Fixed local zeta2 edit predictions using the wrong prompt format. ( #56192 ) Fixed an issue where broken symlinks, permission errors, or other fs errors in watched directories could cause excessive CP
| Date | Count |
|---|---|
| 2026-05-05 | 34 |
| 2026-05-06 | 39 |
| 2026-05-07 | 65 |
| 2026-05-08 | 80 |
| 2026-05-09 | 154 |
| 2026-05-10 | 54 |
| 2026-05-11 | 131 |
EN Join the OpenAI Campus Network—connect student clubs worldwide, access AI tools, host events, and build an AI-powered campus community.
og EN How enterprises scale AI: from early experiments to compounding impact through trust, governance, workflow design, and quality at scale.
og AI要約 「ChatGPTにうまく答えてもらえない」を解決する5つのプロンプト技術【実例・コピペOK】 ChatGPT・Claudeを使い始めると、こんな悩みにぶつかりませんか？ 「指示したのに的外れな回答が来た」 「長文が必要なのに短くまとめられた
og AI要約 はじめに 2026年、AIエージェント開発における最重要プロトコルとして定着したMCP（Model Context Protocol）。AnthropicがClaudeに向けて開発したこの標準規格は、今やOpenAI・Google・Micr
og AI要約 はじめに 「AIに聞いても期待した回答が返ってこない」「毎回プロンプトをゼロから書いている」 そんな経験はないでしょうか？ プロンプトの書き方を少し変えるだけで、AIの出力品質は劇的に変わります。この記事では、エンジニアが日常業務でよく使う
og AI要約 AIエージェントを使いこなせていますか？ ChatGPT・Claude・Geminiを使い始めたものの、「何を入力すればいいかわからない」という壁にぶつかる人は多いです。 この記事では、業務で即使える実務プロンプトを3本、無料で公開します。
og AI要約 DemoSystems株式会社、AI搭載CRM「DemoCRM」の提供を開始 🚀 DemoSystems株式会社は、営業活動における顧客管理・対応履歴の確認・次回アクションの判断を支援するAI搭載CRM 「DemoCRM」 の提供を開始し
og AI要約 Zedエディタのnightlyビルドで、デバッグ時の自動ウォッチ機能(auto watch)が改善された。これは変数を自動的に監視式として登録する機能で、デバッグ体験の向上を狙ったものとみられる。
EN Zed's nightly build includes an improvement to the auto-watch feature (#56126), which automatically tracks variables in the debugger view, aimed at smoother debugging workflows.
media AI要約 こんな経験、ありませんか？ Claude CodeやCursorに「ログイン機能を追加して」と頼んだら、聞かずに勝手にJWT認証を実装されていた。 「このバグを直して」と頼んだら、バグ修正だけでなく周囲のコードまでリファクタリングされ、差分
og AI要約 はじめに Cursor / Claude Code / GitHub Copilot / Cline / Codeium — 2026年にはAIコーディングツールが多数登場しています。「結局どれを使えばいいのか」は、個人開発者や小規模チーム
og AI要約 【理論差異自覚プロトコル：RESOLUTION_CONTRAST】 [理論差異自覚プロトコル] 目的 = "要約で理解している用語と実際の理論との差異を比較し自覚すること" 用語 = "プロトコルエンジニアリング" 原本URL = "htt
og EN arXiv:2605.07453v1 Announce Type: new Abstract: Ancient and endangered languages pose a unique challenge for NLP: their datasets are inherently scarce, difficult to expand, and built from formulaic co
og EN arXiv:2605.06673v1 Announce Type: new Abstract: Aggregate metacognitive quality scores mask within-model variation across MMLU benchmark domains. We administered 1,500 MMLU items (250 per domain, unde
og EN arXiv:2605.06765v1 Announce Type: new Abstract: Human speech conveys expressiveness beyond linguistic content, including personality, mood, or performance elements, such as a comforting tone or hummin
og EN arXiv:2605.06832v1 Announce Type: new Abstract: Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assista
og EN arXiv:2605.06886v1 Announce Type: new Abstract: This work introduces TajPersLexon, a curated Tajik--Persian parallel lexical resource of 40,112 word and short-phrase pairs for cross-script lexical ret
og EN arXiv:2605.06897v1 Announce Type: new Abstract: The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. Whi
og EN arXiv:2605.06901v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, fi
og EN arXiv:2605.06903v1 Announce Type: new Abstract: Large language models are now embedded in everyday writing workflows, making reliable AI-generated text detection important for academic integrity, cont
og EN arXiv:2605.06919v1 Announce Type: new Abstract: Large language models have demonstrated impressive retrieval-augmented capabilities. However, a crucial area remains underexplored: their ability to app
og EN arXiv:2605.06940v1 Announce Type: new Abstract: Annotation automation via Large Language Models (LLMs) is the core approach for scaling NLP datasets; however, LLM behavior with respect to closed-set i
og EN arXiv:2605.06978v1 Announce Type: new Abstract: Skill-augmented agents increasingly rely on large reusable skill libraries, but retrieving relevant skills is not the same as presenting usable context.
EN arXiv:2605.07013v1 Announce Type: new Abstract: Diffusion language models (DLMs) promise parallel, order-agnostic generation, but on standard benchmarks they have historically lagged behind autoregres
og EN arXiv:2605.07040v1 Announce Type: new Abstract: Large language models (LLMs) are widely used for tutoring, feedback generation, and content creation, but their broad pretraining makes them hard to con
EN arXiv:2605.07051v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown good performance on various science educational benchmarks, demonstrating their potential for use in science and
EN arXiv:2605.07053v1 Announce Type: new Abstract: Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed t
EN arXiv:2605.07058v1 Announce Type: new Abstract: Real-world clinical diagnosis is a complex process in which the doctor is required to obtain information from both interaction with the patient and cond
EN arXiv:2605.07068v1 Announce Type: new Abstract: The LLM Wiki pattern, to compile and provide domain knowledge into a persistent artifact and serve it to LLMs via KV cache inference, promises context a
EN arXiv:2605.07076v1 Announce Type: new Abstract: Large language models (LLMs) increasingly receive information as streams of passages, conversations, and long-context workflows. While longer context wi
EN arXiv:2605.07084v1 Announce Type: new Abstract: Automatic speech recognition (ASR) evaluation compares system output to ground truth transcripts, with Word Error Rate (WER) quantifying the distance be