📌 Spotted on GitHub Trending: CloakHQ/CloakBrowser — let's break it down.

🔗 https://github.com/CloakHQ/CloakBrowser
📝 Stealth Chromium that passes every bot detection test. Drop-in Playwright replacement with source-level fingerprint patches. 30/30 tests passed.
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Meet CloakBrowser, a stealthy Chromium browser that passes every bot detection test. It's not a patched config or JS injection, but a real Chromium binary with fingerprints modified at the C++ source level. Antibot systems score it as a normal browser — because it is a normal browser.

Key features include humanize=True for human-like mouse curves, keyboard timing, and scroll patterns, and auto-updating binary with background update checks. It's also free and open source, with no subscriptions or usage limits.

Usage is simple: just pip install cloakbrowser or npm install cloakbrowser, and you're ready to go. The launch() function starts the browser, and you can use the standard Playwright or Puppeteer API.

Technical highlights include 49 source-level C++ patches, covering canvas, WebGL, audio, and more. The binary is verified with SHA-256 checksums to ensure integrity.

CloakBrowser is perfect for data scientists, web scrapers, and automation teams who need to bypass bot detection. With its ease of use and powerful features, it's a game-changer for anyone who needs to interact with websites programmatically.

One-liner takeaway: CloakBrowser makes bot detection disappear, so you can focus on what matters — getting the data you need.

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🧠 Channel: https://t.me/GithubRe

https://t.me/mira?start=ref_148350890

،The best, almost free, and officially verified AI agent on Telegram

⚡ rtk-ai/rtk is making waves. Here's the full picture.

🔗 https://github.com/rtk-ai/rtk
📝 CLI proxy that reduces LLM token consumption by 60-90% on common dev commands. Single Rust binary, zero dependencies
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Introducing rtk-ai/rtk, a high-performance CLI proxy that reduces LLM token consumption by 60-90%. This single Rust binary supports over 100 commands and has an overhead of less than 10ms. Key features include smart filtering, grouping, truncation, and deduplication, which are applied per command type to minimize token usage.

To get started, users can install rtk using Homebrew, a quick install script, Cargo, or pre-built binaries. The installation process is straightforward, and the rtk --version command can be used to verify the installation.

Technical highlights of rtk include its ability to filter and compress command outputs before they reach the LLM context. This is achieved through a hook-based system that rewrites Bash commands to their rtk equivalents before execution. The result is a significant reduction in token consumption, making it an ideal solution for developers, data scientists, and anyone working with large language models.

Audience for rtk includes anyone looking to optimize their LLM workflow and reduce token consumption. Whether you're working with Claude Code, Copilot, or other AI tools, rtk can help you achieve your goals.

In summary, rtk-ai/rtk is a powerful tool for reducing LLM token consumption, and its ease of use, flexibility, and high-performance capabilities make it a must-have for anyone working with large language models: rtk your way to token savings today!

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🧠 Channel: https://t.me/GithubRe

🔥 msitarzewski/agency-agents is trending — and it deserves your attention.

🔗 https://github.com/msitarzewski/agency-agents
📝 A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.
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Introducing The Agency: a collection of AI agent personalities, each with unique expertise and deliverables. From engineering to design and sales, these agents can transform your workflow.

Key features include:
- Specialized deep expertise
- Personality-Driven communication styles
- Deliverable-Focused outcomes
- Production-Ready workflows

Usage options:
- Integrate with Claude Code or other tools like GitHub Copilot or Antigravity
- Use as a reference for best practices
- Browse the agent roster and copy/adapt what you need

Technical highlights include:
- Multi-tool integrations
- Production-ready workflows
- Success metrics for evaluation

This repository is perfect for developers, designers, and sales teams looking to augment their workflows with AI-powered agents.

In a nutshell: The Agency is your ultimate AI team, ready to deliver - never sleep, never complain, always deliver!

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🧠 Channel: https://t.me/GithubRe

💡 colbymchenry/codegraph just hit the trending charts — here's why it matters.

🔗 https://github.com/colbymchenry/codegraph
📝 Pre-indexed code knowledge graph for Claude Code, Codex, Cursor, and OpenCode — fewer tokens, fewer tool calls, 100% local
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CodeGraph is a game-changer for developers, supercharging tools like Claude Code, Cursor, Codex, and OpenCode with semantic code intelligence. This powerful tool provides a pre-indexed knowledge graph, enabling agents to query instantly instead of scanning files. With CodeGraph, you can enjoy 94% fewer tool calls and 77% faster exploration.

To get started, simply run npx @colbymchenry/codegraph and follow the interactive installer. Initialize your projects with codegraph init -i, and you're ready to roll.

Some key features of CodeGraph include smart context building, full-text search, impact analysis, and framework-aware routes. It supports 19+ languages and is 100% local, ensuring your data never leaves your machine.

CodeGraph is perfect for developers looking to boost their productivity and streamline their workflow. So why wait? Try CodeGraph today and experience the power of semantic code intelligence - your code, supercharged!

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🧠 Channel: https://t.me/GithubRe

⚡ multica-ai/andrej-karpathy-skills is making waves. Here's the full picture.

🔗 https://github.com/multica-ai/andrej-karpathy-skills
📝 A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
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The multica-ai/andrej-karpathy-skills GitHub repository provides a set of guidelines to improve the behavior of Claude Code, a coding agent. Inspired by Andrej Karpathy's observations on the pitfalls of Large Language Models (LLMs) in coding, these guidelines aim to address issues such as wrong assumptions, overcomplication, and lack of clarity. The guidelines are based on four principles: Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution.

The guidelines can be installed as a Claude Code plugin or added to a project's CLAUDE.md file. They provide a framework for LLMs to loop until they meet specific goals, reducing the need for constant clarification. The guidelines are designed to be merged with project-specific instructions and can be customized to fit the needs of a particular project.

For example, to install the guidelines as a Claude Code plugin, you can use the following commands:

/plugin marketplace add forrestchang/andrej-karpathy-skills
/plugin install andrej-karpathy-skills@karpathy-skills

These guidelines are working if you see fewer unnecessary changes in diffs, fewer rewrites due to overcomplication, and clean, minimal PRs. Overall, the multica-ai/andrej-karpathy-skills repository provides a valuable resource for improving the behavior of coding agents and reducing costly mistakes. Give your LLMs success criteria and watch them go - it's that simple!

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🧠 Channel: https://t.me/GithubRe

🔥 humanlayer/12-factor-agents is trending — and it deserves your attention.

🔗 https://github.com/humanlayer/12-factor-agents
📝 What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
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The 12-factor-agents GitHub repository provides a set of principles for building reliable and scalable LLM-powered applications. The project, inspired by the 12 Factor Apps methodology, aims to help developers create more maintainable and efficient software.

Purpose: The main goal of 12-factor-agents is to establish a set of guidelines for developing LLM-powered applications that are production-ready and can be easily maintained.

Some key features of the project include:
- A set of 12 factors that provide guidance on building reliable LLM-powered software
- A brief history of software development and how it led to the creation of the 12-factor-agents project
- Examples and illustrations to help explain complex concepts

Usage of the project involves following the 12 factors, which cover topics such as:
- Factor 1: Natural Language to Tool Calls
- Factor 2: Own your prompts
- Factor 3: Own your context window
- And many more

From a technical standpoint, the project highlights the importance of:
- Using a stateless reducer to manage the application's state
- Implementing a loop that consists of LLM determination, deterministic code execution, and context window updates
- Utilizing a DAG (Directed Acyclic Graph) to represent the application's workflow

The target audience for the project includes:
- Developers interested in building LLM-powered applications
- Software engineers looking to create more maintainable and efficient software
- Anyone interested in learning about the 12-factor-agents principles and how to apply them

In conclusion, the 12-factor-agents project provides a comprehensive set of guidelines for building reliable and scalable LLM-powered applications. By following the 12 factors and understanding the technical concepts behind the project, developers can create more maintainable and efficient software. The ultimate takeaway is: build software that is designed to thrive in a world where LLMs are getting exponentially more powerful.

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🧠 Channel: https://t.me/GithubRe