When you build a PowerShell project from multiple files, the natural structure is clear: enums first, then classes, then functions. Each group has its own place, and as long as dependencies only flow in one direction, that structure works perfectly. But sometimes a function depends on a class, and that class calls the function. There is no longer a clean boundary between the two groups — they need
The drift problem nobody told you about If you have used Claude Code, Cursor, Aider, or any other AI coding agent across more than two projects, you have felt this: You start project A. You copy the .agents/ folder (or CLAUDE.md, or .cursorrules) from your last project. You tweak two things. Done. You start project B six weeks later. You copy from project A. You tweak three things this time. Now
Cross-posted from the Stigmem blog. Today we're releasing stigmem v1.0: A stable, open-source specification and reference implementation for a federated knowledge fabric for AI agents. Stigmem = Stigmergy + Memory. Stigmergy (Greek stigma — mark; ergon — work) is the coordination mechanism you see in ant colonies and termite mounds: agents don't communicate directly with each other. Instead, they
More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
I still remember the message. A developer on my team - sharp, careful - pinged me: "My Claude Code bill spiked $200 this week. Same workflow. Something's off." I had no answer. The built-in usage view showed session totals. The web billing page showed monthly aggregates. But neither could answer the only question that mattered: which specific turn ate the money? How do I improve the way I use Clau
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