apstndbと書いてOSI参照モデル<アプセトネデブ>と読む GCP Watcher / #GDE of ☁️

Japan
Joined December 2009
【重要なお知らせ】 BOOK☆WALKERでは、現在緊急でメンテナンスを実施中です。 今回のメンテナンスは、利用しているソフトウェアに重大な脆弱性が確認されたことを受けたもので、直接的な攻撃を受けたわけではございません。 メンテナンス中は、BOOK☆WALKERの全てのサービスをご利用いただけません。 皆様にはご不便をおかけし申し訳ございません。 ◆サービス再開予定 2026/10/22(木)早朝までには再開する予定です ※時間は前後することがあります。ご了承ください。 ご迷惑をおかけしますが、ご理解の程よろしくお願いいたします。
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おっ GQL set operation の column propagation mode がドキュメントに載っている github.com/apstndb/spanner-d…
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DuckDB 2.0 is coming tomorrow!
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まじで今まで知らなかったから震えてる。 スバル大発見チャンネルすぎる。
見えない状態だとモンスターハウスに入っても襲われないのか、スーパーファミコン版で検証してみた。 まじか
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🚨🚨🚨Cyber Alert ‼️ 🇺🇸USA - 𝗙𝘂𝗹𝗰𝗿𝘂𝗺𝗦𝗲𝗰 𝗖𝗹𝗮𝗶𝗺𝘀 𝟭𝟬.𝟲 𝗧𝗕 𝗗𝗮𝘁𝗮 𝗕𝗿𝗲𝗮𝗰𝗵 𝗮𝘁 𝗣𝗶𝗻𝗴𝗖𝗔𝗣 𝗜𝗻𝗰. (𝗧𝗶𝗗𝗕 𝗖𝗹𝗼𝘂𝗱), 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗘𝘅𝗽𝗼𝘀𝗶𝗻𝗴 𝗠𝗮𝗷𝗼𝗿 𝗚𝗹𝗼𝗯𝗮𝗹 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 The FulcrumSec hacker group claims to have breached PingCAP (TiDB Cloud), allegedly exfiltrating 10.6 TB of data comprising approximately 4.9 million files. According to the threat actor, the compromised data includes production databases, source code, customer records, credentials and API keys, potentially affecting over 180,000 organisations. Companies named in the allegedly stolen records include Booking, Shopee, CAPCOM, Colopl, TOPPAN, Catalyst Software, SB Payment Service, Mercari, PayPay, Fujitsu, SoftBank, DMM, AEON, Square Enix, Konami, BANDAI NAMCO, Delhivery, Flipkart, Quincus, Nylas, Airbnb, Pinterest, Databricks, Bitget, Pionex, DigiPlus, NFTscan, Baidu, SES-imagotag, Persona, Transsion, ZOZO, Gumi, Iret, Classmethod, Wetech, Atlassian, Cygames, SBI, LINE, Chainbase, and PatSnap. Additional companies referenced in other allegedly compromised records include Accenture, Red Bull, Klook, Tencent, Huawei, ByteDance, Mastercard, AIG, and Amazon. The group also claims to have released an initial 200 GB dataset and threatens further disclosures. Threat actor: FulcrumSec Sector: ICT Data exposure (claimed): 10.6 TB (4.9 million files) Data type: Production databases, source code, customer records, credentials and API keys Observed: Oct 19, 2026 Status: Pending verification ESIX©: 7.70 Full details and impact assessment on HackRisk.io
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先ほど公開されたDeepSeek-V4.1-Flash-1017のベンチマークスコアが完全におかしいことになっている(アクティブパラメータが8B、16Bで、GPT-6とOpus5、Fableにパフォーマンス、コストともに圧勝) 元々アメリカのフロンティアAIラボが荒れていたタイミングだったので、DeepSeekショック再びの可能性
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オオハナが唐辛子の種をもりもり食べる動画です🔥
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Mythos Preview発表時点の商用フロンティアモデルよりちょっと強いくらいでも守るのは大変なんだよなあ
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出た当時のGLM-5.2/5.3は明らかにフロンティアモデルで、今年のモデルリリース後出しじゃんけんが加速してるから微妙に見えるけどGPT-5.5やOpus4.7より強いのでこれがVRAMさえあれば無料で使えてるのは異常。 逆にOpenAI次世代モデルによるHugging Faceハッキング事件はGLMが無かったら詰んでいたとも。
GLM 5.3 は最先端のフロンティアモデルというわけではなく、SFTを外しやすいモデルってだけで、攻撃にはその程度で十分って話では
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Otsuka Ai 敵だね Otsuka Si 味方だね
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手の中で寝てしまった、、、 #ウロコインコ #インコブリーダー
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Luna よりも安くなることすらないのはちょっと不思議
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コンテキストサイズが大きいとコストが跳ね上がるからベンチマークによっては設定変えないとそうなるか
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(2/4) All effort levels + score vs. time + score vs. cost + score vs. turns attached. BTW. Sonnet 5.5 (max) still wins by running 7x more turns than GPT-6 Astra. It's the most persistent model I've benchmarked. It's also the most expensive and the slowest on the board. Probably a wrong calibration by Anthropic, it makes it useless in practice.
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SESSION_USER が Spanner のドキュメントに載ってる。 row level security 的なやつをやるための下地がやっと…というところか docs.cloud.google.com/spanne…
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Definer's rights views と組み合わせて最低限なんとかという感じに…
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Anthropic has released Claude Haiku 5.5, scoring 43 on the Artificial Analysis Intelligence Index - up 26 points one year after the last Haiku release Haiku 5.5 is the first Haiku model with Anthropic’s effort settings and adaptive thinking, and Anthropic has introduced tiered pricing. Haiku 5.5 is cheaper than its predecessor - it costs $0.10/$0.50 per 1M input/output tokens for prompts up to 100k tokens (the same as GPT-6 Luna and 10% of the previous Haiku model). However, this pricing rises 5x to $0.50/$2.50 above 100k. The site does not yet reflect tiered pricing, so provisional cost figures for Haiku 5.5 do not include the step up cost. We are working on support and will follow up with Cost per Task coverage soon. Key takeaways: ➤ Leading small-class model performance: At max effort Haiku 5.5 sits slightly ahead of models such as GLM-5.3 Flash (42), Gemini 3.8 Flash (41) and GPT-6 Luna (38). Its score is comparable to Kimi K3 (44), a 2.8T parameter open weights model, and trails Claude Sonnet 5.5 (max, 56) by 13 points ➤ Heavy token use compared to GPT-6 Luna: Haiku 5.5 (max) uses ~162k output tokens per Intelligence Index task, ~3x GPT-6 Luna (max, ~50k). Moving from xhigh to max adds 2 points for ~1.8x the tokens. At similar intelligence it also uses more tokens than GPT-6 Luna: Haiku 5.5 (high) scores 38 with ~55k tokens per task against 38 with ~50k for Luna (max), and the gap widens at lower effort settings ➤ Highly capable at agentic knowledge work: on AA-Briefcase, our private evaluation for realistic knowledge work tasks, Haiku 5.5 (max) reaches 1578 Elo, ahead of models including Kimi K3 and GLM-5.3, and comparable to Muse Spark 1.3 (max) ➤ Improvements on terminal use: on Terminal-Bench 4.0 it scores 33%, up from 0% for Haiku 4.5. This is level with GLM-5.3 Flash, and ahead of Gemini 3.8 Flash (20%) and GPT-6 Luna (13%) ➤ Lower factual knowledge, but relatively low hallucinations: as expected for a smaller-class model, Haiku 5.5 has lower factual knowledge than its siblings. AA-Omniscience accuracy is 36%, against 55% for Gemini 3.8 Flash and 44% for GPT-6 Luna, but this is partly driven by more willingness to admit when it doesn’t know - its hallucination rate is lower, at 40% against 55% and 77% ➤ AutomationBench-AA result likely understated: Haiku 5.5 scores 35%, against 53–60% for GPT-6 Luna, Gemini 3.8 Flash and GLM-5.3 Flash. During pre-release testing, a safety refusal issue caused the model to over-refuse. Anthropic is working on resolving this - we will re-run this evaluation with the fix, and expect this score to rise Other model details: ➤ Context window: 1 million tokens, up from 200k for Claude 4.5 Haiku ➤ Pricing: $0.10/$0.50 per 1M input/output tokens up to 100k tokens, $0.50/$2.50 above. Cache reads $0.01 ($0.05 above 100k), 5 minute cache writes $0.125 ($0.625 above 100k) ➤ Multimodality: Text and image input, with text output
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「いくさは嫌にございます」 を打ち出すとクソ大河認定されやすいが、鎌倉殿は視聴者のほうが勝手に 「いくさは嫌にございます!! もうやめてくださいませ!!」 になっていきます。
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Day 2.4/ Decisions API is live. We will use this ourselves to improve the experience for everyone in many fun ways. Realtime general classifier does open some great possibilities.
Let your app choose the right model, tool, or action in near real-time with Decisions API, now available to all developers in public beta. The Decisions API makes decisions up to 10x faster than GPT-6 Luna through the Responses API.
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らき⭐︎すたの番宣CMとかニッチ過ぎる
This quoted post is unavailable.
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