• Aegis Briefing — Feb 11, 2026
  • Priority Briefing
    • #1: Using AI to expand global access to reliable flood forecasts
    • #2: ScreenAI: A visual language model for UI and visually-situated language under…
    • #3: Claude Code のコンテキストウィンドウの内訳と効率的な使い方
    • #4: Generative AI to quantify uncertainty in weather forecasting
    • #5: OpenEvals × Langfuseで始めるAIエージェントのマルチターン評価 | 株式会社AI Shift
  • Serendipity Pick
    • AutoBNN: Probabilistic time series forecasting with compositional bayesian ne…

§Aegis Briefing — Feb 11, 2026

6 insights selected from 29 items. 23 burned as slop.

§Priority Briefing

§#1: Using AI to expand global access to reliable flood forecasts

Score: 7.4/10 | Verdict: quality

Well-researched technical content with credible sources and meaningful insights, though somewhat standard corporate research announcement format Using AI to expand global access to reliable flood forecasts

Posted by Yossi Matias, VP Engineering & Research, and Grey Nearing, Research Scientist, Google Research Floods are the most common natural disaster , and are responsible for roughly $50 billion in annual financ… Source

§#2: ScreenAI: A visual language model for UI and visually-situated language under…

Score: 7.2/10 | Verdict: quality

Novel architecture combining UI and infographic understanding with strong technical foundation and credible Google Research source, though presentation is somewhat dry ScreenAI: A visual language model for UI and visually-situated language understanding

Posted by Srinivas Sunkara and Gilles Baechler, Software Engineers, Google Research Screen user interfaces (UIs) and infographics, such as charts, diagrams and tables, play important roles i… Source

§#3: Claude Code のコンテキストウィンドウの内訳と効率的な使い方

Score: 7.0/10 | Verdict: quality

Technical deep-dive into Claude Code’s context window mechanics with practical optimization strategies, though credibility limited by lack of official source citations Claude Code のコンテキストウィンドウの内訳と効率的な使い方

Claude Codeを使っていて、「セッション後半で指示を忘れてる？」「なんかパフォーマンスが落ちた気がする」と感じたことはありませんか？ その原因の多くは コンテキストウィンドウの枯渇 にあります。 私自身、半年ほどClaude Codeで開発しているプロジェクトがあり、CLAUDE.mdにコーディング規約やMermaid作図時の指示などをつぎはぎで追記していました。 整理しようにも「何をどう書けば効率的なのか」が分からず、そもそもコンテキストがどのように使われているのか… Source

§#4: Generative AI to quantify uncertainty in weather forecasting

Score: 6.9/10 | Verdict: quality

Novel application of diffusion models to weather forecasting with solid technical foundation from credible source Generative AI to quantify uncertainty in weather forecasting

Posted by Lizao (Larry) Li, Software Engineer, and Rob Carver, Research Scientist, Google Research Accurate weather forecasts can have a direct impact on people’s lives, from helping make routine decisions, like wha… Source

§#5: OpenEvals × Langfuseで始めるAIエージェントのマルチターン評価 | 株式会社AI Shift

Score: 6.8/10 | Verdict: quality

Solid technical tutorial combining OpenEvals and Langfuse for multi-turn AI agent evaluation. Well-structured approach with concrete implementation details and evaluation metrics, though not ground… OpenEvals × Langfuseで始めるAIエージェントのマルチターン評価 | 株式会社AI Shift

こんにちは、AIチームの長澤 ( @sp_1999N ) です。 Claude Codeなどを代表として、さまざまなプロダクトやツールでAIエージェントが提供されています。 AIエージェントを構築する場合、評価が大切になりますが、その挙動はマルチホップ・マルチターンを前提としているため、一問一答的な評価では不十分なことがあります。 本記事では、 OpenEvals を使ったマルチターン対話のシミュレーションと、 Langfuse によ… Source

§Serendipity Pick

§AutoBNN: Probabilistic time series forecasting with compositional bayesian ne…

Score: 6.8/10 | Novelty bonus applied

Novel compositional approach combining interpretable Bayesian methods with neural network scalability, though builds incrementally on existing GP kernel research AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks

Posted by Urs Köster, Software Engineer, Google Research Time series problems are ubiquitous, from forecasting weather and traffic patterns to understanding economic trends. Bayesian ap… Source

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Curated by Aegis — AI Content Quality Filter