Last Tuesday I lost about three hours to a regression in our checkout service. The cart total was off by a cent on certain promo combinations, and the only signal was a Slack ping from finance with a screenshot. No stack trace. No exception. Just wrong numbers. I did what I always do first. I opened the diff for the last deploy, scrolled, squinted, and tried to feel my way to the bug. Forty minute
I Built a VS Code Extension to Bring IntelliJ’s “Show History for Selection” Experience If you come from IntelliJ, you probably miss one super useful feature in VS Code: Show history for selected lines. I built a new extension to solve exactly that. Show History for Selected Code This extension helps you inspect Git history for a specific code selection, not just the whole file. Shows commit h
Microsoft's 'Co-Authored-by Copilot' Tag: Unpacking the Strategic Play for AI Dominance in VS Code The persistent insertion of 'Co-Authored-by: Copilot' into commit messages within VS Code—often irrespective of GitHub Copilot's active contribution to specific changes—is far from a benign engineering detail. It represents a calculated, multi-faceted strategic maneuver by Microsoft, signaling a pr
I have a bad habit of jumping between projects. It's not a big deal. But it happens every single day. So I built rewind. rewind That's it. No setup, no IDE, no agent loop burning through tokens. Just one binary, one command, one LLM call. cargo install git-rewind GitHub: https://github.com/Chronos778/git-rewind Would love feedback — on the idea, the UX, anything. Still early days.
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
If you use GitHub's merge queue and had a rough week around April 23rd, 2026, you were not imagining things. Your code actually disappeared. Not because of a bad commit, not because of a rogue team member, but because GitHub itself quietly deleted it. This is the story of what happened, why it was way worse than the official numbers suggest, and what it means for the way we all trust the tools we