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GitHub热门项目 | Univer is a full-stack framework for creating and editing spreadsheets / word processor / presentation on both web and server. | Stars: 13,695 | 160 stars today | 语言: TypeScript

GitHub热门项目 | This is MCP server for Claude that gives it terminal control, file system search and diff file editing capabilities | Stars: 6,289 | 20 stars today | 语言: TypeScript

GitHub热门项目 | A high-performance, 100% client-side tool for removing Gemini AI image & video watermarks. Built with pure JavaScript using mathematically precise Reverse Alpha Blending. / 基于 JavaScript 的纯浏览器端 Gemini AI 图像和视频无损去水印工具，使用数学精确的反向 Alpha 混合算法 | Stars: 4,749 | 27 stars today | 语言: JavaScript

GitHub热门项目 | Sample Whatsapp App - Jasper's Market | Stars: 508 | 10 stars today | 语言: JavaScript

GitHub热门项目 | AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, and more). Turn any folder of code, SQL schemas, R scripts, shell scripts, docs, papers, images, or videos into a queryable knowledge graph. App code + database schema + infrastructure in one graph. | Stars: 80,027 | 885 stars today | 语言: Python

GitHub热门项目 | | Stars: 2,604 | 14 stars today | 语言: Python

GitHub热门项目 | SkillOpt is a text-space optimizer that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits, validation-gated updates, and deployable best_skill.md artifacts. | Stars: 11,571 | 261 stars today | 语言: Python

GitHub热门项目 | 🧠「大模型」2小时完全从0训练64M的小参数LLM！Train a 64M-parameter LLM from scratch in just 2h! | Stars: 52,981 | 176 stars today | 语言: Python

GitHub热门项目 | Declarative Continuous Deployment for Kubernetes | Stars: 23,361 | 20 stars today | 语言: Go

GitHub热门项目 | Next-generation ORM for Node.js & TypeScript | PostgreSQL, MySQL, MariaDB, SQL Server, SQLite, MongoDB and CockroachDB | Stars: 46,390 | 30 stars today | 语言: TypeScript

With funding from tennis star and angel investor Venus Williams, the French app WeWard says that it increases walking time by almost 25%.

The therapy unit at the hospital I work for had six treatment rooms. Room 1, Room 2, Room 3, and so on, each split by the kind of therapy it handled. And each room kept its own document to record patients. The problem wasn't that the documents existed. The problem was that no two of them looked alike. Same patient. Same information. But every room ordered the columns differently and named things differently. One put the date in the first column. Another put it last. One wrote "treatment time." The room next door wrote "minutes used." On their own, each form worked fine. Looked at one at a time, there was nothing wrong. The trouble showed up the moment anyone tried to combine them. The work that never ended Every so often, a request would come down from above: Can we see the overall numbers? That was when the real work began. I would open all six documents side by side. I would line up columns that didn't match, by eye, and move each value into one master table by hand. Days of this would get me a single sheet of statistics. Then the next quarter, the same request came down again. And I started over. The table I'd built last time was useless if the format had shifted even slightly. So I rebuilt it from scratch. Every time. I couldn't stand it. This was obviously a job you do right once and never touch again. We just weren't doing it right. So instead, we kept feeding people's evenings into it. The obvious answer The fix was simple. Make all six rooms use one form. Same columns. Same names. Same order, everywhere. Then there's nothing to move when you combine them. The statistics become a matter of stacking, not translating. The answer was so obvious I wondered why nobody had done it years ago. So I built a unified form in Excel and sent it around. And that's where I learned Excel has walls of its own. Where Excel broke down Once a file gets passed around, you lose track of which copy is the real one. The versions pile up. "Final." "Actually final." "Final, revised."

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Day 9 of the 30 Days of AWS Terraform series focuses on Terraform Lifecycle Rules — powerful controls that decide how Terraform creates, updates, replaces, and destroys resources. What Terraform LifeCycle meta arguments are Lifecycle meta arguments allow us to control how Terraform behaves when it creates, updates, or destroys resources. They help us: Avoid downtime Protect important resources Handle changes made outside Terraform Validate configurations before and after deployment Enforcing compliance Controlling replacement behavior Lifecycle rules allow us to override default behavior safely. Lifecycle rules are Terraform-native controls applied inside a resource block: lifecycle { ... } Lifecycle Rules Covered 1️⃣ create_before_destroy — Zero Downtime Updates Problem: Terraform destroys the old resource before creating the new one → downtime. Solution: lifecycle { create_before_destroy = true } Behavior: New resource is created first Old resource is destroyed only after Ensures zero downtime 2️⃣ prevent_destroy — Protect Critical Resources This setting prevents Terraform from deleting a resource. Example If Terraform tries to destroy this resource, it will fail with an error. This is useful for: Production databases State storage buckets Important data resources 3️⃣ ignore_changes — Allow External Modifications Problem: Terraform overwrites manual or automated external changes. Solution: lifecycle { ignore_changes = [desired_capacity] } Demo: Auto Scaling Group desired capacity modified manually in AWS Console terraform apply did not revert the change Behavior: Terraform ignores changes for specified attributes. ✅ Use for: Auto Scaling Groups Resources modified by external systems Ops-driven configurations 4️⃣ replace_triggered_by — Replace When Dependency Changes Problem: Changing a dependency doesn’t always recreate dependent resources. Solution: lifecycle { replace_triggered_by = [aws_security_group.main] } Behavior: When security group changes EC2 instance i

Catch API drift before your customers do Discussion | Link

I initially wanted to **make a side-scrolling game like MapleStory**. YouTube was flooded with "I made a game with AI (Claude)," so I thought it would be easy. But when I tried it myself – it turned out that **people with existing game development knowledge were just using AI to improve quality and speed**, not that you could just "make it for me" without any knowledge. In the end, what I completed wasn't a playable game, but an **"auto-battle" spectator game** (like raising a mushroom) that you just watch. This post is about that **honest journey** – where I got stuck, why I pivoted, and what I learned. (And you can try out the completed version via the **🎮 Play Now** link below.) I'm a developer in Korea building an AI chatbot alone. I only write about things I've **actually tried and experienced**.## 1. The First Wall — AI-Generated Characters Can't 'Walk' **Moving characters** are essential for games like MapleStory. So, I first tried **AI image generation (gpt-image) to create chibi characters** and then generated walk cycles (4 frames of walking animation) for them. This is where I got stuck. **With each frame, the character subtly became a different character** – the color of the clothes, the proportions, the face all changed slightly between frames 1, 2, 3, and 4. When stitched together in a game, the character wouldn't walk; it would just **tremble erratically.** The Ceiling of Character Animation — AI Generation vs. Pre-made Sprites ❌ AI-Generated Characters (Re-imagined each frame) 🧍1 🧎2 🕴️3 🧍4 → Clothing/proportions wobble each frame = 'Trembling' instead of walking ✅ Pre-made CC0 Sprites (Hand-drawn sheet) 🏃1 🏃2 🏃3 🏃4 → Consistent frames = Smooth walk cycle This is **exactly the same ceiling** I hit in Making AI Videos (Dev Log #3) – AI image generation **cannot create consistent character animation (multi-frame movement).** The same wall in videos, the same wall in games. 2. Pivot ① — Abandoning AI Characters for Pre-made Sprites So, my first surrender

The company is raising $10 billion, leading to a valuation of $130 billion.

Quantum mechanics gave us the transistor before we understood it. The same pattern is happening with AI right now — and the builders who recognize this will define what comes next. The argument that never ended — and the lab that didn't care In 1927, the greatest minds in physics gathered in Brussels for the Solvay Conference. Albert Einstein, Niels Bohr, Werner Heisenberg, Erwin Schrödinger, Max Planck, Marie Curie — twenty-nine of the most brilliant humans who ever lived, in one room. They were arguing about quantum mechanics. Specifically: what does it mean for a particle to exist in multiple states simultaneously until observed? Does reality require an observer? Is the universe fundamentally probabilistic? Is God playing dice? Einstein said no. Bohr said yes. Neither convinced the other. That argument never fully resolved. Nearly a century later, physicists still debate the interpretation of quantum mechanics — the Copenhagen Interpretation, Many Worlds, Pilot Wave theory. We have not settled it. Meanwhile, in 1947 — twenty years after the Solvay Conference — three engineers at Bell Labs in New Jersey quietly invented the transistor. William Shockley, John Bardeen, and Walter Brattain did not wait for the philosophical debate to conclude. They did not need to understand why quantum tunneling worked at a fundamental level. They understood it well enough to build something with it. That transistor became the foundation of every computer, every smartphone, every server, every piece of digital infrastructure that exists today. We built the entire digital civilization on something we still don't fully understand. Not despite the uncertainty. With it. The pattern repeating right now Across the internet in 2025 and 2026, a remarkably similar argument is happening. Will AI take all the jobs? Is it conscious? Does it hallucinate too much to be trusted? Are we building something we cannot control? Should we slow down? Should we stop? These are not trivial questions. The r

If you've been monitoring Linux servers for any length of time, there's a good chance node_exporter was the first thing you installed. It's lightweight, reliable, and exposes a huge amount of machine metrics for Prometheus to scrape. For years, it has been the default answer. As your infrastructure grows, though, your monitoring stack usually grows with it. First comes log collection. Then traces. Before long you're running node_exporter , a log shipper, and maybe another telemetry agent. Each component has its own configuration, service unit, upgrade cycle, and failure modes. Grafana Alloy changes that by consolidating those responsibilities into a single telemetry agent. This post walks through migrating from node_exporter to Alloy on a real fleet, one server at a time, while maintaining continuous visibility throughout the process. These are the exact steps that survived contact with production on the Irin monitoring stack, not the idealized version that looks clean in a diagram. TL;DR If you're already running node_exporter , don't replace it overnight. Install Grafana Alloy alongside it, configure Alloy's built-in prometheus.exporter.unix component, verify that metrics are reaching your remote Prometheus instance, and only then retire node_exporter. Migrating one server at a time minimizes risk, preserves visibility, and positions your infrastructure for logs, traces, and future telemetry without deploying additional agents. The real difference is the direction of travel Before getting started, it's worth understanding what actually changes. This isn't simply replacing one monitoring agent with another. node_exporter is a server. It listens on a port, typically 9100,and waits for Prometheus to connect and scrape metrics. That means every monitored machine needs an open endpoint, network connectivity from Prometheus, firewall rules, and scrape configurations. Alloy flips that model around. Instead of waiting for Prometheus to connect, Alloy collects metrics loca

🎯 Learning Objectives Understand the interface boundary between Terminal Emulators and Shell Interpreters (including Windows Terminal vs. PowerShell vs. CMD). Master File System path tracking, hidden dotfiles, and essential CLI utilities. Map system execution paths via global and local environment configurations. 1. Terminal vs. Shell (The Windows Architecture) Terminal: The visual GUI wrapper. A window application that captures keyboard strokes, handles GPU text rendering, and manages tabs/panes. Examples: Windows Terminal, iTerm2, Alacritty. Shell: The command interpreter engine running inside the terminal. It evaluates text strings, processes scripts, issues system calls ( syscalls ), and interacts with the OS Kernel. Examples: PowerShell, Bash, Zsh, Command Prompt (CMD). ┌────────────────────────────────────────────────────────┐ │ WINDOWS TERMINAL GUI (The Visual Interface Window) │ │ │ │ │ ├───► Tab 1: [ PowerShell Core Engine (Modern) ] │ │ ├───► Tab 2: [ Command Prompt Engine (Legacy) ] │ │ └───► Tab 3: [ WSL Ubuntu Linux Bash (Core) ] │ └───────────────────────────┬────────────────────────────┘ │ Raw Text & Input Streams ▼ ┌────────────────────────────────────────────────────────┐ │ SHELL INTERPRETER (e.g., PowerShell / CMD) │ │ └───► Parses input string commands into system tasks │ └───────────────────────────┬────────────────────────────┘ │ System Call (Syscall) ▼ ┌────────────────────────────────────────────────────────┐ │ OPERATING SYSTEM KERNEL │ │ └───► Interacts directly with underlying hardware │ └────────────────────────────────────────────────────────┘ 2. Deep Dive: PowerShell vs. Command Prompt (CMD) While both are Windows shells hosted inside Windows Terminal, they belong to entirely different computing eras: Command Prompt ( cmd.exe ): A legacy text shell maintained purely for backwards compatibility with 1980s MS-DOS. It pipelines data as Plain Text Only , meaning outputs must be manually string-filtered. PowerShell ( pwsh.exe ): A modern, cros