claude codeã§æŠ•è³‡æˆ¦ç•¥ã‚’考ãˆã¦ã¿ãŸï½œã«ã¨ã‚Š
ã«ã¨ã‚Šã¨ç”³ã—ã¾ã™ã€‚ æ—¥æœ¬æ ªã®æ—¥æ¬¡ãƒãƒ³ã‚°ã‚·ãƒ§ãƒ¼ãƒˆæˆ¦ç•¥ã‚’é‹ç”¨ã—ã¦ã„ã¾ã™! 開示ã—ã¦ã„ã‚‹ã®ã¯ã€ã‚·ã‚¹ãƒ†ãƒ トレードã®æˆç¸¾ã§ã™ã€‚機械å¦ç¿’モデルã®æˆ¦ç•¥ã¨ãƒ«ãƒ¼ãƒ«ãƒ™ãƒ¼ã‚¹ã®æˆ¦ç•¥ç‰è¤‡æ•°ã®æˆ¦ç•¥ã‚’組ã¿åˆã‚ã›ã¦ã„ã¾ã™ã€‚ Claude Codeã¯æœ€åˆã«å…¬é–‹ã•ã‚ŒãŸé ƒã‹ã‚‰æœ¬æ¥ã§ç©æ¥µçš„ã«ä½¿ã†ã‚ˆã†ã«ã—ã¦ã„ã¦ã€æŠ•è³‡æˆ¦ç•¥ã®ã‚¢ã‚¤ãƒ‡ã‚¢å‡ºã—や検証ã«ã‚‚ç©æ¥µçš„ã«åˆ©ç”¨ã—ã¦ã„ã¾ã™ã€‚ (ã‚‚ã¡ã‚ã‚“Maxプランã§èª²é‡‘ã—ã¦ã‚¬ãƒ³ã‚¬ãƒ³ã¶ã‚“回ã—ã¦ã„ã¾ã™ç¬‘) 若干アイデアをLLMã«å…¥åŠ›ã™ã‚‹ã®ã¯å«Œã ãªã¨ã„ã†æ°—æŒã¡ã¯ã‚ã‚Šã¾ã™ãŒã€æœ€è¿‘ã¯ãã‚Œã§ã‚‚使ã†ã—ã‹ãªã„ã‹ãªã¨ã„ã†æ°—æŒã¡ã«ãªã£ã¦ã„ã¾ã™ã€‚ 「AIã«æˆ¦ç•¥ã‚’考ãˆã•ã›ã‚Œã°ã€ã‚¨ãƒƒã‚¸ãŒè¦‹ã¤ã‹ã‚‹ã®ã§ã¯??〠ã¨è€ƒãˆãŸã“ã¨ãŒã‚る人ã¯å¤šã„ã®ã§ã¯ãªã„ã§ã—ょã†ã‹ã€‚自分もãã†ã§ã—ãŸã€‚ 実際ã«ã‚„ã£ã¦ã¿ã‚‹ã¨ã€æ€ã£ãŸã»ã©ç°¡å˜ã«ã¯ã„ã‹ãªã‹ã£ãŸã®ã§ã™ãŒã€ã“ã®åˆ†é‡Žã ã‹ã‚‰ã“ãã®å¯èƒ½æ€§ã‚‚æ„Ÿã˜ãŸã®ã§ã€ãã®ä¸¡é¢ã«ã¤ã„ã¦æ›¸ã„ã¦ã¿ã¾ã™ã€‚ ã“ã®è¨˜äº‹ã¯ã€AI使ãˆã°ç°¡
æˆåŠŸã™ã‚‹æŠ•è³‡ï¼šãƒˆãƒ¬ãƒ¼ãƒ‡ã‚£ãƒ³ã‚°ã®ã‚µã‚¤ã‚¨ãƒ³ã‚¹ - Qiita
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? ã¯ã˜ã‚㫠早速ã§ã™ãŒã€çš†ã•ã‚“ã¯æŠ•è³‡ã‚’ã—ã¦ã„ã‚‹ã§ã—ょã†ã‹ã€‚ã—ã¦ã„ã‚‹ã¨ã™ã‚Œã°ã€ã©ã®ã‚ˆã†ãªæŠ•è³‡ã‚’ã—ã¦ã„らã£ã—ゃるã§ã—ょã†ã‹ã€‚ 世ã®ä¸ã«ã¯æ§˜ã€…ãªæŠ•è³‡å¯¾è±¡ãŒå˜åœ¨ã—ã€ã¾ãŸãã®æŠ•è³‡æ‰‹æ³•ã‚‚様々ã§ã™ã€‚投資ã«é–¢ã™ã‚‹æƒ…å ±ã¯ä¸–ã®ä¸ã«æº¢ã‚Œã¦ã„ã¾ã™ã€‚氾濫ã—ã¦ã„ã‚‹ã¨è¨€ã£ãŸã»ã†ãŒã‚ˆã„ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“。書ç±ã‚’例ã«å–ã‚‹ã¨ã€ç”˜ã„æ–‡å¥ã§æŠ•è³‡ã‚’奨励ã™ã‚‹ãƒ©ã‚¤ãƒˆãªå…¥é–€æ›¸ã‹ã‚‰é‡‘èžã®å°‚門書ã¾ã§ãšã‚‰ã‚Šã¨ä¸¦ã³ã¾ã™ã€‚ã¾ãŸãƒ–ãƒã‚°ã‚„SNSã‚‚é‡è¦ãªæƒ…å ±æºã¨ãªã£ã¦ãŠã‚Šã€æœ€è¿‘ã§ã¯æŠ•è³‡å‘ã‘ã®YouTubeも人気を集ã‚ã¦ã„るよã†ã§ã™ã€‚ ã—ã‹ã—ã“ã‚Œã ã‘多様ãªæƒ…å ±ã‚½ãƒ¼ã‚¹ãŒå˜åœ¨ã™ã‚‹ã«ã‚‚é–¢ã‚らãšã€æŠ•è³‡ã§æˆåŠŸã‚’åŽã‚ã‚‹
LLM×トレーディング ã¾ã¨ã‚ã®ã¾ã¨ã‚｜UKI
ã¯ã˜ã‚ã«UKIã§ã™ã€‚ 今年ã®ã‚¢ãƒ‰ãƒ™ãƒ³ãƒˆã‚«ãƒ¬ãƒ³ãƒ€ãƒ¼ã¯ã€ã©ã®ã‚ˆã†ãªè¨˜äº‹ã‚’書ãã‹éžå¸¸ã«æ‚©ã¿ã¾ã—ãŸã€‚åˆå¿ƒè€…å‘ã‘ã®è¨˜äº‹ã‚’書ã“ã†ã¨ã‚‚æ€ã„ã¾ã—ãŸãŒã€ã‚¢ãƒ‰ãƒ™ãƒ³ãƒˆã‚«ãƒ¬ãƒ³ãƒ€ãƒ¼ã«ã¯æ—¢ã«åˆå¿ƒè€…å‘ã‘ã®ç§€é€¸ãªè¨˜äº‹ãŒå…¬é–‹ã•ã‚Œã¦ã„ã¾ã™ã€‚従ã£ã¦è‡ªåˆ†ãŒç•Œéšˆã«æä¾›ã™ã‚‹è¨˜äº‹ã¯ã€ã‚‚ã£ã¨ãƒ‹ãƒƒãƒã§æ·±ã„記事ã®ã»ã†ãŒé©å½“ã ã¨è€ƒãˆã€æ€¥é½å·®ã—替ãˆã‚‹ã“ã¨ã«ã—ã¾ã—ãŸã€‚ LLMã‚„AIエージェントã«é–¢ã™ã‚‹ç ”究ã¯é‡‘èžåˆ†é‡Žã§ã‚‚盛んã«è¡Œã‚ã‚Œã¦ã„ã¾ã™ã€‚ã“れら1ã¤1ã¤ã®ç ”究ã«ã¤ã„ã¦èª¿æŸ»ã™ã‚‹ã“ã¨ã‚‚é‡è¦ã§ã™ãŒã€å…¨ä½“を俯瞰ã—ã¦ç ”究トレンドを把æ¡ã—ã€è‡ªèº«ã®æ–¹å‘性を検討ã™ã‚‹ã“ã¨ã‚‚é‡è¦ã§ã™ã€‚æ•°ã‚ã‚‹è«–æ–‡ã®ä¸ã«ã¯ã€ã“れらã®ç ”究を体系的ã«æ•´ç†ã—ã¦ãƒ¬ãƒ“ューã—ãŸã‚‚ã®ã‚‚å˜åœ¨ã—ã¾ã™ã€‚ 今回ã®è¨˜äº‹ã¯ã€LLMをトレーディングã«å¿œç”¨ã™ã‚‹ç ”究ã®æ¦‚æ³ã‚’体系的ã«æ•´ç†ãƒ»ãƒ¬ãƒ“ューã—ãŸ5ã¤ã®ã‚µãƒ¼ãƒ™ã‚¤è«–æ–‡ã«ã¤ã„ã¦ã€çŸæœŸçš„ãªãƒˆãƒ¬ãƒ¼ãƒ‰ã§åŽç›Šã‚’得る（予測力をå‘上ã•ã›ã‚‹ï¼‰ãŸã‚ã®è¦³ç‚¹ã«ç‰¹åŒ–ã—ã¦ã¾ã¨ã‚ç›´ã—ãŸã‚‚ã®ã§ã™ã€‚
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Despite the rapid growth of machine learning research, corresponding code implementations are often unavailable, making it slow and labor-intensive for researchers to reproduce results and build upon prior work. In the meantime, recent Large Language Models (LLMs) excel at understanding scientific documents and generating high-quality code. Inspired by this, we introduce PaperCoder, a multi-agent
AIã ã‘ã§æ–°ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã«ã‚ˆã‚‹æ£è¦è¡¨ç¾ãƒ©ã‚¤ãƒ–ラリを作ã£ã¦ã¿ãŸ - エムスリーテックブãƒã‚°
ã“ã‚“ã«ã¡ã¯ã€‚今回ã¯ã€Geminiã¨Claude 4ã¨ã„ã†2ã¤ã®AIアシスタントã ã‘を使ã£ã¦ã€æ£è¦è¡¨ç¾ãƒ©ã‚¤ãƒ–ラリを一ã‹ã‚‰ä½œæˆã—ãŸä½“験をãŠè©±ã—ã—ã¾ã™ã€‚ ãã£ã‹ã‘：å¤ã„ç†è«–ã¸ã®èˆˆå‘³ é©šç•°çš„ãªé–‹ç™ºé€Ÿåº¦ï¼šæ£å‘³æ•°æ™‚é–“ã§å®Œæˆ AIアシスタントã«ã‚ˆã‚‹é–‹ç™ºãƒ—ãƒã‚»ã‚¹ 役割分担ã®è‡ªç„¶ãªç™ºç”Ÿ AIã«ã‚ˆã‚‹ã‚³ãƒ¼ãƒ‰ç”Ÿæˆã®è³ª å¦è¡“論文レベルã®ç†è«–æ•´å‚™ Abstract（概è¦ï¼‰ã®è‡ªå‹•ç”Ÿæˆ å…ˆè¡Œç ”ç©¶ã¨ã®è©³ç´°æ¯”較 ç†è«–的基盤ã®æ–‡çŒ®èª¿æŸ» 完æˆã—ãŸãƒ©ã‚¤ãƒ–ラリã®æ©Ÿèƒ½ 基本機能 高度ãªæ©Ÿèƒ½ AIアシストã®é©æ–°çš„å´é¢ 1. ç†è«–ã®ç¾ä»£çš„解釈 2. 包括的ãªãƒ†ã‚¹ãƒˆè¨è¨ˆ 3. ドã‚ュメント作æˆã®è‡ªå‹•åŒ– 4. 段階的ãªæ©Ÿèƒ½æ‹¡å¼µ 開発体験ã‹ã‚‰è¦‹ãˆãŸAIã®å¯èƒ½æ€§ é©šã„ãŸã“㨠課題ã¨ã—ã¦è¦‹ãˆãŸã“㨠将æ¥ã¸ã®ç¤ºå”† ç ”ç©¶é–‹ç™ºã®åŠ 速 教育ã¸ã®å¿œç”¨ 産æ¥å¿œç”¨ã®å¯èƒ½æ€§ ã¾ã¨ã‚ We are hiring! ãã£ã‹ã‘：å¤ã„ç†è«–ã¸ã®èˆˆå‘³ 事ã®å§‹ã¾ã‚Šã¯ã€1964å¹´
Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely, that would accelerate AI development and allow us to reap its benefits much sooner. Meta-learning can automate the discovery of novel algorithms, but is limited by first-order improvements and the human design of a sui
Google DeepMindã€AIãŒè‡ªã‚‰ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ を発見・進化ã•ã›ã‚‹ã€ŒAlphaEvolveã€ç™ºè¡¨
ç±³Google傘下ã®Google DeepMindã¯5月14日（ç¾åœ°æ™‚間）ã€AIãŒè‡ªã‚‰ã‚³ãƒ³ãƒ”ュータã®è¨ˆç®—æ‰‹é †ã‚„ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã‚’ç™ºè¦‹ã—ã€ã•ã‚‰ã«é€²åŒ–ã•ã›ã‚‹æ–°ã—ã„AIエージェント「AlphaEvolveã€ã‚’発表ã—ãŸã€‚ AlphaEvolveã®ç›®çš„ã¯ã€ã‚³ãƒ³ãƒ”ュータサイエンスã®åŸºç¤Žã¨ãªã‚‹ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ や複雑ãªæ•°å¦çš„課題ã«å¯¾ã™ã‚‹è§£æ³•ã‚’ã€äººé–“ãŒã‚¼ãƒã‹ã‚‰é–‹ç™ºã™ã‚‹ã®ã§ã¯ãªãã€AI自身ãŒè¦‹ã¤ã‘出ã—ã€æœ€é©åŒ–ã™ã‚‹ã“ã¨ã«ã‚ã‚‹ã¨ã„ã†ã€‚ ã“ã®ã‚·ã‚¹ãƒ†ãƒ ã®å¤§ã¾ã‹ãªæµã‚Œã¯ã€é«˜é€Ÿãªã€ŒGemini Flashã€ã§ã‚¢ã‚¤ãƒ‡ã‚¢å€™è£œã‚’生æˆã—ã€ãれを「Gemini Proã€ãŒæ·±ã洞察ã—ã¦æ案ã™ã‚‹ã“ã¨ã§ã€ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ を実装ã™ã‚‹ãƒ—ãƒã‚°ãƒ©ãƒ を生ã¿å‡ºã™ã€‚ã“ã®ãƒ—ãƒã‚°ãƒ©ãƒ ã‚’ã€AlphaEvolveã«çµ„ã¿è¾¼ã¾ã‚ŒãŸè‡ªå‹•è©•ä¾¡ãƒ„ールãŒæ¤œè¨¼ã€å®Ÿè¡Œã€ã‚¹ã‚³ã‚¢ãƒªãƒ³ã‚°ã™ã‚‹ã“ã¨ã§ã€ã‚¢ã‚¤ãƒ‡ã‚¢ã‚’「進化ã€ã•ã›ã¦ã„ã。 AlphaEvolveã¯ã€æ—¢ã«Google内部ã§å¤šå²ã«ã‚ãŸã‚‹æˆ
ラベルä¼æ’アルゴリズム- Platform For AI - Alibaba Cloud ドã‚ュメントセンター
ラベルä¼æ’アルゴリズム(LPA) ã¯ã€åŠæ•™å¸«ä»˜ã機械å¦ç¿’アルゴリズムã§ã‚る。 é ‚ç‚¹ã®ãƒ©ãƒ™ãƒ« (コミュニティ) ã¯ã€éš£æŽ¥ã™ã‚‹é ‚点ã®ãƒ©ãƒ™ãƒ«ã«ä¾å˜ã—ã¾ã™ã€‚ ä¾å˜åº¦ã¯ã€é ‚点間ã®é¡žä¼¼æ€§ã«ã‚ˆã£ã¦æ±ºå®šã•ã‚Œã‚‹ã€‚ データã¯ã€å復ä¼æ¬æ›´æ–°ã‚’実行ã™ã‚‹ã“ã¨ã«ã‚ˆã£ã¦å®‰å®šã™ã‚‹ã€‚ Label Propagation Clusteringコンãƒãƒ¼ãƒãƒ³ãƒˆã¯ã€ã‚°ãƒ©ãƒ•å†…ã®ã™ã¹ã¦ã®é ‚点ã®åŽæŸå¾Œã«å„é ‚ç‚¹ã®ã‚°ãƒ«ãƒ¼ãƒ—ã‚’æä¾›ã§ãã¾ã™ã€‚ アルゴリズムã®èª¬æ˜Žã‚°ãƒ©ãƒ•ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ã¯ã€ã‚°ãƒ©ãƒ•ã®ãƒˆãƒãƒã‚¸ã«åŸºã¥ã„ã¦ã‚°ãƒ©ãƒ•ã‚’サブグラフã«åˆ†å‰²ã™ã‚‹ãŸã‚ã«ä½¿ç”¨ã•ã‚Œã¾ã™ã€‚ ã—ãŸãŒã£ã¦ã€ã‚µãƒ–グラフ内ã®é ‚点間ã®ãƒªãƒ³ã‚¯ã¯ã€ã‚µãƒ–グラフ間ã®ãƒªãƒ³ã‚¯ã‚ˆã‚Šã‚‚多ããªã‚Šã¾ã™ã€‚ ã“ã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã¯ã€ä¸€æ„ã®ãƒ©ãƒ™ãƒ«ã‚’使用ã—ã¦å„é ‚ç‚¹ã‚’åˆæœŸåŒ–ã—ã€é ‚点をå復ã—ã€ã‚³ãƒŸãƒ¥ãƒ‹ãƒ†ã‚£å†…ã®éš£æŽ¥ã™ã‚‹é ‚点ã®ä¸ã§æœ€ã‚‚é »ç¹ã«ç¾ã‚Œã‚‹ãƒ©ãƒ™ãƒ«ã‚’é ‚ç‚¹ã«å‰²ã‚Šå½“ã¦ã¾ã™ã€‚ アルゴリズムã¯ã€å„é ‚ç‚¹ãŒãã®éš£æŽ¥ã™ã‚‹é ‚点ã®ä¸ã§æœ€
Understanding the BM25 full text search algorithm
19 Nov, 2024 BM25, or Best Match 25, is a widely used algorithm for full text search. It is the default in Lucene/Elasticsearch and SQLite, among others. Recently, it has become common to combine full text search and vector similarity search into "hybrid search". I wanted to understand how full text search works, and specifically BM25, so here is my attempt at understanding by re-explaining. Motiv
ID生æˆæ–¹æ³•ã«ã¤ã„ã¦ã‚ã‚Œã“ã‚Œ
ID生æˆã«ã¤ã„ã¦èžã‹ã‚Œã‚‹ã“ã¨ãŒå¤šã„ã®ã§ã€ç‹¬è‡ªã®è¦³ç‚¹ã§ã¾ã¨ã‚ã¦ã¿ã¾ã™ã€‚タイトルã¯é©å½“ã§ã™â€¦ã€‚ DBã¯MySQL(InnoDB)を想定ã—ã¦ã„ã¾ã™ã€‚ã‚ã—ã‹ã‚‰ãšã€‚ ID生æˆã‚’知りãŸã„ãªã‚‰ ID生æˆã«é–¢ã—ã¦ã¯ä»¥ä¸‹ã®è¨˜äº‹ãŒã‚ˆãã¾ã¨ã¾ã£ã¦ã„ã‚‹ã®ã§å‚考ã«ã—ã¦ã¿ã¦ãã ã•ã„。値形å¼ãªã©è©³ã—ã書ã‹ã‚Œã¦ã„ã¾ã™ã€‚ ID生æˆå¤§å…¨ Facebook, Twitter, Instagramç‰ãŒã©ã†ã‚„ã£ã¦IDを生æˆã—ã¦ã„ã‚‹ã®ã‹ ã¾ã¨ã‚ ID生æˆæ–¹æ³• 以下ã®ID生æˆæ–¹æ³•ã¯ã€ãŠæ‰‹è»½ã«æŽ¡ç”¨ã—ã‚„ã™ã„ã‚‚ã®é †ã§åˆ—挙ã—ã¾ã™ã€‚ DB採番/連番型 AUTO_INCREMENT DBã®AUTO_INCREMENTã§æŽ¡ç•ªã™ã‚‹æ–¹æ³•ã€‚ Pros 数値型ã§æ‰±ãˆã‚‹ 普通ã¯64ビットã®æ•´æ•°åž‹ã‚’採用ã™ã‚‹ã“ã¨ãŒå¤šã„ å˜èª¿å¢—åŠ ã™ã‚‹é€£ç•ªã§ã™ã®ã§ã€ã‚½ãƒ¼ãƒˆå¯èƒ½ã§ã‹ã¤ã‚¤ãƒ³ãƒ‡ãƒƒã‚¯ã‚¹ã®ç©ºé–“効率ãŒã‚ˆã„ å˜èª¿å¢—åŠ ã™ã‚‹ã®ã§ã€ã‚ャパシティを予測ã—ã‚„ã™ã„ 64ビットã‚ã‚Œã°ã‚ã¾ã‚Šæ°—ã«
ランクå¦ç¿’(Learning to Rank)ã§é †ä½ã‚’予測ã™ã‚‹ï¼ˆå®Ÿè£…ã§å¦ã¶æ©Ÿæ¢°å¦ç¿’ã‚·ãƒªãƒ¼ã‚ºï¼‰ï½œæ ªå¼ä¼šç¤¾Rossoå…¬å¼note
ã“ã‚“ã«ã¡ã¯ã€‚æ ªå¼ä¼šç¤¾Rossoã€AI部ã§ã™ã€‚競馬ã®ç€é †é †ä½ã€æ¤œç´¢ã‚¨ãƒ³ã‚¸ãƒ³ã®æ¤œç´¢é †ä½ã€é¸æŒ™ã®æŠ•ç¥¨æ•°é †ä½ãªã©ç§é”ã®èº«ã®å›žã‚Šã«ã¯ã€é †ä½ã€ãƒ©ãƒ³ã‚ングãŒé–¢ã‚る多ãã®äº‹è±¡ãŒå˜åœ¨ã—ã¦ã„ã¾ã™ã€‚今回ã¯ã€é †ä½ã€ãƒ©ãƒ³ã‚ングを機械å¦ç¿’ã§æ‰±ã†ãƒ©ãƒ³ã‚¯å¦ç¿’ã‚’ã”紹介ã—ã¾ã™ã€‚ã¾ãŸã€xgboostã«ä»˜å±žã—ã¦ã„るランクå¦ç¿’ã®ã‚³ãƒ¼ãƒ‰ã‚’例ã«ã—ã¦ã€ãƒ©ãƒ³ã‚¯å¦ç¿’ã®ç°¡å˜ãªå®Ÿè£…ã‚’è¡Œã£ã¦ã¿ã¾ã™ã€‚最後ã«é€šå¸¸ã®æ©Ÿæ¢°å¦ç¿’ã¨ãƒ©ãƒ³ã‚¯å¦ç¿’ã®é•ã„ã«ã¤ã„ã¦ã€ç°¡å˜ã«æ¤œè¨¼ã‚’è¡Œã£ã¦ã„ãã¾ã™ã€‚ ランクå¦ç¿’ã¨ã¯ãƒ©ãƒ³ã‚¯å¦ç¿’(Learning to Rank)ã¨ã¯ã€ç«¶é¦¬ã®ç€é †äºˆæ¸¬ã€æ¤œç´¢ã‚¨ãƒ³ã‚¸ãƒ³ã®æ¤œç´¢ãƒ©ãƒ³ã‚ングãªã©ã®é †åºãã®ã‚‚ã®ã‚’目的変数ã¨ã—ãŸå ´åˆã«ç”¨ã„られる機械å¦ç¿’ã®æ‰‹æ³•ã®ã“ã¨ã§ã™ã€‚ 通常ã®æ©Ÿæ¢°å¦ç¿’ã¨é•ã†ç‚¹ã¨ã—ã¦ã¯ã€é€šå¸¸ã®æ©Ÿæ¢°å¦ç¿’ã¯ã€äºˆæ¸¬å€¤ã¨å®Ÿç¾å€¤ã®èª¤å·®ã«ç€ç›®ã™ã‚‹ï¼ˆåŸºæ•°çš„アプãƒãƒ¼ãƒï¼‰ã®ã«å¯¾ã—ã€ãƒ©ãƒ³ã‚¯å¦ç¿’ã§ã¯ã€äºˆæ¸¬çµæžœã®é †åºã¨å®Ÿç¾çµæžœã®é †åºãŒåˆã£ã¦ã„る（åºæ•°çš„アプãƒãƒ¼ãƒï¼‰ã‹
å††ç›¸å ´ 一時 1ドルï¼149円å°ã¾ã§å€¤ä¸‹ãŒã‚Š 米雇用統計å—ã‘ | NHK
4æ—¥ã®ãƒ‹ãƒ¥ãƒ¼ãƒ¨ãƒ¼ã‚¯å¤–å›½ç‚ºæ›¿å¸‚å ´ã§ã¯ã€ã“ã®æ—¥ã«ç™ºè¡¨ã•ã‚ŒãŸã‚¢ãƒ¡ãƒªã‚«ã®é›‡ç”¨çµ±è¨ˆã®å†…容ã‹ã‚‰åŠ´åƒå¸‚å ´ãŒå …調ã ã¨ã„ã†å—ã‘æ¢ã‚ãŒåºƒãŒã‚Šã€å††å£²ã‚Šãƒ»ãƒ‰ãƒ«è²·ã„ãŒåŠ 速ã—ã€å††ç›¸å ´ã¯ã€ä¸€æ™‚ã€1ドルï¼149円å°ã¾ã§å€¤ä¸‹ãŒã‚Šã—ã¾ã—ãŸã€‚ 4æ—¥ã®ãƒ‹ãƒ¥ãƒ¼ãƒ¨ãƒ¼ã‚¯å¤–å›½ç‚ºæ›¿å¸‚å ´ã§ã¯ã“ã®æ—¥ã€ç™ºè¡¨ã•ã‚Œã‚‹å…ˆæœˆï¼ˆ9月）ã®é›‡ç”¨çµ±è¨ˆã«æ³¨ç›®ãŒé›†ã¾ã£ã¦ã„ã¾ã—ãŸã€‚ 統計ã§ã¯å°±æ¥è€…ã®ä¼¸ã³ãŒå¸‚å ´äºˆæƒ³ã‚’å¤§ãã上回りã€åŠ´åƒå¸‚å ´ã®å …調ã•ã‹ã‚‰FRBï¼é€£é‚¦æº–備制度ç†äº‹ä¼šã«ã‚ˆã‚‹åˆ©ä¸‹ã’ã®ãƒšãƒ¼ã‚¹ãŒç·©ã‚„ã‹ã«ãªã‚‹ã¨ã„ã†å—ã‘æ¢ã‚ãŒåºƒãŒã‚Šã¾ã—ãŸã€‚ 日米ã®é‡‘利差ãŒç¸®ã¾ã‚Šã«ãã„ã¨ã®è¦‹æ–¹ã‹ã‚‰å††å£²ã‚Šãƒ»ãƒ‰ãƒ«è²·ã„ã®å‹•ããŒåŠ 速ã—ã€çµ±è¨ˆã®ç™ºè¡¨å‰ã€1ドルï¼146円å°åŠã°ã§ã—ãŸãŒã€ä¸€æ™‚ã€1ドルï¼149円å°ã¾ã§2円余り値下ãŒã‚Šã—ã¾ã—ãŸã€‚ ã“ã¨ã—8月ä¸æ—¬ä»¥æ¥ã€ãŠã‚ˆã1ã‹æœˆåŠã¶ã‚Šã®å††å®‰æ°´æº–ã§ã™ã€‚ å¸‚å ´é–¢ä¿‚è€…ã¯ã€ŒFRBã®é‡‘èžæ”¿ç–を見極ã‚る上ã§æœ€å¤§ã®ç„¦ç‚¹ã¨ãªã£ã¦ã„ãŸé›‡ç”¨ã«ã¤ã„ã¦åŠ›å¼·ã„内容ãŒç¤ºã•ã‚ŒãŸã“ã¨ã§F
Zstandard - Real-time data compression algorithm
Zstandard is a fast compression algorithm, providing high compression ratios. It also offers a special mode for small data, called dictionary compression. The reference library offers a very wide range of speed / compression trade-off, and is backed by an extremely fast decoder (see benchmarks below). Zstandard library is provided as open source software using a BSD license. Its format is stable a
何故パスワードをãƒãƒƒã‚·ãƒ¥åŒ–ã—ã¦ä¿å˜ã™ã‚‹ã ã‘ã§ã¯é§„ç›®ãªã®ã‹ï¼Ÿ - NRIãƒãƒƒãƒˆã‚³ãƒ Blog
ä¸æ£ã‚¢ã‚¯ã‚»ã‚¹ã«ã‚ˆã‚‹IDã¨ãƒ‘スワードã®æ¼æ´©ã‚’å—ã‘ã¦ã€MD5ã«ã‚ˆã‚‹ãƒãƒƒã‚·ãƒ¥åŒ–ã«ã¤ã„ã¦è©±é¡Œã«ãªã£ã¦ã„ã¾ã—ãŸã€‚システムを作る上ã§ã€ãƒ‘スワードã®ç®¡ç†ã‚„èªè¨¼ã¯ã©ã†è¨è¨ˆã™ã¹ãã‹ã‚’考ãˆã‚‹ãŸã‚ã«ã€å°‘ã—æ•´ç†ã‚’ã—ã¦ã¿ã¾ã™ã€‚ã‚‚ã—事実誤èªãŒã‚ã‚Œã°ã€ã©ã—ã©ã—ã”指摘ãã ã•ã„。 == 2023/8/21追記 == ã“ã®è¨˜äº‹ã¯ã€ãƒãƒƒã‚·ãƒ¥ã®ä¿å˜ã®ä»•æ–¹ä¸€ã¤ã¨ã£ã¦ã‚‚ã€æ²¢å±±ã®å¯¾ç–方法ãŒå¿…è¦ã§ã‚ã‚‹ã¨ã„ã†ã“ã¨ã‚’ãŠä¼ãˆã™ã‚‹ãŸã‚ã«è¨˜è¼‰ã—ã¦ã„ã¾ã™ã€‚ãã—ã¦ã€ã“ã‚Œã‹ã‚‰ç´¹ä»‹ã™ã‚‹æ‰‹æ³•ã‚’å–ã‚Œã°å®‰å…¨ã¨ãŠå‹§ã‚ã—ã¦ã„る訳ã§ã¯ãªã„ã®ã§ã€ãã®ç‚¹ã‚’ã”ç•™æ„ã„ãŸã ã‘ã‚Œã°ã¨æ€ã„ã¾ã™ã€‚攻撃手法ã«å¿œã˜ã¦ã®å¯¾å¿œç–ã®å¤‰é·ã‚’知ã£ã¦ã„ãŸã ãã“ã¨ã§ã€ã‚»ã‚ュリティ対ç–ã¯ä¸€åº¦è¡Œãˆã°å®‰å…¨ã¨ã„ã†ã‚‚ã®ã§ã¯ãªã„ã“ã¨ã‚’知ã£ã¦é ‚ãã‚ッカケã«ãªã‚Œã°å¹¸ã„ã§ã™ã€‚ == 追記終ã‚ã‚Š == パスワードã®ãƒãƒƒã‚·ãƒ¥åŒ– ã¾ãšæœ€åˆã«ãƒ‘スワードã®ä¿å˜æ–¹æ³•ã§ã™ã€‚ä½•ã‚‚åŠ å·¥ã—ãªã„ã§å¹³æ–‡ã§ä¿å˜ã™ã‚‹ã®ã¯é§„ç›®ã¨ã„ã†ã®ã¯ã€ã ã„ã¶èª
グラフアルゴリズム実践活用術
æƒ…å ±ã‚ªãƒªãƒ³ãƒ”ãƒƒã‚¯å¤å£ã‚»ãƒŸãƒŠãƒ¼ 2023: https://jcioi-summer-seminar-2023.peatix.com/ ã§ã®è¬›æ¼”スライドã§ã™ã€‚ 講義概è¦: アルゴリズムを勉強ã—ã¦ã„ã‚‹ã¨ï¼Œã‚°ãƒ©ãƒ•ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã«ãŸãã•ã‚“出会ã„ã¾ã™ï¼Žã—ã‹ã—，グラフアルゴリズムãŒç¾å®Ÿä¸–ç•Œã§ã©ã®ã‚ˆã†ã«æ´»èºã—ã¦ã„…
ランã‚ング
ãŠçŸ¥ã‚‰ã›
ランã‚ング
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
https://x.com/tommy_love123/status/2028005212435501510?s=12&t=iU_i0OjOM9ACBlUj4kLuSA
https://x.com/tommy_love123/status/2022796607738335369?s=12&t=iU_i0OjOM9ACBlUj4kLuSA
https://x.com/blog_uki/status/2022174249335894413?s=12&t=iU_i0OjOM9ACBlUj4kLuSA
AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms
How Recommendation Algorithms Actually Work
{{#tags}}- {{label}}
{{/tags}}