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I believe SendGrid's logging capabilities are too limited to be suitable for sending GPTS emails.

 I don't think there's a way to determine this, because GPTS cannot display the details of the response, and SendGrid is not designed to log responses during abnormal conditions. Sure — here is the English version, grounded in what the linked sources explicitly say. Your point (“there’s no way to identify the cause because GPTs can’t show the detailed HTTP response, and SendGrid doesn’t keep logs of failed API responses”) is largely true in a ‘GPTs calls SendGrid directly’ setup , because you lose observability at the exact place where the useful diagnostic data exists. What the URLs do show is: GPTs / Actions debugging is limited , and people report getting vague “Error …” style failures with little actionable detail. ( OpenAI Developer Community ) SendGrid does return a detailed error response on failure (HTTP 403 with a JSON errors array). In real examples of this exact error, the response includes field: "from" along with the message. ( Qiita ) SendGrid’s own ...
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Urania’s Diamond Era: Karel Thole Covers (1964–1967)

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 A visual tour of Urania issues 336–457, where Karel Thole’s circular illustrations and the “diamond” layout defined Italian SF design, alongside key editorial and publication shifts. https://www.quadernidaltritempi.eu/rivista/numero40/approdi/q40_cop_urania06.html This page is essentially a gallery and data sheet for a specific run of covers from Urania , a famous Italian science-fiction paperback series published by Mondadori. What the page is showing It focuses on the “diamond” (rombo) design phase of Urania , covering issue No. 336 through No. 457 . The top portion is a slideshow of cover images , then below it is a short block of publication details and a long grid of thumbnails (issues 336–457). Key facts summarized (in English) Time period covered: from 14 June 1964 to 12 March 1967 . Responsible editor (Direttore responsabile): Enzo Pagliara up to issue 447 , then Alberto Tedeschi starting with 448 . Editorial curator: Carlo Fruttero is joined by Fra...
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“Atic Atac” is an action-adventure game released in 1983 for the ZX Spectrum by Ultimate Play the Game

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 https://geni.us/wH5BzZU “ Atic Atac ” is an action-adventure game released in 1983 for the ZX Spectrum by Ultimate Play the Game (a company later connected to the lineage that became Rare). You explore a castle-like maze while avoiding (or sometimes fighting) enemies, collecting the pieces of the “Golden Key of ACG,” and then escaping. You can choose from three characters — the Wizard, the Knight, or the Serf — and each one differs in weapons and which secret passages they can access. If you want to play it today, you can use ZX Spectrum emulation to try the original version, and there are also modern remakes and derivative versions made by fans.
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NiFiはなぜFBPなのか——用語対応と歴史（GPSS→AMPS→DSLM）

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  Apache NiFiはシステム間のデータフローを自動化する基盤です。本稿はNiFiとフローベースプログラミング（FBP）の対応と、FBP史（GPSS/AMPS/DSLM）の要点を、原典に基づき手短に整理します。 Apache NiFiは、データルーティング、変換、システム調停ロジックの強力でスケーラブルな有向グラフをサポートする。 簡単に言えば、NiFiはシ ステム間のデータフローを自動化するために作られた。 「データフロー」という言葉は様々な文脈で使われますが、ここではシステム間の情報の流れの自動化・管理という意味で使っています。この問題領域は、企業が複数のシステムを持ち、あるシステムはデータを作成し、あるシステムはデータを消費するようになってから、ずっと存在してきました。このような問題や解決方法は、これまでにも数多く議論され、明確にされてきました。包括的で容易に利用できる形式は、Enterprise Integration Patterns（エンタープライズ・インテグレーション・パターン）に記載されています。 https://nifi.apache.org/docs.html https://hub.docker.com/r/apache/nifi NiFiの基本的な設計コンセプトは、フローベースプログラミング[fbp]の主要な考えと密接に関連しています。ここでは、NiFiの主なコンセプトとFBPとの対応関係を紹介します。 https://nifi.apache.org/docs.html コンピュータプログラミングにおいて、フローベースプログラミング（FBP）とは、アプリケーションを「ブラックボックス」プロセスのネットワークとして定義するプログラミングパラダイムであり、メッセージパッシングによってあらかじめ定義された接続を介してデータを交換する。このブラックボックスプロセスは、内部を変更することなく、無限に再接続して異なるアプリケーションを形成することができる。このように、FBPはコンポーネント指向である。 FBPはデータフロープログラミングの特殊な形態で、境界付きバッファ、定義された寿命を持つ情報パケット、名前付きポート、接続の個別定義に基づいている。 https://en.wikipedia.org/wiki/Flow-based_progr...
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Algiz Rune in Modern Fiction

  Algiz Rune in Modern Fiction Berserk (Manga/Anime) – Brand of Sacrifice In Kentaro Miura’s dark fantasy Berserk , the infamous Brand of Sacrifice is a symbol seared into characters chosen as offerings to demons. Miura designed this mark by combining two runes: Algiz (ᛉ) and Othala (ᛟ) evil-genius.us . The Algiz rune (called Eolh in Anglo-Saxon) traditionally means elk and symbolizes protection or sanctuary evil-genius.us , while Othala signifies inheritance or property . In Berserk’s lore, the inverted Othala encircling Algiz implies the branded person has become “sanctified property” of malevolent gods evil-genius.us . This twist subverts Algiz’s usual protective power – the Brand offers no protection at all; instead it attracts evil. Visually, the mark resembles an elongated Y-shape (Algiz’s form) with additional prongs, appearing on Guts’s neck and bleeding in the presence of demons. Thus, Berserk repurposes Algiz’s protective imagery into a grim emblem of a curse , r...
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 - MADO OS, or perhaps QUIVIS

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  It was a fleeting moment when the name resurfaced - MADO OS, or perhaps QUIVIS. That sound, like the opening of an old drawer, unsealed a vault of memories, revealing a time when the streets of Akihabara thrived with innovation and ambition. The year was 1997, and the echoes of that era now glimmer faintly in my mind.
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Japan’s “Japanese Language Shrinkage” Warning, Why Domestic AI Options Now Matter

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 Daisuke Okanohara (Preferred Networks co-founder; appointed President/Representative Director on November 26, 2025) is an influential voice in Japan’s AI debate, including as a member of the “AI Business Operator Guidelines” expert committee. 株式会社Preferred Networks +1 He argues that global competition (Google, OpenAI, Chinese players, etc.) will keep improving models on a 6–12 month cadence, so we should focus on the underlying trend rather than each announcement. He frames three risks of not having a domestic (Japanese) AI option: (1) an economic risk—AI will become infrastructure and a platform for new industries, so lacking domestic choices matters over the long term; (2) a language-and-culture risk—overseas frontier models may speak Japanese well, but the share of Japanese in their training data can be tiny (e.g., 0.5% or even 0.1%), which can cause generated Japanese to converge on a narrow set of expressions and “thin out” the language; and (3) a sovereignty/security risk—w...
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