In Q3 2024, our 12-person platform team slashed log ingestion spend by 35% in 90 days, moving from a brittle Elasticsearch-based pipeline to a tuned Vector 0.30 and Loki 3.0 stack—without losing a single log or breaking our 99.95% SLA. GameStop makes $55.5B takeover offer for eBay (279 points) Talking to 35 Strangers at the Gym (144 points) Newton's law of gravity passes its biggest test (15
We Cut Compliance Costs by 40% Using Pulumi 3.140 and Chef 18 for Multi-Cloud AWS and GCP Modern multi-cloud environments offer unmatched flexibility, but they also introduce complex compliance challenges. For our team managing hybrid infrastructure across AWS and GCP, manual policy enforcement and fragmented tooling were driving up compliance costs by 22% year-over-year. By integrating Pulumi 3
In Q3 2024, our 12-person platform engineering team reduced confirmed security incidents by 41.7% (from 72 to 42 per quarter) after rolling out Trivy 0.50 for pre-deployment scanning and Falco 0.40 for runtime detection across 142 production microservices. We didn’t rewrite our CI/CD pipeline, we didn’t hire a dedicated security team, and we didn’t spend a dime on enterprise security tools. Here’s
選定理由 Paper: https://arxiv.org/abs/2512.01020 【社会課題】 【データの設計と従来技術の限界】 Issue Tree（法的論点ツリー）に変換し、葉ノードに対しルーブリック基準を適用可能にした。原告・被告・裁判所の主張をツリー構造で整理した約24,000インスタンスのデータセットを構築。評価軸は「論点カバレッジ」と「正確さ」の2次元。以下がサンプルである： 【原告の主張】被告は540万円を支払え └─【原告】保険金の支払い義務がある ├─【原告】死亡は突発的・偶発的な事故だった │ └─【原告】餅を食べて窒息死＝外因による傷害 │ └─【被告】死因は既往症の可能性が高い └─【裁判所の結論】突発的事故と認定 ただし窒息死は証明不十分 この
Introduction To understand knowledge graphs, you first need to grasp three core concepts: entities, relations, and triples. Imagine a knowledge graph as a network that models the real world using nodes and connections. In this network, an entity is any distinct thing or object such as a person, city, or company. For example, “Sreeni”, “Plano”, and “Caterpillar” are all entities. A relation descr
This is my Day 2 of learning AI fundamentals where I will be covering the following concepts: Vector Embeddings How Tokenisation and Vector Embeddings relate to each other Vector embeddings is the process of turning each token id(generated during tokenisation) into high dimensional vector where semantic similarity results into geometric closeness. Think of it like this: dog is closer to puppy, al