I’m going on a short vacation this week, so this post is coming out a bit earlier than usual. I actually had a different, more “useful” topic in mind — something educational, something responsible. But then I came across this fascinating article: I don’t like Tailwind. Sorry not sorry written by @freshcaffeine , and I couldn’t get it out of my head. So I decided to write a response instead. I actu
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
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
🤔 Why v0 Output Alone Isn't Production-Ready If you've used v0.dev to spin up a landing page, you've probably hit the same wall on the next step. The component looks clean inside v0, but the moment you drop it into your Next.js project the design tokens drift, dark mode breaks, metadata is empty, and Lighthouse scores land in the 60s. This isn't a v0 limitation — it's that v0's output is "desig
Diving into blockchain data (Solana specifically) changed how I think about “data” entirely 👇 At first, I expected something like a clean database—tables, rows, easy queries. ⚡ The “click” moment: 🧩 Biggest surprise: 🆚 Compared to traditional APIs: 🚧 Still learning: It’s less “database access” and more “state archaeology.” And that shift changes everything.