Data is no longer treated as a byproduct of business operations and has become one of the most valuable organizational assets. Every interaction on a banking application, e-commerce platform, hospital system, logistics network or social media service generates data continuously. As organizations increasingly adopt digital workflows, cloud platforms, machine learning systems and real-time applicati
In modern data-driven organizations, managing and analyzing data efficiently is critical. OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are both integral parts of data management, but they have different functionalities. Understanding how they differ, and how they complement each other is essential for anyone working with data systems. Online Transaction Processing (
SQL is widely known for data querying and manipulation but systems do grow; data becomes larger; processes become repetitive and operations become sensitive. SQL has some features which enables it to be considered a fully fledged programming language. Some of the features which I discuss in this article are procedures, functions and transactions. Each of these concepts serve distinct purposes. Sto
Claude + Mobile via MCP: Giving the Model Hands on a Real Phone I plugged in a Pixel two months ago, ran one command in Claude Desktop, and watched it open Maps and start navigation to my home address from a single sentence prompt. It was the first time I'd ever seen a language model physically operate a phone. Latency was about two seconds per action; the part that surprised me was the third st
AI-Native Mobile Testing: What It Actually Means in 2026 The phrase "AI-native" has been thrown around in the testing space since 2019. Almost every tool calling itself that just bolts a language model on top of Appium and ships the same brittle XPath selectors with a new label. That's not AI-native testing. That's Appium with a chatbot. This post is about what AI-native actually has to mean to
The Missing Control Plane for Local AI Agents I sat with my Pixel for 20 minutes trying to get Claude Desktop to dictate a Slack message via accessibility. It was miserable. The model was capable. The transport wasn't. That gap — between an AI that can reason and an AI that can actually do — is what I've been working on with Drengr. This post is the version of the argument I'd give to anyone bui
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
If you are stepping into the world of data engineering or analytics, you have likely been hit with a wave of storage buzzwords like data lake and data warehouse. In this article, we will demystify these terms so you can understand exactly where your data belongs. Imagine you just launched a business. You need a system to record daily operations every time a customer buys a product, updates their