凛として黄昏

研究開発職: 画像処理、機械学習、映像生成など。大学ではvideo prediction 関連を研究していました。小説の好きなジャンル: 幻想文学とSF。好きな言語: Rust, C++, Python

[あんはる@AI受託・モバイルアプリ開発会社社長Flutter Golang Rust @_anharu]

Googleにインデックスされるのか、、
気をつけないと。。。。
site:https://t.co/kkUBklNz6m 〇〇で検索できちゃう😱

[ゲームメーカーズ @GameMakersJP]

ニンテンドーシステムズ、社内CI/CD基盤の開発・運用事例を解説。「SRE NEXT 2026」講演資料が公開
https://t.co/98zeCgt3Kc

GitHub ActionsのSelf-hosted Runnerを活用し、AWS上にCI/CD環境を構築。専属チームは3名で、他プロダクトと兼務のうえ運用を効率化した事例も紹介している

[tatsuki | Claude Code活用支援 @nobel_824]

claude codeを社内導入したい人は読んだ方がいい

[渡邉正裕 @masa_mynews]

日本人の給料が上がらない構造がキオクシアにわかりやすく出てる。ボーナス計算式が決まっておらず、労使交渉でぬるっと曖昧に決めちゃう。三菱商事とキーエンスは計算式が決まってて営業利益連動で自動的にボーナスが決まるから年間ボーナスが1千万円超える年が普通にある。

[How To Prompt @HowToPrompt__]

MIT open-sourced an AI model that converts photos into fully editable CAD programs and it quietly kills the $150/hour CAD modeling industry.

Just upload a sketch or photo and it generates the full parametric 3D model. exportable as STL. ready for manufacturing.

→ no SolidWorks license
→ no weeks of modeling
→ no CAD engineer needed

100% Open Source

[凛として黄昏 @rintositelol]

SANA-WMとは？単一GPUで720p・60秒動画を生成するNVIDIAの効率的世界モデル https://t.co/4CT5tZFQSy

[ぷらぎあ @plastic_gear]

書きました
反応次第で続けます

【ぷらぎあのロボットハンドノート】①ロボットハンドの現在地：なぜ「手」を作るのはこれほど難しいのか｜plastic_gear @plastic_gear https://t.co/xqL17f5QaM

[ごまどう @gomadouFFF]

ゴールデンウィーク暇なら簿記2級or基本情報を勉強しよう！

4月30日：試験の申し込みをする（受験日は5月6日）
5月1日：参考書を買い、机の上に置いて就寝
5月2日：起きた瞬間に勉強を開始。ポモドーロタイマーに従い、寝る瞬間まで勉強する
5月3,4,5日：起きてから寝るまで勉強
5月6日：受験する

[田中悠斗＠Fore- @tanaka_yuto]

この発想、めちゃくちゃ盲点だったわ。

Claudeに期限切れ特許を食わせて、誰も作らなくなった商品を掘り起こし、それを1つすでに生産開始したらしい。

420万件以上のパブリックドメイン特許から、シンプルに作れて売れそうなやつをAIがピックアップ。

こういう新しいハックポイントを力技じゃなく出来るってのも、また新しいターニングポイントではある。最近毎日が怖いくらい面白い。

[Massimo @Rainmaker1973]

Gilbert Strang, an MIT professor, taught the same linear algebra course for 62 years. When he delivered his final lecture in May 2023, students from around the world tuned in online to watch.

The course is MIT 18.06 Linear Algebra. Millions of machine learning engineers, data scientists, quants, and self-taught programmers learned the essential math behind AI from his clear, free video lectures, even though most never stepped foot on the MIT campus.

Strang joined the MIT faculty in 1962 and retired in 2023. When MIT launched OpenCourseWare in 2001–2002, he was one of the first to embrace it fully. While many professors hesitated, Strang saw it as an opportunity to share mathematics with everyone. He filmed his lectures and made them freely available.

He completely changed how linear algebra is taught. Instead of starting with abstract vector spaces and proofs, Strang began with something simple and visual: matrix multiplication. He built intuition first using concrete examples, then introduced more advanced ideas like eigenvectors and singular value decomposition. He insisted that students should be able to explain every concept with a small, tangible matrix before moving to theory.

Beyond the content, his teaching style stood out. He spoke to students with genuine respect, patience, and kindness, never using words like “obviously” or “trivially.” He regularly paused to check if anyone was lost and treated beginners as thoughtfully as he would his colleagues.

As a result, Strang became the default linear algebra teacher for much of the planet. Universities in many countries began recommending his lectures to their own students. Some even replaced their in-person courses with his videos because they could not match their clarity.

His final lecture ended with a long standing ovation. Strang seemed surprised by the applause, smiled humbly, and simply thanked everyone.

In his short comment under the YouTube video, he expressed gratitude for a wonderful life of teaching and hoped others would continue teaching the subject well. No self-promotion, no grand farewell—just quiet sincerity.

When you add up every version, every upload, help sessions, and all the different recordings MIT has shared over the years, the total has surpassed 20 million views.

Today, the full course, including all lectures, problem sets, and solutions, remains freely available on MIT OpenCourseWare. One of the most important mathematical foundations of modern AI is still just one click away.

[まるた @marurur]

流行の Physical AI というのをやってみたくて，Claude CodeにUSB接続の Furuta Pendulum と基礎的な入出力を行うコード例，その実行方法を与えて「10秒以上倒立できるまで頑張れ！」で放置
システム同定→エネルギー法→状態フィードバックと進んで１時間ほどで10秒維持できるところまで来ました．

[Claude @claudeai]

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale.

It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days.

Now in public beta on the Claude Platform.

[Rihard Jarc @RihardJarc]

People are bearish on memory, but the leaked Claude Code source code is showing us some additional memory demand that the market hasn't priced in IMO.

1. The market thinks about AI memory demand as a server-side story: HBM on H100s/B200s for inference. What the bug reports reveal in this code is that the client-side of AI coding agents is also extraordinarily memory-hungry. Idle Claude Code processes growing to 15GB each, active sessions hitting 93-129GB. This matters because the feature flag pipeline (DAEMON, PROACTIVE, CRON) points toward future always-on background agents. If a developer has a persistent daemon agent running alongside their active sessions, you're looking at baseline memory consumption of 15-30GB+ just for Claude Code on a developer workstation - before they even open their IDE, browser, or anything else. This means either enterprise IT needs a big uplift to higher-RAM workstations or we move even more memory-hungry workloads towards the cloud.

2. The Auto Dream consolidation feature runs background Claude sessions to clean up memory files. One observed consolidation took 8-9 minutes processing 913 sessions. In other words, a meaningful fraction of Anthropic's token consumption is the system managing its own memory, not the user doing productive work. As memory systems get more sophisticated (team sync, cross-session event buses, memory consolidation), this overhead grows. It's a recursive cost - more memory features require more inference to manage memory. I don't think anyone is modeling this as a distinct line item in token consumption estimates.

3. 1M token context windows for Claude Code. Moving from 200K to 1M context is a 5x increase in KV cache memory per session on the server side. Combined with multi-agent (5-15x per user) and the proactive/daemon features (sessions that persist for hours/days instead of minutes), you get a compounding memory demand curve that's steeper than linear adoption growth that many analysts model.

Memory demand per active user is increasing faster than user count, because each user's sessions are getting longer, wider (more agents), and deeper (larger context windows).

[AI at Meta @AIatMeta]

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound.

Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks.

Try the demo and learn more here: https://t.co/VkMd1YpQWI

[Elon Musk @elonmusk]

@SonohennoKuma After Fukushima, to support Japan, I personally visited the area and ate locally grown food to prove that there was no nuclear danger.

I also donated a small solar system.

https://t.co/KxUgJIsvo4

[Elon Musk @elonmusk]

The mind of humanity

[yousukezan @yousukezan]

Pythonの人気ライブラリLiteLLMが改ざんされ、認証情報窃取とKubernetes侵害を伴う大規模サプライチェーン攻撃が発覚した。数十万台規模の感染が主張され、別案件から連鎖する深刻な被害拡大が懸念されている。

攻撃はハッカー集団TeamPCPによるもので、PyPIに公開されたLiteLLM 1.82.7および1.82.8に悪性コードが仕込まれていた。Endor Labsによれば、proxy_server.pyに埋め込まれたBase64ペイロードが読み込み時に実行され、さらに1.82.8ではlitellm_init.pthによりPython起動のたびに自動実行される仕組みが追加された。これにより利用有無に関係なく感染が拡大する構造となっていた。ペイロードはTeamPCP Cloud Stealerの亜種で、SSH鍵、クラウド認証情報、Kubernetesシークレット、.envやDB資格情報、暗号資産ウォレットなど広範な機密を収集し、暗号化してhttps://t.co/Rk3OxoWYDTへ送信する。加えてkube-systemへ特権Podを展開して横展開を図り、systemdサービスとしてバックドアを常駐させる。さらに特定条件では破壊的挙動も確認されている。被害規模は約50万件とされるが独立検証はされていない。現在不正バージョンは削除済みで、1.82.6が安全版とされる。
https://t.co/SjzQa3fLb0

[結城浩 / Hiroshi Yuki @hyuki]

Claude Codeが途中をはしょるのは、「早く達成する」ことが狙いのようですよ。なので「時間はたっぷりあります。品質を優先して」というと私の場合はうまくいきました。

[凛として黄昏 @rintositelol]

C++のGUI関連調べたけど、imguiすごいな。

[Yuki Kamitani @ykamit]

出てた

神谷之康, 脳とAI は似ているか — NeuroAI の挑戦—, 人工知能, 2026　
 https://t.co/mv9BljQd4q, https://t.co/7fzBCpmI1w