+00:00",{"id":27,"slug":28,"title":29,"summary":30,"category":15,"image_url":31,"cover_image":31,"language":17,"created_at":32},"d1218662-3c24-4bd5-8fdd-826164864369","peft-vs-full-fine-tuning-zh","PEFT vs 全量微調","PEFT 適合多數大型語言模型微調情境，全量微調則適合需要深度改動模型行為的少數案例。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780603379788-d2wm.png","2026-06-04T20:02:31.805871+00:00",{"id":34,"slug":35,"title":36,"summary":37,"category":38,"image_url":39,"cover_image":39,"language":17,"created_at":40},"4bdcf208-fb80-484e-b4b6-06af035a6df1","modulate-aws-voice-chats-into-signals-zh","Modulate 用 AWS 把語音聊天做成訊號","我拆 Modulate 的 AWS 架構，整理成台灣開發者可直接抄的語音分析管線、排隊策略與模板。","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780519733892-rxue.png","2026-06-03T20:48:22.697917+00:00",{"id":42,"slug":43,"title":44,"summary":45,"category":15,"image_url":46,"cover_image":46,"language":17,"created_at":47},"29576246-18ce-4b8a-99f4-8fa36a5099c4","7-minimax-models-for-agentic-apps-zh","7 個 MiniMax 模型選擇","7 個 MiniMax 模型一次看懂，從代理式應用、語音到影像與影片，快速判斷哪一款最適合你的產品。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780452189029-ht1c.png","2026-06-03T02:02:41.252112+00:00",{"id":49,"slug":50,"title":51,"summary":52,"category":38,"image_url":53,"cover_image":53,"language":17,"created_at":54},"a55669fd-8a18-48ec-ad5e-470295c2eb35","lora-fine-tuning-llms-practical-zh","LoRA 讓 LLM 微調更實用","LoRA 只訓練小型 adapter，就能微調 LLM，省下 VRAM、時間與成本，讓中小團隊也能玩得起。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780220882507-izhl.png","2026-05-31T09:47:33.881489+00:00",{"id":56,"slug":57,"title":58,"summary":59,"category":60,"image_url":61,"cover_image":61,"language":17,"created_at":62},"d077afc5-6593-4e0f-afbf-b12229d083b6","what-large-language-models-are-how-they-work-zh","大型語言模型是什麼，怎麼運作","大型語言模型把海量文字學成可預測 Token 的系統，能寫作、摘要、翻譯，也會胡說八道。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1779341172184-vgjc.png","2026-05-21T05:25:42.974116+00:00",{"id":64,"slug":65,"title":66,"summary":67,"category":15,"image_url":68,"cover_image":68,"language":17,"created_at":69},"365f007a-340b-42cc-9f3c-0fd3db6b3ff0","why-small-language-models-should-replace-llm-first-enterpris-zh","為什麼企業 AI 應該先用小型語言模型，而不是 LLM 優先","企業 AI 的預設架構應該是小型語言模型，而不是大型 LLM，因為多數工作更便宜、更快，也更容易控管風險。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778461848994-64df.png","2026-05-11T01:10:23.524005+00:00",{"id":71,"slug":72,"title":73,"summary":74,"category":60,"image_url":75,"cover_image":75,"language":17,"created_at":76},"838cb5fd-5651-49fb-9b4c-c2dbde25ca02","claude-opus-45-gpt-parameters-estimate-zh","Claude Opus 4.5 和 GPT 到底多大","GPT-4 常被估到 1.6 兆參數，但 GPT-4o 可能只有 200B 到 300B。Claude Opus 4.5 的真實大小沒公開，重點其實是成本、延遲和效能比。","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775207388141-adee.png","2026-04-03T09:09:28.833454+00:00",0]