⚡ NanmiCoder/MediaCrawler is making waves. Here's the full picture.

🔗 https://github.com/NanmiCoder/MediaCrawler
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MediaCrawler is a powerful multi-platform media data collection tool that supports the collection of publicly available information from popular platforms like Xiaohongshu, Douyin, Kuaishou, Bilibili, Weibo, Tieba, and Zhihu.

The tool is built using the Playwright browser automation framework and does not require JavaScript reverse engineering. It utilizes a saved login state and browser context to obtain signature parameters through JavaScript expressions.

Key features of MediaCrawler include:

* Support for multiple platforms
* Keyword search and specified post ID crawling
* Comment crawling and generation of word clouds
* Login state caching and IP proxy pool
* Data storage in various formats such as CSV, JSON, and Excel

Technical highlights include the use of Playwright for browser automation, a uv package manager for dependency management, and a Node.js installation for running the tool.

Audience: MediaCrawler is suitable for developers, researchers, and individuals who need to collect data from social media platforms for analysis, research, or other purposes.

To get started, users can install the required dependencies, configure the tool, and run the crawler using the provided commands.

One-liner takeaway: MediaCrawler is a versatile and powerful tool for collecting publicly available data from multiple social media platforms, making it an ideal solution for researchers, developers, and analysts.

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into addyosmani/agent-skills — fresh off the trending list.

🔗 https://github.com/addyosmani/agent-skills
📝 Production-grade engineering skills for AI coding agents.
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Agent Skills is a GitHub repository that provides production-grade engineering skills for AI coding agents. The repository includes 24 skills that encode workflows, quality gates, and best practices for senior engineers. These skills are packaged to help AI agents follow them consistently across every phase of development.

The repository offers 8 slash commands that map to the development lifecycle, including /spec, /plan, /build, /test, /review, /webperf, /code-simplify, and /ship. It also includes a /build auto command that generates a plan and implements every task in a single approved pass.

To get started, you can install the skills using the skills CLI or by cloning the repository and following the setup instructions for your preferred tool, such as Claude Code, Cursor, or Codex.

The repository is designed for developers who want to automate manual steps and ensure consistent quality in their code. The skills are structured workflows with steps, verification gates, and anti-rationalization tables that help AI agents make informed decisions.

Agent Skills is a powerful tool for any developer looking to improve their coding efficiency and quality - it's like having a senior engineer guiding your AI coding agent every step of the way!

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💡 paperclipai/paperclip just hit the trending charts — here's why it matters.

🔗 https://github.com/paperclipai/paperclip
📝 The open-source app everyone uses to manage agents at work
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Paperclip is an open-source platform that helps manage AI agents for work. It's designed to orchestrate a team of AI agents to run a business, allowing users to bring their own agents, assign goals, and track work and costs from one dashboard. Under the hood, Paperclip has org charts, budgets, governance, goal alignment, and agent coordination.

The platform is built around four pillars: Agentic Task Manager, Org Chart for Agents, Agent Employee Training, and Agentic OS. It features a Bring Your Own Agent approach, Goal Alignment, Heartbeats, Cost Control, and Governance.

Paperclip is suitable for those who want to build autonomous AI companies, coordinate multiple agents, and manage costs. The platform is mobile-ready, allowing users to monitor and manage their autonomous businesses from anywhere.

In short, Paperclip is the control plane for your autonomous business, and with it, you can manage business goals, not pull requests.

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into PrimeIntellect-ai/prime-agent — fresh off the trending list.

🔗 https://github.com/PrimeIntellect-ai/prime-agent
📝 A self-improving RLM agent for coding workflows and long-running autonomous tasks.
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The Prime Agent is a self-improving Recursive Language Model (RLM) agent designed for general and long-running work. Its key features include a persistent Python control environment and durable harness state, allowing useful working context and reusable operating patterns to outlive a single chat window.

The agent combines a Continual Harness that stores supplemental prompts, memories, and reusable subagent specifications with a Recursive Language Model (RLM) that treats context as variables and tools like recursive subagents as function calls.

You can install the agent using

curl -fsSL https://app.primeintellect.ai/prime-agent/install.sh | sh

and start it from the repository or directory you want it to work in.

Getting started is straightforward: simply run

prime-agent

and follow the prompts. The agent is built for long-running work, especially for evaluations in research, with features like direct agent-to-agent communication, daemon-backed continuity, and heartbeats and schedules.

The Prime Agent is perfect for researchers, developers, and anyone looking for a powerful tool to streamline their workflow.
Takeaway: With Prime Agent, you can work smarter, not harder.

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🧠 Channel: https://t.iss.one/GithubRe

🎯 LadybirdBrowser/ladybird landed on trending. Worth a proper look.

🔗 https://github.com/LadybirdBrowser/ladybird
📝 Truly independent web browser
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The Ladybird browser is a truly independent web browser, built using a novel engine based on web standards. It features a multi-process architecture with separate processes for UI, rendering, image decoding, and networking, making it more robust against malicious content.

Key features include out-of-process image decoding and network connections, as well as a sandboxed renderer process for each tab. Ladybird inherits various core library components from SerenityOS, such as LibWeb, LibJS, and LibCrypto.

To get started, follow the build instructions to build and run Ladybird on Linux, macOS, Windows, or other Unix-like systems.

The project is currently in a pre-alpha state and is primarily suited for developers. To participate, join the Discord server and read the CONTRIBUTING.md guidelines.

One-liner takeaway: Ladybird is a developer-oriented, independently-engineered browser that's still in its early stages, but shows great promise for a secure and robust browsing experience.

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: ruvnet/RuView — let's break it down.

🔗 https://github.com/ruvnet/RuView
📝 π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video.
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RuView is a WiFi sensing platform that turns radio signals into spatial intelligence, detecting people, tracking movement, and monitoring rooms without cameras or wearables. It works with major smart-home ecosystems like Home Assistant, Apple Home, Google Home, and Alexa.

Key features include presence detection, vital sign measurement, activity recognition, and environment mapping. It's built on RuVector and Cognitum Seed, running entirely on edge hardware like ESP32 nodes.

The system learns each environment locally using spiking neural networks and multi-frequency mesh scanning. It's designed for low-power edge applications, with edge modules that run directly on the ESP32 sensor.

RuView is perfect for those looking for a contactless, camera-free sensing solution for various applications, including health, security, and research.

One notable aspect is its wifi-densepose-pretrained model, which can estimate 17 body keypoints from WiFi CSI. With its guided operation and honesty checks, RuView is a robust and reliable choice.

In short, RuView turns WiFi into a powerful sensing tool, making it an exciting solution for various use cases, and its ability to provide accurate spatial intelligence without cameras or wearables is a game-changer.

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🧠 Channel: https://t.iss.one/GithubRe

🔥 TauricResearch/TradingAgents is trending — and it deserves your attention.

🔗 https://github.com/TauricResearch/TradingAgents
📝 TradingAgents: Multi-Agents LLM Financial Trading Framework
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The TradingAgents framework is a multi-agent trading platform that utilizes Large Language Models (LLMs) to inform trading decisions. It's designed to mimic real-world trading firms by deploying specialized agents, including fundamental analysts, sentiment experts, and technical analysts, to evaluate market conditions. The framework is highly customizable, supporting multiple LLM providers, such as OpenAI, Google, and Anthropic, and can be used for research purposes.

Key features include:

* Multi-agent architecture with specialized roles
* Support for multiple LLM providers
* Customizable configuration using environment variables or a `.env` file
* Docker support for easy deployment
* Interactive CLI for selecting tickers, analysis date, and LLM provider

Usage involves cloning the repository, creating a virtual environment, and installing the package and its dependencies. The framework can be run using the interactive CLI or by importing the `tradingagents` module in Python code.

From a technical standpoint, TradingAgents is built with LangGraph, ensuring flexibility and modularity. The framework supports various markets and tickers, including US, Hong Kong, Tokyo, London, and more.

The target audience is researchers and developers interested in building trading platforms using LLMs. With its modular design and support for multiple providers, TradingAgents is an excellent choice for those looking to explore the intersection of AI and finance.

In summary, TradingAgents is a powerful framework for building trading platforms using LLMs, offering a high degree of customizability and flexibility. With its interactive CLI and support for multiple providers, it's an excellent choice for researchers and developers looking to explore the possibilities of AI-driven trading: Automate your trading with TradingAgents - where AI meets finance.

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🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet google-deepmind/weathernext: a gem from today's GitHub trending list.

🔗 https://github.com/google-deepmind/weathernext
📝 No description.
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The google-deepmind/weathernext GitHub repository is home to the WeatherNext 2 (WN2) model, a global, medium-range atmospheric and cyclone forecasting model developed by Google DeepMind and Google Research. The repository also includes code for prior generation models, such as GraphCast and GenCast.

Key features of the WeatherNext models include global weather forecasting, cyclone tracking, and medium-range forecasting capabilities. Users can access the models' outputs through various platforms, including Google Cloud, WeatherLab, and OpenMeteo.

To get started with WeatherNext 2, users can run the provided Colab Notebook, which defaults to the WeatherNext Cyclones Mini model. The notebook demonstrates how to load model weights, initialize the model, and generate forecast predictions.

The repository provides pre-trained model weights for different versions of WeatherNext 2 and WeatherNext Cyclones, which can be downloaded from the Google Cloud Bucket. The models are implemented in JAX and require specific hardware, such as TPUs or GPUs, to run efficiently.

The target audience for this repository includes researchers, developers, and meteorologists interested in experimenting with and improving the WeatherNext models.

In summary: WeatherNext 2 is a powerful tool for global weather forecasting, and the google-deepmind/weathernext repository provides the code, models, and resources needed to get started - so, forecast the future with WeatherNext 2.

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🧠 Channel: https://t.iss.one/GithubRe