Anthropic Leaks Claude Code, a Literal Blueprint for AI Coding Agents - eweek.com
<a href="https://news.google.com/rss/articles/CBMiekFVX3lxTE4wQVkzQVlOWnBaVWozLWNBdUpYakZKYWFtNFI2YnZoaEFiNjFfWVNrcWlYVFg1U244cVR5YXlTUlp3M3lJRWlxLV9rclJNaHg5SGlzZUxObDZGRG43VHBldkdUYmgtdldJVEdtbVlYVXZWdi1NMk1QT2dB?oc=5" target="_blank">Anthropic Leaks Claude Code, a Literal Blueprint for AI Coding Agents</a> <font color="#6f6f6f">eweek.com</font>
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KPMG: Inside the AI agent playbook driving enterprise margin gains - AI News
<a href="https://news.google.com/rss/articles/CBMipgFBVV95cUxNQzJ3VjFQbU5SbUlOR0dMZW1EZm5BRTRwaTlRNU8xWGFzNGNrdDBueUV6QTJqb2tiS1c1T2JFYUlYVXJYcDlaR2x1eWxxNi1DY1piQVF5UWdIbkc1NlpBejlJakVvZ2dWRUR2TTlaQXJDdGlKSHlTSnp0M2RZQzlKbWwtZS1nZkxQVHB3bXQzaW5TczJQaDljejhSZThWQk9SNUJCNm5n?oc=5" target="_blank">KPMG: Inside the AI agent playbook driving enterprise margin gains</a> <font color="#6f6f6f">AI News</font>
will.i.am teaching new Agentic A.I. course at ASU - FOX 10 Phoenix
<a href="https://news.google.com/rss/articles/CBMihgFBVV95cUxPY194cjhpRzREMTBVOHc3SzJMZEd2cUkwOWNOaTJUVm1rakRlRktRdXQ1UkZFUkJ5T2pnNEtTZVFVOGl0Z3k4RTB4MlVsZTlXOGptVzF6aTZIRUphOUpzX3hta1BHUGEtZVBPUURjSmVIRUwweDZPY0E1SnpDTHljZ2ZSQXA4UdIBiwFBVV95cUxOamFWVm5iTmJZNU5YMk4zVnRhRkVpT2RQNkJLM1NNYWFpcHdfV21MWVhQYVcwS1Y4OWZlYTlIQmVSNXhOWFk4Rzd4dlRXTU8wOXpxcWh4OE5yWjYzNURzNjdaa0J0MTlFMVcxbWhCN0hmZElISHJrSWdjY3N1RGdUbGZpLTV1MUc3U3Fz?oc=5" target="_blank">will.i.am teaching new Agentic A.I. course at ASU</a> <font color="#6f6f6f">FOX 10 Phoenix</font>
5 critical steps to achieve business resilience in cybersecurity
What does it really take to keep your organization running when attackers strike? The answer is business resilience—being able to detect, contain, and recover fast enough that disruptions are minimized, customers stay confident, and operations keep moving. From the latest 2026 State of the SOC Report , which is based on more than 900,000 alerts observed between March and December 2025 from the Adlumin Managed Detection and Response (MDR) provided by the N-able SOC, we’ve seen firsthand where security strategies succeed—and where they fall short. Below, we break down five actionable ways to build true resilience for your IT environment, using real-world data, strategic guidance, and frameworks that leading IT teams put into practice today. 1. Stop trusting single-layer security If you’re de
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「半歩先」こそが実装の急所ーー量子アニーリング7年の実践で見えた、日本企業が勝つための条件
プロフィール：最首英裕（さいしゅ えいひろ） 株式会社グルーヴノーツ 代表取締役社長・創業者。 早稲田大学第一文学部にて詩人の鈴木志郎康に師事。卒業後は、都市再開発事業のコンサルタントを経て、都市空間におけるIT基盤の企画・開発に取り組む。その後、米国Apple Computerの製品開発プロジェクトの日本対応開発責任者として、様々な製品開発を手掛ける。株式会社グルーヴノーツ設立後は、AIと量子コンピュータを活用したサービスを開発。金融・物流を中心に数多くの社会課題を解決。金融分野における高度なインテリジェンス機能の実現や、物流分野におけるインテリジェンスと量子コンピュータの融合などに取り組む。 技術ありきではない——課題起点の量子導入 グルーヴノーツの創業は2011年。最首氏はそれまで経営してきた自らの会社を売却し、福岡にあった会社を買収して社名を変更、新たなスタートを切った。社名は「Groove（演奏者と聴衆が互いに盛り上がり最高の演奏ができている状態）」と「nauts（航海士）」を組み合わせた造語で、顧客や関わる全ての人たちがわくわくでき、社会全体が可能性に満ちあふれるように——という思いを込めた。 技術の進化により、シンプルな構造で少人数の方が優れたシステムを作れる時代になった。にもかかわらず、現場は相変わらず人数と予算の規模を競っている。 「お金と時間をかけない方が良いものができるのに、なぜ日本のIT業界は人数と予算の規模を競うのか」——そんな問題意識から、創業以来、最先端技術をわかりやすく使えるプロダクト開発に取り組んできた。当初は量子ではなくAI分野に注力し、2017〜2018年頃にはディープラーニングの実装を進めていた。 量子との出会いは2018年。コールセンターの電話本数予測プロジェクトでのことだ。グルーヴノーツが作成した予測モデルの精度は高かったが、その
My most common advice for junior researchers
Written quickly as part of the Inkhaven Fellowship . At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece. Doing quick sanity checks Research is hard (almost by definition) and people are often wrong. Every researcher has wasted countless hours
Parameter Count Is the Worst Way to Pick a Model on 8GB VRAM
<h1> Parameter Count Is the Worst Way to Pick a Model on 8GB VRAM </h1> <p>I've been running local LLMs on an RTX 4060 8GB for six months. Qwen2.5-32B, Qwen3.5-9B/27B/35B-A3B, BGE-M3 — all crammed through Q4_K_M quantization. One thing I can say with certainty:</p> <p><strong>Parameter count is the worst metric for model selection.</strong></p> <p>Online comparisons rank models by size — "32B gives this quality," "7B gives that." Benchmarks like MMLU and HumanEval publish rankings by parameter count. But those assume abundant VRAM. On 8GB, parameter count fails to predict the actual experience.</p> <p>This article covers three rules I derived from real measurements, plus a decision framework for 8GB VRAM model selection. All data comes from <a href="https://qiita.com/plasmon" rel="noopener
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