标签：#open

Here we're going to cover the installation steps of Browsers in Linux - specifically, Debian GNU/Linux . We're covering the procedural steps for this in Debian Testing (Forkey), which is already beginning the gleanings of what will become Debian 14. At the time of writing,the current kernel version is 7.0.13+deb14-amd64. Daily drivers will likely be along the lines of Vivaldi, Firefox, Lagrange, and Brave Browser, and in no particular order. Any others will be mostly for particular reasons like, testing, curiosity, Etc., but some specialized products like the Tor browser, which focus on privacy are also covered. This list is by no means complete, but there's a bunch of them we cover, so let's jump right in! I can haz #Cheezburgerz? 🍔 🍟 Well, let's see... First up is the first rate and fully featured Vivaldi, built on top of the open source Chromium, as are so many others in this list. Vivaldi Manual setup of the Vivaldi Linux repos - According to their website, "you no longer need to do this. After downloading a Linux package and installing it our Linux update repositories will be configured automatically for you to receive updates." That's awfully nice, so visit the download page above and get the .deb package: ~# mkdir -pv /usr/local/packages/vivaldi ; cd /usr/local/packages/vivaldi ~# wget https://downloads.vivaldi.com/stable/vivaldi-stable_8.1.4087.48-1_amd64.deb ~# apt install ./vivaldi * .deb That's it! You're ready to go now. Next up... Firefox ~# apt update && apt install firefox-esr There's none of this messing about anymore with softforks and trying to remember their names...'IceCat?', 'IceWeasel?'. The Trademark issues over branding have been resolved (and that's a good thing). The one thing you might want to look into is SeaMonkey , which combines the Browser with an email client (like Thunderbird), an RSS Reader, IRC client, and a few other goodies. Brave Browser ~# apt install curl ~# curl -fsSLo /usr/share/keyrings/brave-browser-archive-keyring.gpg ht

GitHub热门项目 | Delightful JavaScript Testing. | Stars: 45,535 | 81 stars today | 语言: TypeScript

GitHub热门项目 | The official Python library for the OpenAI API | Stars: 31,211 | 92 stars today | 语言: Python

GitHub热门项目 | CLI tool for configuring and monitoring Claude Code | Stars: 28,654 | 104 stars today | 语言: Python

GitHub热门项目 | Asio C++ Library | Stars: 6,035 | 87 stars today | 语言: C++

GitHub热门项目 | A modern, C++-native, test framework for unit-tests, TDD and BDD - using C++14, C++17 and later (C++11 support is in v2.x branch, and C++03 on the Catch1.x branch) | Stars: 20,570 | 69 stars today | 语言: C++

GitHub热门项目 | Skills for Real Engineers. Straight from my .claude directory. | Stars: 164,017 | 1,728 stars today | 语言: Shell

GitHub热门项目 | A YAML parser and emitter in C++ | Stars: 6,050 | 65 stars today | 语言: C++

GitHub热门项目 | Abseil Common Libraries (C++) | Stars: 17,472 | 3 stars today | 语言: C++

The problem every self-hoster hits You built something. A local API. A Minecraft world for your friends. A self-hosted dashboard. An ERP running on the office machine. It works great — on your LAN. The moment you want someone outside to reach it, the fun begins: Port forwarding? Good luck if you're behind CGNAT, a corporate firewall, or an ISP that doesn't give you a public IP. VPN? Now every person who wants access has to install a client, join a network, and stay connected. Overkill for "let me show you this one page." Cloud deploy? Now you're maintaining two environments, paying for a VPS you didn't need, and shipping data somewhere it doesn't have to live. What most people actually want is simpler: take this one local port, give it a public address, done. That's exactly what FRP does. What FRP is FRP (Fast Reverse Proxy) is an open-source tool by fatedier that exposes a local service behind a NAT or firewall to the public internet. It's battle-tested, written in Go, and has been the go-to answer in self-hosting communities for years. The model is clean — two pieces: frps (the server) — runs on a machine with a public IP (a $5 VPS is plenty). frpc (the client) — runs on your local machine, the one with the service you want to expose. The client opens an outbound tunnel to the server. The server listens on a public port and forwards traffic back through the tunnel. NAT and firewalls don't matter because the connection is initiated from inside . [Visitor] → [frps on public VPS:7000] ⇄ tunnel ⇄ [frpc on your laptop] → [localhost:8080] That's the whole idea. It works for TCP, UDP, HTTP, HTTPS. People run Minecraft servers, remote desktops, internal dashboards, and dev previews through it every day. The catch: config files FRP works great. The friction isn't the protocol — it's the workflow . To run frpc , you write a TOML/INI config file: serverAddr = "203.0.113.10" serverPort = 7000 auth.token = "your-secret-key" [[proxies]] name = "my-web" type = "tcp" localIP = "1

BDE Score™ — Open-Source Multi-Factor Stock Analysis One number. 0-100. Every stock. A composite score combining 5 dimensions: Momentum (30%), Volatility (25%), Volume (20%), Trend (15%), Risk (10%). Coverage : 74 stocks across US (25), Hong Kong (26), and A-Share China (23) markets — all in real-time. Why It's Different Zero signup — REST API works without authentication Multi-market — US, HK and A-Share coverage Transparent scoring — Every factor weight is documented Open source — Full methodology on GitHub Real-time badges — Embed live scores in any README Quick Start curl "https://atlantic-remains-atomic-floor.trycloudflare.com/api/analyze?market=ALL" Links GitHub: https://github.com/hbhqq9/bde-score Live Demo: https://atlantic-remains-atomic-floor.trycloudflare.com/api/snapshot?market=ALL Not financial advice. Technical service for educational purposes. ⭐ Star us on GitHub!

GitHub热门项目 | Visual testing tool for MCP servers | Stars: 10,331 | 13 stars today | 语言: TypeScript

GitHub热门项目 | Automatic quad remeshing tool | Stars: 2,430 | 403 stars today | 语言: C++

Artificial intelligence has transformed software development. Instead of simply generating code snippets, modern coding assistants can understand entire codebases, refactor applications, write tests, debug issues, and even execute development workflows. Among the most capable tools available today are Claude Code and Codex. While both are designed to accelerate software development, they take different approaches to coding assistance. This article compares their strengths, weaknesses, and ideal use cases. What Is Claude Code? Claude Code is Anthropic's command-line coding assistant built around the Claude family of language models. Rather than functioning as a traditional autocomplete tool, Claude Code works as an AI development agent that can inspect projects, edit files, explain code, write tests, fix bugs, and help developers navigate large repositories. Its workflow is centered around natural language. Developers describe what they want, and Claude Code performs the necessary steps while keeping the developer involved throughout the process. Key features Deep understanding of large codebases Multi-file editing Test generation Refactoring assistance Terminal-based workflow Strong reasoning for complex programming tasks Excellent documentation generation What Is Codex? Codex is OpenAI's AI coding agent designed to help developers write, understand, and modify software. Unlike the original Codex model introduced in 2021, today's Codex operates as a software engineering agent capable of working across repositories, generating code, fixing bugs, creating pull requests, running tests, and assisting with development workflows. Codex integrates closely with OpenAI's ecosystem and focuses on turning natural language instructions into production-ready code while maintaining awareness of project context. Key features Repository-aware coding Autonomous task execution Code generation Bug fixing Test writing Pull request assistance Integration with modern development workflow

GPT-5.6 is finally live, and three takes immediately showed up in my feed: "Sol replaces GPT-5.5 everywhere." "The API still isn't broadly available." "The 1.05M context window means you can stop thinking about prompt size." Two are wrong. The third is exactly how you end up with a bill that is almost twice your estimate. I spent the morning reading the new model pages, rollout docs, pricing table, migration guide, and system card. My conclusion is less exciting than "route everything to Sol," but much more useful: Terra is the GPT-5.6 tier I'd test first for most production workloads. TL;DR No, GPT-5.6 Sol should not replace every GPT-5.5 request. It has the same $5/$30 standard token price and different agent behavior. Yes, the API is live. Sol, Terra, and Luna are in OpenAI's public model catalog; ChatGPT access is still rolling out gradually. Terra is the practical default: $2.50 input and $15 output per million tokens, exactly half Sol's price. Luna is the volume tier: $1 input and $6 output, with the same 1.05M context window. The 272K boundary matters: go above it and the entire request moves to 2x input and 1.5x output pricing. The uncomfortable part: OpenAI says GPT-5.6 is more likely than GPT-5.5 to take actions beyond user intent in agentic coding. What actually shipped This isn't one model with three marketing labels. It is a three-tier family with explicit model IDs. Tier Model ID Input / 1M Output / 1M My default use Sol gpt-5.6-sol $5.00 $30.00 Hard coding and deep analysis Terra gpt-5.6-terra $2.50 $15.00 General production Luna gpt-5.6-luna $1.00 $6.00 Extraction, routing, batch work All three have: 1,050,000 tokens of context 128,000 maximum output tokens February 16, 2026 knowledge cutoff Text and image input Reasoning levels from none through max Responses API and Chat Completions support The unsuffixed gpt-5.6 alias points to Sol. I wouldn't use that alias in a cost-sensitive production service. An explicit model tier makes billing behavior easi

You've seen the alert. You've opened the PR. You've read the changelog. Then you realize: your code doesn't even call the vulnerable function. Every week. Hundreds of teams drowning in CVE notifications for packages sitting dormant in their node_modules — dependencies they pulled in years ago, bundled by a transitive library, and never actually executed. Meanwhile, the real vulnerabilities get buried. VigilOps is a free Node.js CLI that fixes this. How VigilOps Works VigilOps does three things: Scans dependencies against OSV.dev — the open vulnerability database used by GitHub, PyPI, and npm Runs static reachability analysis to filter out unreachable vulnerabilities (packages in your tree but never called by your code) Auto-opens a GitHub PR with the fix The result: you get one PR with one real vulnerability. Not a spreadsheet. Not a wall of Slack messages. A fix. Demo Here's a quick scan: npx vigilops scan examples/vigilops-demo-lodash And to see everything including suppressed (unreachable) deps: npx vigilops scan examples/vigilops-demo-express --all The --all flag shows what's in your dependency tree but not actually reachable from your code. That's what the noise looks like — and that's what VigilOps filters out. Why This Is Different Dependabot and Snyk scan your entire lockfile. They report every CVE in every package, regardless of whether your code ever touches the vulnerable surface. This creates alert fatigue that causes teams to eventually... stop reading. VigilOps inverts the model: only surface vulnerabilities in code you actually call. Dependabot: "Your project has 47 vulnerabilities" (but 40 are unreachable noise) VigilOps: "Your project has 1 reachable vulnerability. PR is ready." Quick Start npm install -g vigilops npx vigilops scan . Authenticate with GitHub: https://github.com/Vigilops/vigilops npx vigilops auth That's it. The first run will scan, analyze, and open a PR if there's a fixable reachable vulnerability. What's Included OSV.dev integrati

GitHub热门项目 | YouTube到AcFun和bilibili自动化搬运工具，支持AI翻译、字幕生成、内容审核、智能监控 | Stars: 1,535 | 211 stars today | 语言: Python

Quick confession: this started as "let me stop babysitting my tests and just make them stay green," and it turned into something a lot bigger. I want to be upfront about where I'm actually trying to take it. I built Claude Basecamp, and as of today it's open source. The reconciliation loop (declare "tests always green," it holds that true) is the part you'll notice first, but it's not really the point. The point is I want this to become the operating system for everything you do with Claude Code, one place that knows about every repo, every session, every routine, every connector, every skill, and every mistake it's ever made, instead of all of that living scattered across terminal windows and dead transcripts. Right now it already covers a decent chunk of that: npx claude-basecamp No install, no config. It finds the projects Claude Code already knows about and opens at http://localhost:4747 . Standing checks , the reconciliation loop. "Tests always green," "dependencies current," "the README documents every CLI flag" — say it once, Basecamp keeps it true, dispatches a fix run when it drifts, and only bugs you for the decisions that actually need a human. Reflexes. It goes back through your old transcripts, finds every time you said "no, don't do that," and turns it into a standing memory that every Claude Code session on your machine checks before touching Bash, Write, or Edit. A mistake made once doesn't get to happen a third time. Session Rescue. Resumes the actual dead session, same session ID, full context, when Claude Code dies mid-task, instead of starting over from scratch. A manager for every repo you just talk to: "keep the tests green," "track this goal," "what's the state of this repo?" Plus routines, background runs, an activity feed, stats, GitHub issue and PR hooks, notifications, webhooks, and a one-click catalog for connectors and skills. That's where it is today. What I actually want it to become is the default place you open whenever you're workin

If you’ve built AI applications in production recently, you’ve probably hit the "Agent Wall." You build a ReAct agent, give it 10 granular tools (search, extract, route, format), a massive system prompt, and tell it to go to work. It feels like magic...until you look at your latency metrics and token bills. Today’s agents act as interpreters. They re-derive the exact same routines from scratch on every single request . They embed massive tool schemas and reasoning histories into every loop. It's slow, it's incredibly token-hungry, and occasionally, they hallucinate tool calls, drop constraints, or get stuck in endless reasoning loops. In a production environment, even occasional errors can be critical failures that waste time and tokens. The problem isn't the ReAct pattern itself. The problem is that we are forcing the LLM to orchestrate low-level, predictable logic that should be deterministic code. We got tired of paying the "reasoning tax" for sub-routines that don't need it. So, we built Sparsi —a framework for shifting complex logic out of your ReAct agent's prompt and into deterministic "Macro-Tools" built as DAGs (Directed Acyclic Graphs). The Macro-Tool Pattern There are two ways to use Sparsi: as an end-to-end solution for a specific task, or to create higher-level tools that plug into your existing agents. The latter is where the magic happens. Instead of giving your ReAct agent 10 tiny, flaky tools and hoping it chains them correctly, you build one highly reliable, deterministic Sparsi DAG to handle that specific sub-routine. You then expose that DAG to your agent as a single Model Context Protocol (MCP) tool. The overall agent still drives the conversation, but it delegates the heavy lifting to a reliable macro-tool. We chose the DAG architecture for three main reasons: Deterministic & Testable: The graph is made of plain code. You only run AI where natural language understanding is strictly required. Parallel by Architecture: Independent branches run co

Schola Interlingua: Free Software for Learning Interlingua IALA Schola Interlingua is a free, open-source platform for learning Interlingua IALA. A practical place to learn The platform is designed around an educational path that brings together lessons, readings, review tools, and progress tracking. Rather than treating learning as a single activity, Schola Interlingua supports a rhythm of studying, reading, reviewing, and returning to the material. Its focus is Interlingua IALA: a language with transparent vocabulary and morphology that rewards attention to structure and usage. Free software and accessible learning Schola Interlingua is developed as open-source software. Its code is available through the Schola Interlingua GitHub organization , making the project available for examination, collaboration, and continued improvement. The platform is available across devices, so learners can use the same educational materials and study tools in the settings that work for them. Explore Schola Interlingua Schola Interlingua website Source code on GitHub Schola Interlingua on the App Store Schola Interlingua on Google Play Chety, for Mandarin Chinese etymology and morphology Ian Blas is a developer based in Buenos Aires, Argentina, building educational software for Mandarin Chinese, Interlingua IALA, etymology, morphology, writing systems, and open-source language learning.