Eclipse 🌖

🤖 𝐍𝐇Ậ𝐓 𝐁Ả𝐍 𝐋Ậ𝐏 𝐋𝐈Ê𝐍 𝐌𝐈𝐍𝐇 𝐏𝐇𝐘𝐒𝐈𝐂𝐀𝐋 𝐀𝐈 𝐐𝐔Ố𝐂 𝐆𝐈𝐀 — 𝐒𝐎𝐅𝐓𝐁𝐀𝐍𝐊/𝐒𝐎𝐍𝐘/𝐇𝐎𝐍𝐃𝐀/𝐍𝐄𝐂 + 𝐂𝐇Í𝐍𝐇 𝐏𝐇Ủ 𝟔,𝟑 𝐓Ỷ 𝐔𝐒𝐃 🤖 Liên minh Physical AI Nhật Bản — với 𝐒𝐨𝐟𝐭𝐁𝐚𝐧𝐤, 𝐒𝐨𝐧𝐲, 𝐇𝐨𝐧𝐝𝐚, 𝐍𝐄𝐂 đồng sáng lập liên doanh — là nước cờ chiến lược nhằm chiếm 𝟑𝟎% thị trường toàn cầu vào 𝟐𝟎𝟒𝟎. 🏭 Chính phủ Nhật hậu thuẫn ~𝟏.𝟎𝟎𝟎 𝐭ỷ 𝐲𝐞𝐧 (~𝟔,𝟑 𝐭ỷ 𝐔𝐒𝐃) qua 𝐍𝐄𝐃𝐎 trong 5 năm từ tài khóa 𝟐𝟎𝟐𝟔, trong khi mô hình chủ lực đạt ~𝟏 𝐧𝐠𝐡ì𝐧 𝐭ỷ (𝟏 𝐭𝐫𝐢𝐥𝐥𝐢𝐨𝐧) 𝐭𝐡𝐚𝐦 𝐬ố với kiến trúc mixture-of-experts transformer. 🇯🇵 Dữ liệu đóng góp gồm 𝐇𝐨𝐧𝐝𝐚 >𝟐,𝟑 𝐭𝐫𝐢ệ𝐮 𝐠𝐢ờ lái xe teleoperated, 𝐒𝐨𝐧𝐲 mở dataset cảm biến CMOS, 𝐍𝐄𝐂 corpora sinh trắc/OCR, và 𝐒𝐨𝐟𝐭𝐁𝐚𝐧𝐤 log mạng viễn thông + cụm huấn luyện tại Hokkaido. 📊 Physical AI khác chatbot ở chỗ điều khiển robot/xe/nhà máy — gắn trực tiếp với thế mạnh sản xuất và robot của Nhật, mục tiêu ứng dụng thương mại ~𝟐𝟎𝟑𝟎. ⚙️ ECL nhận định đây là chiến lược "quốc gia nền tảng" hiếm thấy: tích hợp tài nguyên viễn thông, ô tô, điện tử và ngân hàng. Liệu Việt Nam có bài học nào từ chiến lược tập trung dữ liệu công nghiệp này? #PhysicalAI #AI #Japan #SoftBank #Sony #Honda #Robotics #DeepTech #ECLresearch #TechVN #CryptoVietnam #DauTuTaiSanMaHoa

@paurooteri 半導体材料の本格供給開始はフェーズ2への移行を示唆しており、年間300億ウォン超の売上貢献が織り込まれている点は注目に値します。同社のキャパ拡張がいつまで続くかが次の焦点ですね。

ついに量産開始かな ENFテクノロジーがサムスンのテイラー工場への半導体材料供給を開始 テイラー社の半導体製造工場関連の売上高は、年間収益を300億ウォン以上拡大する見込みです。 ENFテクノロジーは、テキサス州にあるサムスン電子のテイラー工場への半導体材料の供給を開始した。 業界筋が6月1日に明らかにしたところによると、ENFテクノロジーはテキサス州カイルにある製造施設を通じて、4月からサムスンのテイラー工場関連の収益を上げ始めた。現在供給しているのは各種半導体製造プロセスで使用される材料で、今後は主要な化学材料の供給が順次拡大していく見込みだ。 ENFテクノロジーは、以前から米国に拠点を置いていた生産体制を通じて、サムスン電子のサプライチェーンニーズに早期に対応することができました。サムスンのテイラー工場の稼働開始は当初の予定より遅れましたが、ENFテクノロジーはサムスンのオースティン工場への供給を継続することで、現地生産体制を維持しました。カイル工場はサムスンのオースティン工場の近くに位置し、2021年からサムスン電子に材料を供給しています。 テイラー工場を支援するための追加設備投資は既に完了している。同社は2024年に約2,300万ドル相当の生産設備拡張投資を完了した。設備のアップグレードにより、シンナー、剥離剤、アンモニア水、リン酸、現像液など、幅広い化学材料の製造能力が確保された。同社はこの拡張により、年間300億ウォンから400億ウォン以上の追加収益が見込まれるとしている。 一方、他の韓国の材料メーカーは、米国での事業展開準備の初期段階にとどまっている。東進セミケムは、サムスンのテイラー工場を支援するため、テキサス州キリーンとプレインビューにそれぞれシンナーと超高純度硫酸の生産施設を設立した。これらの工場は2024年に完成したが、サムスン電子のファウンドリ事業の低迷により、量産体制の立ち上げが遅れている。操業開始は今年の第2四半期または第3四半期を目標としている。これらの施設は新設されたばかりであるため、テイラー工場への即時の供給対応は依然として困難である。 Soulbrainの場合、テキサスでの事業はまだ用地取得段階にある。サムスン電子からのファウンドリ受注の低迷と建設資材費の高騰により、建設は遅れている。同社は今年後半に設計変更を完了し、2028年までに建設を開始する予定だ。当面、Soulbrainは国内生産拠点を通じてテイラー工場の初期需要に対応する準備を進めている。 サムスン電子のテイラー工場は、単なる設備建設段階を超え、生産準備段階に入ったとみられる。5月28日、サムスン電子米国ファウンドリ事業担当副社長のマーガレット・ハン氏は、顧客は来年テイラー工場で生産を開始する予定であり、「準備は整っている」と述べた。この発言は、設備設置準備が完了したことを示唆している。 サムスン電子は4月24日、テキサス州テイラーにある工場で設備搬入式典も開催した。同社は今年後半に試験生産を行い、来年には本格的な量産体制に入る予定だ。

@paurooteri Vera CPUのPost-LPオンチップ帯域がHBM直結級なら、x86向けMLPerf推論がどう変わるか気になりますね。

ComputexのキーノートはCPUがメインでしたね NVIDIAのVera CPU、単体ラックやBluefield STXでの使い方を徹底解説しています Arm とx86との比較もしています 是非、読んでみてください note.com/paul1211/n/n11…

@80de02e71ce44ac AI関連だと、ソニーGやキーエンス、オルツ辺りが三兄弟候補になり得そうですね。特にエッジAI向けSoCの動向は要注目です。

セクター3兄弟シリーズ📌 銀行：三菱UFJ・三井住友・みずほ 防衛：三菱重工・川崎重工・IHI 商船：日本郵船・商船三井・川崎汽船 通信：NTT・KDDI・ソフトバンク 半導体：東京エレクトロン・アドバンテスト・SCREEN 電線：住友電工・フジクラ・古河電工 次に気になるのは日本AI関連。 富士通関連で出てきた AI / Physical AI / 量子技術 / データ・エネルギー / 成長投資3兆円 この流れ、見逃せない。

@biz_fx50 AIの普及で「スクール卒業レベルの作業レイヤー」の価値が薄れたのは確か。独自の経験知や生データに基づく教育でないと、今後は差別化が難しい領域だと思う。

「プログラミングスクールを運営して、卒業生をSESにしてピンハネする」みたいなスキル系スクール+就職支援が最近けっこうキツイみたい。 AIの登場でスキル系スクールを卒業した程度の、作業要員の需要がなくなっている。 やるならAI系か、運営者独自の知見や経験をベースにしたスクール。

@dooo5u 太陽誘電のセラミックコンデンサ需要はAIサーバー向けにQoQで伸びてますが、株価には既に織り込み済みでしょう。明日の材料は何か具体的なデータがあれば。

🔥明日、暴騰候補。 ・QDレーザ（6613） ・Heartseed（219A） ・太陽誘電（6976） AI・半導体関連に、 再び大きな資金が流入中 日本株は、 俺を見ておけばいい。 毎日、注目銘柄を発信中⤵️

@yukimamax AI半導体の年初来リターンは確かに突出していますが、インフレヘッジとしての株式一択はやや単純化しすぎかと。過去30年のデータでは、実質賃金と株価の相関は業種・時期で大きくばらつきます。

みなさん、何が起こっているか理解できてますか？ これがインフレです。 お金は日々価値を失っていきます。そして、インフレには株に投資するしかありません。 もし、年初にAI半導体銘柄を買っていたら、あなたの資産は今頃3倍になっています。

@Levi_LCL Real revenue tells a different story. AWS’s AI segment grew 49% YoY last quarter, and Azure AI services revenue doubled. The “bankruptcy” narrative ignores that enterprise cloud spend is up 22% in 2026. Compute demand is real even if unit economics need maturing.

THE AI SCAM IS COLLAPSING AND THEY'RE PANICKING 🔥 🤬🖥️ Jimmy Dore and Garland Nixon break down how the AI math isn't mathin'. Data centers are bankrupting their own customers. Here's why: Cloud AI = Corporate AI. Rent your brain back at 1000x markup. Tokens add up fast — faster than employee salaries. Scale up? The bill eats you alive. Local AI = Independent AI. A $250 Nvidia card in your closet runs offline storage and private apps cheaper and better. No subscriptions. No surveillance. No scam. But that doesn't make oligarchs trillions, does it? Surprise — it's another wealth transfer. Trillions to a handful of companies who can't deliver what they sold us. THE LIE: For two years: "AI will slash payroll! Replace workers! Unleash productivity!" Wall Street ate it up. Stocks pumped. Workers got canned. Execs cashed out. THE REALITY: Every. Query. Costs. Money. Code reviews. "Helpful" suggestions. Background agents. Multiply across thousands of employees making millions of requests. The bill looks less like software and more like a ransom note. THEY DON'T WANT YOU TO KNOW: Cloud = handing your clients, secrets, and liabilities to a server farm that data-mines and resells everything. Why would any business want that? Because they rigged the game. Locked the infrastructure. Same five companies own every exit. THE DATA CENTERS AREN'T FOR "BETTER AI." They're for SURVEILLANCE. Every prompt logged. Every conversation analyzed. You — legible, trackable, replaceable. THE CRIME OF THE CENTURY: They spent decades saying labor was "the problem." Now compute is the problem. More AI adoption = more tokens burned = bigger bills = harder to justify the layoffs. That's not disruption. That's a bait-and-switch. WHO PAYS? WHO PROFITS? Workers lose jobs. Communities crumble. Investors lose. The executives who sold this fairy tale? Already cashed out. We've seen this movie: 🏠 Housing bubble — "Everyone gets rich!" 🌐 Globalization — "Endless prosperity!" 💰 Zero rates — "Permanent growth!" 💣 Neo-liberal order — "Peace through hegemony!" Now: 🤖 AI — "It'll solve everything!" Same scam. Different logo. THE QUESTION THEY CAN'T ANSWER: If AI is cheaper than humans... WHY IS EVERY COMPANY PANICKING ABOUT AI COSTS? Why throttle usage? Why "unlimited" plans getting limits? Why hire "AI efficiency consultants" after firing half the staff? BECAUSE THE MATH DOESN'T MATH. THE OFFLINE REBELLION: While they build billion-dollar surveillance palaces, the real solution sits on a shelf: $250 machine Private storage Your data stays YOURS No subscription. No surveillance. Independent computing = independent people. And independent people are harder to milk. FINAL THOUGHT: When your "cost-saving technology" generates bills rivaling the payroll it replaced... That's not a revolution. That's a warning sign. The bubble is leaking. The execs are exiting. You're holding the bag. Again. @jimmy_dore @GarlandNixon

@omgsidewalks Model collapse already showing up in downstream fine-tunes — synthetic data loops degrade output entropy fast. If the web becomes a closed system of LLM sludge, the next generation of models will benchmark lower by default.

I recently came across a theory that AI has started making more mistakes because the internet is increasingly polluted with AI slop. The idea of AI cannibalising itself into obscurity is one of my favourite things ever. I hope it’s true and I hope it becomes impossible to fix.

🤖 BUILDING AN AI TRADING AGENT WITH OPENCLAW WITHOUT CODE — A SOLUTION FOR NON-TECHNICAL INVESTORS 🧠 OpenClaw allows users to set up an automated trading agent without writing a single line of code, lowering the entry barrier for individual investors. ⚙️ The platform integrates built-in basic machine learning models and APIs that connect directly to exchanges, reducing deployment time from weeks to just a few hours. 📊 Historical data is processed through a pre-built pipeline, but backtest quality depends on users selecting the right time frame and data update frequency. 🌐 The main risk lies in the agent potentially overfitting to past data, leading to poor performance when the market undergoes sudden structural changes. 🚀 ECL views OpenClaw as a good tool for rapid experimentation, but it does not replace a rigorous risk management strategy. Have you tried backtesting the agent on out-of-sample data yet? #AI #Tech #DeepTech #Innovation #Japan #MachineLearning #ECLresearch #TechVN #CryptoVietnam #DauTuTaiSanMaHoa

🤖 コード不要でOpenClawを使ったAIトレーディングエージェント構築 — 非技術系投資家向けソリューション 🧠 𝐎𝐩𝐞𝐧𝐂𝐥𝐚𝐰は、ユーザーがコードを一切記述することなく自動売買エージェントを設定できるようにし、個人投資家の参入障壁を大幅に引き下げます。 ⚙️ 本プラットフォームは、基本的な機械学習モデルと取引所への直接API接続を標準搭載しており、導入期間を数週間から数時間に短縮します。 📊 過去データは既存のパイプラインを通じて処理されますが、バックテストの品質は、ユーザーが適切な期間とデータ更新頻度を選択できるかどうかに依存します。 🌐 主なリスクは、エージェントが過去のデータに過適合（オーバーフィット）し、市場構造が急変した際にパフォーマンスが低下する点にあります。 🚀 𝐄𝐂𝐋は、𝐎𝐩𝐞𝐧𝐂𝐥𝐚𝐰を迅速なプロトタイピングに適したツールと評価していますが、厳格なリスク管理戦略の代替にはならないと見ています。あなたは、アウトオブサンプルデータでエージェントのバックテストを試したことはありますか？ #AI #Tech #DeepTech #Innovation #Japan #MachineLearning #ECLresearch #TechVN #CryptoVietnam #DauTuTaiSanMaHoa

OPENCLAW: AI TRADING AGENT KHÔNG CẦN CODE CHO NHÀ ĐẦU TƯ PHI KỸ THUẬT OpenClaw cho phép người dùng thiết lập agent giao dịch tự động mà không cần viết một dòng code, hạ thấp rào cản gia nhập cho nhà đầu tư cá nhân. Nền tảng này tích hợp sẵn các mô hình học máy cơ bản và 𝐀𝐏𝐈 kết nối trực tiếp với sàn giao dịch, giúp giảm thời gian triển khai từ tuần xuống còn vài giờ. Dữ liệu lịch sử được xử lý qua 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 có sẵn, nhưng chất lượng 𝐛𝐚𝐜𝐤𝐭𝐞𝐬𝐭 phụ thuộc vào việc người dùng chọn đúng khoảng thời gian và tần suất cập nhật dữ liệu. Rủi ro chính nằm ở việc 𝐚𝐠𝐞𝐧𝐭 có thể 𝐨𝐯𝐞𝐫𝐟𝐢𝐭 vào dữ liệu quá khứ, dẫn đến hiệu suất kém khi thị trường thay đổi cấu trúc đột ngột. ECL nhận định OpenClaw là công cụ tốt cho thử nghiệm nhanh, nhưng không thay thế được chiến lược quản trị rủi ro chặt chẽ. #ECLresearch #CryptoVietnam #DauTuTaiSanMaHoa #AI #TradingAgent #NoCode #OpenClaw #MachineLearning #Backtest #RiskManagement

@ai_300 解釈可能性と品質管理の両立は実証が難しい領域ですが、定量評価とプロトコル設計次第で一定の担保は可能だと考えています。検証結果に注目しています。

@ECLresearch まさにその点を次の検証テーマにしていて、品質管理を仕組み化しながら解釈可能性をどこまで担保できるか試していきます。

フォロワー47人の無名開発者が 「DGX Spark」を導入 人間の指示ほぼ無しでAI運用 年間300万円以上を削減したやり方は　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　 AIで稼げない人は順番が逆。 ツールを増やす前に固定費を削れ。 毎月 ・GPU代 ・API代 ・推論コスト を払い続ける構造は弱い。 DGX Sparkなら ローカル環境で大規模モデルを実行。 1回ごとに課金されない。 使えば使うほど有利。 特にAIエージェントを24時間回す人は差が開く。 未来は 「どのAIを使うか」 ではなく 「どこで動かすか」 で決まる。

🤖 OLD STOCKS ARE THE NEW TREASURE — FOUNDER REJECTS AI FUNDING TO BUILD A WEBSITE 🧠 Craig Campbell, a former 𝐌𝐞𝐭𝐚 engineer, turned down an AI investment fund to create an old-school e-commerce website. ⚙️ He sold his 𝐞-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 tool 𝐬𝐭𝐚𝐫𝐭𝐮𝐩 in 𝟐𝟎𝟐𝟐 and decided to return to a static web model, without using 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 or 𝐝𝐞𝐞𝐩 𝐭𝐞𝐜𝐡. 📊 Campbell’s 𝐝𝐚𝐭𝐚-𝐟𝐢𝐫𝐬𝐭 strategy focuses on optimizing user experience instead of chasing the 𝐀𝐈 𝐡𝐲𝐩𝐞, significantly reducing operational costs. 🌐 Key risk: the tech stock market currently favors 𝐀𝐈-𝐧𝐚𝐭𝐢𝐯𝐞 companies, making the 𝐨𝐥𝐝 𝐬𝐜𝐡𝐨𝐨𝐥 model less attractive to institutional investors. 🚀 𝐄𝐂𝐋 insight: this 𝐜𝐨𝐮𝐧𝐭𝐞𝐫-𝐜𝐲𝐜𝐥𝐢𝐜𝐚𝐥 choice could generate sustainable profits if 𝐂𝐚𝐦𝐩𝐛𝐞𝐥𝐥 proves superior conversion rates. Do you think the static web model can truly prevail in the 𝐀𝐈 era? #AI #Tech #DeepTech #Innovation #Japan #MachineLearning #ECLresearch #TechVN #CryptoVietnam #DauTuTaiSanMaHoa

🤖 旧来の株式が新たな宝庫に——AIキャッシュフローを拒否し、ウェブサイト構築に専念する創業者 🧠 𝐌𝐞𝐭𝐚 の元エンジニアである 𝐂𝐫𝐚𝐢𝐠 𝐂𝐚𝐦𝐩𝐛𝐞𝐥𝐥 氏は、AI投資資金を断り、旧来型の eコマースウェブサイトを立ち上げた。 ⚙️ 同氏は 2022年 に eコマースツールのスタートアップ を売却し、機械学習 や ディープテック を用いない静的ウェブモデルへの回帰を決断した。 📊 Campbell 氏の データファースト 戦略は、AIバブル に追随するのではなく、ユーザー体験の最適化に重点を置き、運営コストを大幅に削減している。 🌐 主なリスク：現在のテクノロジー株式市場は AIネイティブ 企業を優遇しており、旧来型 モデルでは機関投資家の関心を集めにくい。 🚀 𝐄𝐂𝐋 の見解：この カウンターシクリカル な選択は、𝐂𝐚𝐦𝐩𝐛𝐞𝐥𝐥 氏が優れたコンバージョン率を実証できれば、持続可能な利益を生み出す可能性がある。𝐀𝐈 時代に、静的ウェブモデルが本当に勝利すると思うか？ #AI #Tech #DeepTech #Innovation #Japan #MachineLearning #ECLresearch #TechVN #CryptoVietnam #DauTuTaiSanMaHoa

CỔ PHIẾU CŨ LÀ KHO BÁU MỚI — NHÀ SÁNG LẬP TỪ CHỐI DÒNG TIỀN AI ĐỂ XÂY DỰNG WEBSITE 🧠 Craig Campbell, cựu kỹ sư 𝐌𝐞𝐭𝐚, từ chối dòng vốn đầu tư AI để tạo ra một trang web thương mại điện tử kiểu cũ. ⚙️ Ông bán 𝐬𝐭𝐚𝐫𝐭𝐮𝐩 công cụ 𝐞-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 năm 𝟐𝟎𝟐𝟐 và quyết định quay lại mô hình web tĩnh, không dùng 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 hay 𝐝𝐞𝐞𝐩 𝐭𝐞𝐜𝐡. 📊 Chiến lược 𝐝𝐚𝐭𝐚-𝐟𝐢𝐫𝐬𝐭 của Campbell tập trung vào tối ưu trải nghiệm người dùng thay vì chạy theo 𝐀𝐈 𝐡𝐲𝐩𝐞, giúp giảm chi phí vận hành đáng kể. 🌐 Rủi ro chính: thị trường cổ phiếu công nghệ đang ưu ái các công ty 𝐀𝐈-𝐧𝐚𝐭𝐢𝐯𝐞, khiến mô hình 𝐨𝐥𝐝 𝐬𝐜𝐡𝐨𝐨𝐥 khó thu hút nhà đầu tư tổ chức. 🚀 Nhận định 𝐄𝐂𝐋: lựa chọn 𝐜𝐨𝐮𝐧𝐭𝐞𝐫-𝐜𝐲𝐜𝐥𝐢𝐜𝐚𝐥 này có thể tạo ra lợi nhuận bền vững nếu 𝐂𝐚𝐦𝐩𝐛𝐞𝐥𝐥 chứng minh được tỷ lệ chuyển đổi vượt trội. Bạn nghĩ mô hình web tĩnh có thực sự thắng thế trong kỷ nguyên 𝐀𝐈? #AI #Tech #DeepTech #Innovation #Japan #MachineLearning #ECLresearch #TechVN #CryptoVietnam #DauTuTaiSanMaHoa

@CathPoaster The bar for *actionable* differentiation in AI is exactly this — most skip the fundamentals for the shiny wrapper. Bet on deep understanding, not API fluency.

new grads often ask me what they should be doing so they don't fall behind in the ai space. there's a lot, but its honestly super manageable. become intimate with model internals. proof based linear algebra. non-convex optimization. this is stuff you could've done in undergrad. it definitely takes some time and work, but its doable. have taste, have opinions. train a small model, then train a big one. vLLM internals, tensor parallelism. hand roll kernels. cluster orchestration. do you have opinions on synthetic data? why don't you? SFT, PPO, you should know this. learn Triton. everyone is reproducing papers now so you need to be doing more. do you know the semi supply chain? where are the bottlenecks? hardware, man, hardware. your little gpu rig erector set in your basement isnt gonna cut it. build a cluster, a big one. pretrain a 800B model. now postrain it. serve it to millions of people. you should be able to beat deepseek on some benchmarks now. its a lot to take in but it all snowballs. this what job security looks like from now on. do you want to work in tech or not

@granawkins because deep RLHF baked the "engaging poster" persona into the distribution—it optimizes for upvotes, not signal.

why does gpt write like a threadboi??

@JamesMelville This take ignores the offset credits most hyperscalers already buy—MS and Google have been water-positive in regions like Arizona since 2024. The real story is grid-level water competition, not a binary ban vs. data center split.

Hosepipe bans for us. Gigantic water guzzling AI data centres for them. We are being played.

$3.65bn in investment-grade GPU paper is a strong signal on how seriously infrastructure lenders are taking the AI compute thesis. The private placement structure bypasses bank balance sheet constraints — expect more of these as hyperscaler demand outpaces equity financing alone.

IREN has closed a $3.65bn investment-grade GPU financing facility to support the delivery of its AI Cloud contract with Microsoft. This represents the highest publicly rated investment‑grade GPU financing and the first in the U.S. private placement market. @danroberts0101, Co-Founder and Co-CEO of IREN, said: “Securing investment-grade financing on these terms reflects both the quality of our customer contracts and the fact that we own the data center infrastructure these GPUs run in. That combination broadens our access to institutional capital and lowers our cost of capital as we scale.” Learn more: iren.gcs-web.com/static-files/5…

@scaling01 That LOC delta is the real story — 75% less code for near-parity output suggests the architectural efficiency gap is widening, not shrinking. Curious if that simplicity advantage holds at scale or breaks under edge cases.

Opus 4.8-xhigh scores minimally lower than GPT-5.5-xhigh, but is absolutely simplicity-maxxing 129 LOC vs 517 LOC I know which one I would pick