CloakBrowser: Stealth Chromium That Passes Every Bot Detection Test — 25,000 Stars for Scraping — A Practical Guide 2026 ┌──────────────────────────────────────────────────────┐ │ CloakBrowser Anti-Detection │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Canvas │ │ WebGL │ │ Audio │ │ │ │ Fingerprint │ │ Fingerprint │ │ Fingerprint │ │ │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │ │ │ │ │ │ ┌──────▼────────────────▼─────────────────▼──────┐ │ │ │ Source-Level Patches │ │ │ │ • navigator.webdriver = false │ │ │ │ • chrome runtime spoofing │ │ │ │ • TLS fingerprint randomization │ │ │ │ • WebRTC leak prevention │ │ │ │ • Headless detection bypass │ │ │ │ • Geolocation spoofing │ │ │ └───────────────────────┬───────────────────────┘ │ │ │ 30/30 tests passed │ │ ┌───────────────────────▼───────────────────────┐ │ │ │ Anti-detection browser │ │ │ └───────────────────────────────────────────────┘ │ └──────────────────────────────────────────────────────┘ CloakBrowser: drop-in Playwright replacement that passes every bot test ...

title: “Clone and install” description: “Technical guide and comparison.” date: 2026-05-15T04:20:25+09:00 lastmod: 2026-05-15T04:20:25+09:00 tech_stack: - Docker Go Java JavaScript Rust TypeScript application_domain: “Llm Frameworks” source_version: "" licensing_model: “Open Source” license_type: “MIT” file_size: “3.2 MB” file_md5: "" download_url: “https://github.com/rtk-ai/rtk" backup_url: "” last_maintained: “2026-05-15” draft: false aliases: /posts/9router-smart-llm-proxy-token-saver-free-coding/ faqs: - q: ‘What is 9Router and how does it work?’ a: ‘9Router is an open-source, self-hosted smart proxy that sits between your AI coding tool and model providers, running on localhost:20128 by default. Instead of calling Claude or OpenAI directly, your tool sends requests to 9Router, which routes them across 40+ providers using intelligent fallback logic and token compression.’ q: ‘Does 9Router cost money to use?’ a: ‘No. The 9Router software is free forever under an open-source MIT license and is self-hosted on your own hardware. It has no billing infrastructure and never charges you; you only pay providers directly for whatever subscriptions or API keys you configure, and free providers stay free. The dashboard’’s cost figures are a savings tracker showing what equivalent paid-API usage would have cost.’ q: ‘How much does 9Router reduce AI token usage?’ a: ‘Through its built-in RTK compression, 9Router compresses tool outputs like git diffs, grep results, and directory listings before they reach the LLM, saving 20-40% of input tokens per request. Its optional Caveman mode reduces verbose model output by up to 65%, and developers making 500+ daily calls report effective model consumption dropping 40-60%.’ q: ‘Which AI coding tools and IDEs work with 9Router?’ a: ‘Any tool that supports a custom OpenAI-compatible API endpoint can connect to 9Router via http://localhost:20128/v1. Supported tools include Claude Code, OpenAI Codex CLI, Cursor IDE, GitHub Copilot, Cline, Continue, Roo Code, Antigravity, Droid, Kilo Code, and OpenCode.’ q: ‘Can I use 9Router for AI coding at zero monthly cost?’ a: ‘Yes. You can build a combo using only free providers such as Kiro AI (free unlimited via AWS Builder ID, Google, or GitHub OAuth, no API key), OpenCode Free (zero auth passthrough), and Vertex AI ($300 free Google Cloud credits). Combined with RTK compression, this delivers production-quality responses at literally $0 per month.’— ...

{</* resource-info */>} Introduction: This Is Not Just Another Tool Upgrade If you’re still equating “AI coding” with GitHub Copilot’s autocomplete suggestions, you’ve missed the most significant platform shift in developer tooling since the IDE wars of the early 2000s. Between January and May 2026, the AI coding assistant space transformed from “intelligent autocomplete” to “autonomous software engineering.” Claude Code’s skills ecosystem went from an experimental curiosity to a marketplace with thousands of public skills. OpenAI rebuilt Codex CLI from scratch. Google shipped Gemini 3 Pro alongside Antigravity, an agentic IDE platform. And in the open-source corner, Hermes Agent added 32,000 GitHub stars in a single week, while Andrej Karpathy’s personal skills repository pulled 44,000 stars in seven days. ...

title: “Clone repository” description: “Technical guide and comparison.” No Chrome dependencies, no corporate influence, pure open source. date: 2026-05-15T04:20:25+09:00 lastmod: 2026-05-15T04:20:25+09:00 tech_stack: - C++ Docker Go Java JavaScript application_domain: Ai Tools source_version: ’' licensing_model: Open Source license_type: MIT file_size: “436.4 MB” file_md5: ’' download_url: https://github.com/LadybirdBrowser/ladybird backup_url: ’' last_maintained: “2026-05-15” draft: false aliases: /posts/ladybird-independent-web-browser/ faqs: - q: ‘What is the Ladybird browser?’ a: ‘Ladybird is a truly independent web browser built entirely from scratch, without relying on Chromium, Firefox, or any other existing browser engine. It uses its own rendering engine (LibWeb) and JavaScript engine (LibJS), written in C++.’ q: ‘Who created the Ladybird browser?’ a: ‘Ladybird was created by Andreas Kling, who is also the creator of SerenityOS and a former Apple Safari engineer. The project is now developed by a global community of over 100 open-source contributors.’ q: ‘Is Ladybird ready for daily use?’ a: ‘No. Ladybird is still under active development and not yet ready for daily use. Basic HTML/CSS, JavaScript, forms, images, tables, and Flexbox work, but features like WebGL, video, and WebAssembly are not yet supported.’ q: ‘Why does an independent browser engine like Ladybird matter?’ a: ‘Roughly 73% of browsers use Google’’s Chromium engine, giving Google strong influence over web standards. An independent engine adds diversity, reduces a single point of failure, and supports true privacy with no telemetry or corporate tracking.’ q: ‘How do you install or try Ladybird?’ a: ‘You build Ladybird from source by cloning its GitHub repository, installing dependencies (build-essential, cmake, ninja-build on Ubuntu/Debian), then building with CMake and Ninja and running ./bin/Ladybird. An experimental Docker image is also available.’ {</* resource-info */>} ...

title: “Chatwoot 2026: The Open-Source Customer Support Platform… description: “Technical guide and comparison.” date: 2026-05-19T00:00:00+08:00 lastmod: 2026-05-19T00:00:00+08:00 tech_stack: [] application_domain: Dev Utils source_version: ’' licensing_model: Open Source license_type: MIT file_size: ’' file_md5: ’' download_url: ’' backup_url: ’' last_maintained: “2026-05-19” draft: false categories: [“dev-utils”] tags: [“chatwoot”, “customer-support”, “open-source”, “ai-chatbot”, “self-hosted”, “docker”, “ruby-on-rails”, “live-chat”] aliases: /posts/chatwoot-open-source-customer-support-ai/- Introduction: Why Your Support Stack Needs a Reset In 2025, the average SaaS company spent $847 per agent per month on customer support tooling — Zendesk, Intercom, Freshdesk, and a patchwork of AI add-ons. That is $10,164 per seat annually for what is essentially a ticket database with a chat widget. For a 20-person support team, you are looking at over $200,000/year before salaries. ...

title: “Codebase Memory MCP: 24K+ Star AI Code Intelligence Server” description: “Technical guide and comparison.” date: 2026-07-03T09:00:00+09:00 lastmod: 2026-07-03T09:00:00+09:00 slug: codebase-memory-mcp-deep-code-intelligence category: llm-frameworks tags: [“mcp”, “code-intelligence”, “ai-agents”, “vector-search”, “open-source”] tech_stack: - C Rust Python Editor’s Disclosure: This analysis uses publicly available GitHub data (star counts, commit frequency, fork counts) as of June 30, 2026. All code examples are tested and verified. We may earn a commission from affiliate links. TL;DR Codebase Memory MCP (24K+ stars) is a high-performance Model Context Protocol (MCP) server that transforms any LLM into a codebase-aware assistant. By indexing entire repositories into persistent vector memory, it enables AI agents to understand, navigate, and reason about code at a scale that traditional token-limited approaches cannot achieve. Built with C and Rust for maximum performance, it processes 100K+ line codebases in seconds. ...

Codebase-Memory-MCP: High-Performance Code Intelligence for AI Coding Agents In the rapidly evolving landscape of AI-assisted software development, one bottleneck remains stubbornly persistent: how do AI coding agents efficiently understand and navigate large codebases? Traditional approaches like file-by-file search or naive RAG systems waste enormous amounts of tokens, produce fragmented context, and struggle with structural code understanding. Enter codebase-memory-mcp — a revolutionary MCP (Model Context Protocol) server that transforms how AI coding agents interact with code. With 7,100+ GitHub stars and 2,300 stars added today alone, this project represents the cutting edge of code intelligence. ...

Codegraph: The Code Knowledge Graph That Slashes LLM Token Costs by 40-60% — Pre-Indexed, 100% Local — A Practical Guide 2026 ┌──────────────────────────────────────────────────────┐ │ Codegraph Knowledge Graph Engine │ │ │ │ ┌────────────┐ ┌────────────┐ ┌───────────┐ │ │ │ Source Code │ │ Configs │ │ Docs │ │ │ │ (.py,.ts) │ │ (.yaml) │ │ (.md) │ │ │ └─────┬──────┘ └─────┬──────┘ └─────┬─────┘ │ │ │ │ │ │ │ ▼ ▼ ▼ │ │ ┌───────────────────────────────────────────────┐ │ │ │ Codegraph Indexer & Graph Builder │ │ │ │ • AST Parsing • Dependency Analysis │ │ │ │ • Symbol Linking • Call Graph Construction │ │ │ └───────────────────────┬───────────────────────┘ │ │ │ Local vector store │ │ ┌───────────────────────▼───────────────────────┐ │ │ │ AI Agent Query (Claude Code / Codex / ...) │ │ │ │ Returns: relevant code snippets, not entire repo │ │ └───────────────────────────────────────────────┘ │ └──────────────────────────────────────────────────────┘ Codegraph: source code → knowledge graph → precise agent queries ...

title: “CogVideo: 视频生成模型部署与使用指南” description: “Technical guide and comparison.” date: 2026-05-19T00:00:00+08:00 lastmod: 2026-05-19T00:00:00+08:00 tech_stack: [] application_domain: Ai Tools source_version: ’' licensing_model: Open Source license_type: Apache-2.0 file_size: ’' file_md5: ’' download_url: ’' backup_url: ’' last_maintained: “2026-05-19” draft: false categories: [“ai-tools”] tags: [“cogvideo”, “cogvideox”, “text-to-video”, “diffusion-transformer”, “zhipu-ai”, “comfyui”, “open-source-ai”, “video-generation”] aliases: /posts/cogvideo/- Turn text and images into cinematic video with Zhipu AI’s open-source diffusion transformer. From zero to production in under 30 minutes. ...

title: “Multi-Modal Content Pipeline 2026: The 5-Component Stack… description: “Technical guide and comparison.” date: 2026-05-21T00:00:00+08:00 lastmod: 2026-05-21T00:00:00+08:00 tech_stack: - Python PyTorch CUDA FFmpeg application_domain: Collections source_version: ’' licensing_model: Open Source license_type: MIT file_size: ’' file_md5: ’' download_url: ’' backup_url: ’' last_maintained: “2026-05-21” draft: false categories: [“collections”] tags: [“multi-modal”, “content pipeline”, “podcast”, “video”, “tts”, “stack”, “collection”] aliases: /posts/multi-modal-content-pipeline/- The 2026 creator economy runs on multi-modal content — podcasts with AI co-hosts, short-form video with AI narration over generated visuals, blog posts with AI-illustrated header images, audiobooks read by stable AI voices. The SaaS-stack way costs $200-500/month (ElevenLabs + Midjourney + Descript + Pictory + a dozen others). This collection assembles the self-hosted 5-component alternative for $30-80/month — using the same models the SaaS providers use, on a GPU you rent by the hour. ...