kernel/vm探検隊part7ã®ç™ºè¡¨ã‚¹ãƒ©ã‚¤ãƒ‰ã§ã™ã€‚ https://kernelvm.connpass.com/event/330079/ 以下å†æ¼”å‹•ç”»ã®ãƒ†ã‚ストã§ã‚‚ã‚ã‚Šã¾ã™ã€‚ https://youtu.be/kJzG-o0_m24
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kernel/vm探検隊part7ã®ç™ºè¡¨ã‚¹ãƒ©ã‚¤ãƒ‰ã§ã™ã€‚ https://kernelvm.connpass.com/event/330079/ 以下å†æ¼”å‹•ç”»ã®ãƒ†ã‚ストã§ã‚‚ã‚ã‚Šã¾ã™ã€‚ https://youtu.be/kJzG-o0_m24
Postgresqlã®ltreeを活用ã—ãŸéšŽå±¤æ§‹é€ ã®ä¾¿åˆ©ãªåˆ©ç”¨æ³• - RAKUS Developers Blog | ラクス エンジニアブãƒã‚°
ã¯ã˜ã‚ã« ltreeã¨ã¯ ltreeåž‹ ltreeã®æ“作 活用法 1. 承èªãƒ•ãƒãƒ¼ã®æ§‹ç¯‰ 事å‰æº–å‚™ ãƒ†ãƒ¼ãƒ–ãƒ«ä½œæˆ ãƒ‡ãƒ¼ã‚¿è¿½åŠ 2. テーブルã«ç´°ã‹ãアクセス制御をã‹ã‘ã‚‹ 事å‰æº–å‚™ ltreeã®æœ‰åŠ¹åŒ– ãƒ†ãƒ¼ãƒ–ãƒ«ä½œæˆ ãƒãƒªã‚·ãƒ¼ä½œæˆ 行セã‚ュリティãƒãƒªã‚·ãƒ¼ã®æœ‰åŠ¹åŒ– ãƒãƒªã‚·ãƒ¼ã®è¨å®š ãƒ‡ãƒ¼ã‚¿ã‚’è¿½åŠ ãƒ¦ãƒ¼ã‚¶ãƒ¼ä½œæˆ è©¦ã™ ã¾ã¨ã‚ ã¯ã˜ã‚ã« ã“ã‚“ã«ã¡ã¯ï¼ エンジニア2å¹´ç›®ã®TKDSã§ã™ï¼ 今回ã¯ltreeã«ã¤ã„ã¦èª¿ã¹ã€ãã®æ´»ç”¨æ³•ã‚’考ãˆã¦ã¿ã¾ã—ãŸã€‚ ltreeã«ã¤ã„ã¦ã€ltreeã®æ´»ç”¨æ³•ã®2段構æˆã§ã™ã€‚ ltreeã¨ã¯ éšŽå±¤ãƒ„ãƒªãƒ¼æ§‹é€ ã‚’æ¨¡ã—ãŸæ§‹é€ ã‚’æ ¼ç´ã™ã‚‹æ©Ÿèƒ½ã‚’æä¾›ã™ã‚‹æ‹¡å¼µæ©Ÿèƒ½ã§ã™ã€‚ 詳ã—ãã¯ãƒ‰ã‚ュメントをã¿ã¦ãã ã•ã„。 ltreeåž‹ éšŽå±¤ãƒ„ãƒªãƒ¼æ§‹é€ ã‚’è¡¨ã™åž‹ã§ã™ã€‚ 例）`Company.Department.Team1 ドット区切りã§å¤§æ–‡å—å°æ–‡å—ã¯åŒºåˆ¥ã—ãªã„よã†ã§ã™ã€‚ å„データã¯ãƒ©ãƒ™ãƒ«ã¨å‘¼ã³ã¾ã™ï¼ˆä¸Šè¨˜ã§ã®C
GitHub - dleemiller/WordLlama: Things you can do with the token embeddings of an LLM
from wordllama import WordLlama # Load the default WordLlama model wl = WordLlama.load() # Calculate similarity between two sentences similarity_score = wl.similarity("i went to the car", "i went to the pawn shop") print(similarity_score) # Output: 0.06641249096796882 # Rank documents based on their similarity to a query query = "i went to the car" candidates = ["i went to the park", "i went to th
research!rsc: The xz attack shell script
Posted on Tuesday, April 2, 2024. Updated Wednesday, April 3, 2024. Introduction Andres Freund published the existence of the xz attack on 2024-03-29 to the public oss-security@openwall mailing list. The day before, he alerted Debian security and the (private) distros@openwall list. In his mail, he says that he dug into this after “observing a few odd symptoms around liblzma (part of the xz packag
GitHub - recommenders-team/recommenders: Best Practices on Recommendation Systems
Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems. Recommenders is a project under the Linux Foundation of AI and Data. This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The exam
Data Developer Platform
Home Data Developer Platform¶ A Data Platform Specification, open for adoption by any data platform developer. A modern way to run data engineering teams¶ Data teams are drained from continuously plumbing integrations and fragile pipelines, which leaves little to no time to focus on the real deal - data and data applications. Businesses that have a good grasp on data realise that today data makes
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デジタル技術を活用ã—ã¦ä¼æ¥ã®ãƒ“ジãƒã‚¹ã‚’変é©ã—ã€è‡ªç¤¾ã®ç«¶äº‰åŠ›ã‚’高ã‚ã¦ã„ã「デジタル・トランスフォーメーション（DX）ã€ãŒæ³¨ç›®ã‚’集ã‚ã‚‹ãªã‹ã€å¾“æ¥ã®ã‚ˆã†ãªITベンダやシステム部門ãŒä¸å¿ƒã«ãªã£ã¦è¦ä»¶å®šç¾©ã‚’ã™ã™ã‚るスタイルã‹ã‚‰ã€æ¥å‹™éƒ¨é–€ã®ãƒ¦ãƒ¼ã‚¶ãŒä¸»ä½“çš„ã«é–¢ä¸Žã™ã‚‹ã‚¹ã‚¿ã‚¤ãƒ«ã¸ã®å¤‰é©ã®å¿…è¦æ€§ãŒå¢—ã—ã¦ã„ã¾ã™ã€‚ システムã®è¦ä»¶ã‚’定義ã™ã‚‹è²¬ä»»ã¯ã€æ§‹ç¯‰ã•ã‚ŒãŸã‚·ã‚¹ãƒ†ãƒ を利用ã—ã¦ãƒ“ジãƒã‚¹ã«è²¢çŒ®ã™ã‚‹å½¹ç›®ã‚’è² ã†ãƒ¦ãƒ¼ã‚¶ã«ã‚ã‚‹ã¨è¨€ã‚ã‚Œã¦ã„ã¾ã™ã€‚ã—ã‹ã—ãªãŒã‚‰ã€ã‚·ã‚¹ãƒ†ãƒ 開発ã®é…延ã®éŽåŠã¯è¦ä»¶å®šç¾©ã®å¤±æ•—ã«ã‚ã‚‹ã¨è¨€ã‚れるよã†ã«ã€è¦ä»¶å®šç¾©ã«ãŠã„ã¦ã¯ã€ãã®éŽç¨‹ã§æ§˜ã€…ãªå•é¡Œã«ç›´é¢ã—ã¾ã™ã€‚ ãã“ã§IPAã§ã¯ã€è¦ä»¶å®šç¾©ã®éŽç¨‹ã§ç›´é¢ã™ã‚‹å•é¡Œã¸ã®å¯¾å¿œã‚’ガイドã™ã‚‹ã“ã¨ãŒã€ãƒ¦ãƒ¼ã‚¶ã¸ã®ã‚ˆã‚Šã„ã£ãã†ã®æ”¯æ´ç–ã¨ãªã‚‹ã¨è€ƒãˆã€ã€Œãƒ¦ãƒ¼ã‚¶ã®ãŸã‚ã®è¦ä»¶å®šç¾©ã‚¬ã‚¤ãƒ‰(åˆç‰ˆ)ã€ã®å†…容を一新ã—ã€ã€Œãƒ¦ãƒ¼ã‚¶ã®ãŸã‚ã®è¦ä»¶å®šç¾©ã‚¬ã‚¤ãƒ‰ 第2版 è¦ä»¶å®šç¾©ã‚’æˆåŠŸã«å°Žã128ã®å‹˜ã©ã“ã‚ã€ã¨
Mercari’s passkey adoption | Mercari Engineering
Mercari, Inc. offers C2C marketplace services as well as online and mobile payment solutions. Users can sell items on the marketplace, and make purchases in physical stores. Mercari is actively working on preventing phishing attacks. This is the driving force behind the adoption of passkey authentication. To enhance phishing resistance, several factors need to be considered, leading to the introdu
CVSSãƒãƒ¼ã‚¸ãƒ§ãƒ³4.0ã®å¤‰æ›´ç‚¹ã¨æ´»ç”¨å¯èƒ½æ€§
CVSS（Common Vulnerability Scoring System）ã¯ã€è„†å¼±æ€§ç®¡ç†ã«ãŠã‘る基本的ãªä»•çµ„ã¿ã¨ã—ã¦åºƒã利用ã•ã‚Œã¦ãŠã‚Šã€æ¥ç•Œå…¨ä½“ã®ãƒ‡ãƒ•ã‚¡ã‚¯ãƒˆã‚¹ã‚¿ãƒ³ãƒ€ãƒ¼ãƒ‰ã«ãªã£ã¦ã„ã¾ã™ã€‚CVSSã¯FIRST（Forum of Incident Respones and Security Teams）内ã«è¨ç½®ã•ã‚ŒãŸCVSS-SIG（Special Interest Group）1ã«ã‚ˆã£ã¦ç–定ã•ã‚Œã€2023å¹´7月ç¾åœ¨ã®æœ€æ–°ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã¯3.1ã¨ãªã£ã¦ã„ã¾ã™ã€‚2023å¹´6月ã«æ¬¡ãƒãƒ¼ã‚¸ãƒ§ãƒ³ã§ã‚ã‚‹4.0ã®ãƒ‘ブリックプレビュー版2ãŒå…¬é–‹ã•ã‚Œã¦ãŠã‚Šã€å¯„ã›ã‚‰ã‚ŒãŸã‚³ãƒ¡ãƒ³ãƒˆã‚’レビュー・åæ˜ ã—ãŸå¾Œã€2023å¹´10月を目途ã«ãƒãƒ¼ã‚¸ãƒ§ãƒ³4.0ã®å…¬é–‹ãŒäºˆå®šã•ã‚Œã¦ã„ã¾ã™ã€‚本稿ã§ã¯ãƒ‘ブリックプレビュー版ã«åŸºã¥ã„ã¦ã€ç¾è¡Œã®ãƒãƒ¼ã‚¸ãƒ§ãƒ³3.1ã¨ã®å¤‰æ›´ç‚¹ã‚’解説ã—ã¾ã™ã€‚ã¾ãŸã€SSVC（Stakeholder-Specific
Retentive Network: A Successor to Transformer for Large Language Models
In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modeling, which supports three computation paradigms, i.e., parallel, recurre
大è¦æ¨¡è¨€èªžãƒ¢ãƒ‡ãƒ«(LLM)ã®ä½œã‚Šæ–¹ GPT-NeoXç·¨ Part 1
ã¯ã˜ã‚ã« Turing æ ªå¼ä¼šç¤¾ã®ãƒªã‚µãƒ¼ãƒãƒãƒ¼ãƒ ã§ã‚¤ãƒ³ã‚¿ãƒ¼ãƒ³ã‚’ã—ã¦ã„ã‚‹æ±äº¬å·¥æ¥å¤§å¦ B4 ã®è—¤äº•(@okoge_kaz)ã§ã™ã€‚ 大è¦æ¨¡ãƒ¢ãƒ‡ãƒ«ã¸ã®æ³¨ç›®ã®é«˜ã•ã‚’è‚Œã§æ„Ÿã˜ã‚‹ä»Šæ—¥ã“ã®ã”ã‚ã§ã™ãŒã€äº‹å‰å¦ç¿’ã®çŸ¥è¦‹ã«ã¤ã„ã¦ã¯ä¾ç„¶ã¨ã—ã¦å分ã«å…±æœ‰ã•ã‚Œã¦ã„ã‚‹ã¨ã¯è¨€ã„難ã„ã¨å€‹äººçš„ã«æ„Ÿã˜ã¦ã„ã¾ã™ã€‚ Turingæ ªå¼ä¼šç¤¾ã§ã¯ã€æ¬¡ä¸–代ã®è‡ªå‹•é‹è»¢æŠ€è¡“を支ãˆã‚‹æŠ€è¡“ã®1ã¤ã¨ã—ã¦å¤§è¦æ¨¡è¨€èªžãƒ¢ãƒ‡ãƒ«ã«æ³¨ç›®ã—ã¦ãŠã‚Šã€ç‹¬è‡ªã«ç ”究開発を行ã£ã¦ã„ã¾ã™ã€‚今回ã¯å¤§è¦æ¨¡è¨€èªžãƒ¢ãƒ‡ãƒ«ã‚’å¦ç¿’ã™ã‚‹éš›ã€ç”¨ã„るライブラリ候補ã®ï¼‘ã¤ã«ä¸ŠãŒã‚‹ã§ã‚ã‚ã†GPT-NeoXã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚ 以下ã§ç’°å¢ƒæ§‹ç¯‰æ–¹æ³•ã€å¦ç¿’ã‚’è¡Œã†æ–¹æ³•ãªã©ã«ã¤ã„ã¦è©³ã—ã解説ã—ã¾ã™ã€‚ GPT-NeoXã¨ã¯ EleutherAIãŒç®¡ç†ã—ã¦ã„ã‚‹NIDIA/Megatron-LM ベースã®å¤§è¦æ¨¡è¨€èªžãƒ¢ãƒ‡ãƒ«(Large Language Model: LLM)ã‚’å¦ç¿’ã™ã‚‹ãŸã‚ã®ãƒ©ã‚¤ãƒ–ラリã§ã™ã€‚ Mi
CTOã®è¦–点ã‹ã‚‰è¦‹ãŸAzure OpenAI Serviceã¨OpenAIã®ChatGPT APIã®æ·±å €ã‚Šæ¯”較 - Qiita
※ ã“ã®è¨˜äº‹ã®å†…容ã¯å…ˆæ—¥ã®Qiita Nightã§ãŠè©±ã—ã—ãŸã“ã¨ã¨ä¸€éƒ¨é‡è¤‡ã—ã¾ã™ã€‚ Qiita Nightã§ã¯LTã®åˆ¶é™æ™‚é–“(10分)ã«åŽã‚ã‚‹ãŸã‚ã€çµæ§‹ç«¯æŠ˜ã‚Šã¾ã—ãŸã€‚ ã¯ã˜ã‚ã« ChatGPTãŒç™»å ´ã—ã¦ã‹ã‚‰æ•°ãƒ¶æœˆãŒçµŒã¡ã¾ã—ãŸã€‚ ChatGPTã‚’ã¯ã˜ã‚ã¨ã—ãŸGenerative AIã¯å®Œå…¨ã«ç¾åœ¨ã®ITæ¥ç•Œã®ãƒˆãƒ¬ãƒ³ãƒ‰ã¨ãªã£ã¦ã„ã¾ã™ã€‚ 今や多ãã®ä¼æ¥ã§ChatGPT APIをサービスã«çµ„ã¿è¾¼ã‚“ã§é¡§å®¢æ供を開始ã—ãŸã‚Šã€è‡ªç¤¾ã®ç¤¾å†…システムã«çµ„ã¿è¾¼ã‚“ã ã‚Šã¨ç©æ¥µçš„ã«åˆ©ç”¨ã™ã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã—ãŸã€‚ ç§ã‚‚Generative AIãŒç„¡ããªã‚‹ã¨æ¥å‹™ã«æ”¯éšœãŒå‡ºã‚‹ãƒ¬ãƒ™ãƒ«ã§åˆ©ç”¨ã—ã¦ã„ã¾ã™ã€‚ ãã—ã¦ã€2023å¹´5月時点ã§ã¯ChatGPTã®APIを利用ã™ã‚‹æ–¹æ³•ã¨ã—ã¦ã€æœ¬å®¶OpenAI社ãŒæä¾›ã—ã¦ã„ã‚‹APIを利用ã™ã‚‹æ–¹æ³•ã¨Microsoft社ãŒæä¾›ã—ã¦ã„ã‚‹Azure OpenAI Serviceã®APIを利用ã™ã‚‹æ–¹æ³•ã®2ã¤ãŒ
sqlfmtã«ã‚ˆã£ã¦ä½•ã‚’解決ã—ãŸã„ã®ã‹
ã“ã‚Œã¯ä½• データ基盤ã®é–‹ç™ºã«sqlfmtã‚’å°Žå…¥ã™ã‚‹ã“ã¨ã«ã¤ã„ã¦è€ƒãˆã¦ã¿ãŸã‚‚ã®ã§ã™ã€‚ (ãƒãƒ¼ãƒ ã«sqlfmtã‚’å°Žå…¥ã™ã‚‹ãŸã‚ã«æ›¸ã„ã¦ã‚‹ã‚‚ã®ã«ãªã‚Šã¾ã™) sqlfmtã«ã‚ˆã£ã¦ã©ã®ã‚ˆã†ãªèª²é¡Œã‚’解決ã—ãŸã„ã®ã‹ 大ããã¯ã“ã‚Œã«é›†ç´„ã•ã‚Œã‚‹ã‹ãªã¨æ€ã„ã¾ã™ã€‚ ã§ã¯ã‚¹ã‚¿ã‚¤ãƒ«å‘¨ã‚Šã«ã‚る開発者生産性を阻害ã™ã‚‹è¦å› ã¨ã¯ã©ã®ã‚ˆã†ãªã‚‚ã®ã‹ã¨ã„ã†ã¨: èªã¿ã¥ã‚‰ã„SQLã«ã‚ˆã‚‹ãƒã‚°ã®ç™ºè¦‹ã®é…ã‚Œ ãƒã‚¸ãƒƒã‚¯å‘¨ã‚Šã«ã¯é–¢ä¿‚ã®ãªã„箇所ã®ãƒ¬ãƒ“ューをã™ã‚‹å¿…è¦æ€§ SQLスタイルã®ã‚¹ã‚¿ãƒ³ã‚¹ã®é•ã„ã«ã‚ˆã‚‹è¡çª ã¨ã„ã†ã‚‚ã®ãŒã‚ã‚Šã¾ã™ã€‚ ã“れをsqlfmtãªã‚‰ã©ã†è§£æ±ºã§ãã‚‹ã‹ã‚’紹介ã—ã¾ã™ã€‚ sqlfmtãªã‚‰ã©ã†è§£æ±ºã§ãã‚‹ã‹ èªã¿ã¥ã‚‰ã„SQLã«ã‚ˆã‚‹ãƒã‚°ã®ç™ºè¦‹ã®é…ã‚Œ ã“ã‚Œã¯ãã‚‚ãã‚‚formatterã‚’å°Žå…¥ã—ã¦ã„ãªã„ã“ã¨ã«ã‚ˆã‚Šç”Ÿã˜ã‚‹ã‚‚ã®ã‚’想定ã—ã¦ã„ã¾ã™ã€‚ ã‚„ãŸã‚‰ã¨é•·ã„一行ã€ã‚¹ãƒšãƒ¼ã‚¹ã®ç„¡ã„濃密ãªä¸€è¡Œã€æƒã‚ãªã„インデントã€ç„¡æ„味ãªæ”¹è¡Œ...ãªã©ã«ã‚ˆã‚Šã€ä¸ç”¨æ„ã«
methexis-inc/img2prompt – Run with an API on Replicate
Run time and cost This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 32 seconds. The predict time for this model varies significantly based on the inputs. Model description Provides approximate text prompts that can be used with stable diffusion to re-create similar looking versions of the image/painting. Try it by copying the text prompts to stable diffusion! A sligh
Stable Diffusionã‚’ã„らã™ã¨ã‚„ã§ãƒ•ã‚¡ã‚¤ãƒ³ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã™ã‚‹
å··ã§è©±é¡Œã®Stable Diffusion（以下SD）をファインãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã™ã‚‹æ–¹æ³•ãŒå…¬é–‹ã•ã‚ŒãŸã®ã§ã€æ—©é€Ÿã‚„ã‚Šã¾ã™ã€‚
Stable Diffusion Public Release — Stability AI
It is our pleasure to announce the public release of stable diffusion following our release for researchers [https://stability.ai/stablediffusion] Over the last few weeks, we all have been overwhelmed by the response and have been working hard to ensure a safe and ethical release, incorporating data from our beta model tests and community for the developers to act on. In cooperation with the tirel
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GitHub - openai/swarm: Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.
GitHub - beeware/briefcase: Tools to support converting a Python project into a standalone native application.
GitHub - facebook/memlab: A framework for finding JavaScript memory leaks and analyzing heap snapshots
PlayTorch is a framework for rapidly creating mobile AI experiences.
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