[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-large-language-models":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"64c461d0-6a5a-4e83-a1a3-cfde93450a4d","large language models","large-language-models",4,"大型語言模型（LLM）正從聊天工具走向基礎AI層，牽動模型訓練、推理成本、能力評測、提示工程與可解釋性等議題。這個主題也涵蓋模型安全、企業合作與部署策略，影響產品設計與算力布局。","Large language models are becoming a core layer of AI systems, shaping how teams train, evaluate, prompt, and deploy models. This topic covers model safety, explainability, inference cost, and the business deals that determine who gets access to compute and capability.",[12,21,29,36,43],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"551703cb-117b-45e6-98d0-3f0dfe16e086","ae-llm-adaptive-efficiency-optimization-en","AE-LLM aims to make LLMs more efficient","AE-LLM proposes adaptive efficiency optimization for large language models, but the provided source does not include benchmark details.","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1778051450895-f6re.png","en","2026-05-06T07:10:33.795652+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":26,"image_url":27,"cover_image":27,"language":19,"created_at":28},"c6474b2b-cdcb-4376-9487-18c7945a3dc2","google-plans-40b-bet-on-anthropic-en","Google Plans $40B Bet on Anthropic","Alphabet may invest up to $40 billion in Anthropic, deepening a rival partnership as Google races to secure more AI capacity.","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777334646418-2se7.png","2026-04-28T00:03:38.768168+00:00",{"id":30,"slug":31,"title":32,"summary":33,"category":17,"image_url":34,"cover_image":34,"language":19,"created_at":35},"fd36cdcc-d9b7-4d57-b64d-f89c8ad531a5","mythos-anthropic-unreleased-ai-model-explained-en","Mythos, Anthropic’s unreleased AI model, explained","Anthropic says Mythos is too dangerous to ship. Here’s what its 73% hacking score, 31-point math gain, and limited rollout mean.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776738631321-l0a3.png","2026-04-21T00:03:43.12614+00:00",{"id":37,"slug":38,"title":39,"summary":40,"category":17,"image_url":41,"cover_image":41,"language":19,"created_at":42},"2c255fb7-7404-4166-ba60-19df68a21338","llms-knowledge-graphs-ml-explainability-en","LLMs plus knowledge graphs for ML explainability","A manufacturing XAI method uses a knowledge graph plus an LLM to turn ML results into clearer, more user-friendly explanations.","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1776665388778-cht0.png","2026-04-20T06:09:32.866405+00:00",{"id":44,"slug":45,"title":46,"summary":47,"category":48,"image_url":49,"cover_image":49,"language":19,"created_at":50},"738e7f42-6aac-4342-9cf8-31818fc2c74d","prompt-engineering-explained-without-the-hype-en","Prompt Engineering, Explained Without the Hype","Prompt engineering turns vague requests into usable AI outputs. AWS breaks down the methods, use cases, and tradeoffs behind better prompts.","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775164735997-2du2.png","2026-04-02T21:18:36.566316+00:00"]