🔍 Deep-diving into affaan-m/ECC — fresh off the trending list.

🔗 https://github.com/affaan-m/ECC
📝 The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
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The ECC repository on GitHub is a harness-native operator system designed for agentic work. It's built from real-world multi-harness engineering workflows, offering a complete system with skills, instincts, memory optimization, continuous learning, security scanning, and research-first development.

The system works across various AI agent harnesses, including Codex, Claude Code, Cursor, OpenCode, Gemini, Zed, and GitHub Copilot.

Key features include production-ready agents, skills, hooks, rules, and configurations, all of which have evolved over 10+ months of intensive daily use in building real products.

The repository has a large community with 182K+ stars, 28K+ forks, and 170+ contributors, making it a significant project in the open-source space.

The system is written in multiple programming languages, including Shell, TypeScript, Python, Go, Java, and Perl, and it supports 12+ language ecosystems.

To get started, users can refer to the Shorthand Guide, Longform Guide, and Security Guide for detailed information on setup, foundations, philosophy, token optimization, memory persistence, and security.

In summary, ECC is a powerful tool for agentic work, with a wide range of features, a large community, and support for multiple programming languages, making it an ideal choice for developers and operators alike.

The takeaway: ECC is the ultimate operator system for agentic work, empowering developers to build and manage complex workflows with ease.

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🧠 Channel: https://t.me/GithubRe

🔥 Panniantong/Agent-Reach is trending — and it deserves your attention.

🔗 https://github.com/Panniantong/Agent-Reach
📝 Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
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Agent Reach 是一个让 AI Agent 无缝访问各大网站和平台的工具，解决了 Agent 访问网页、YouTube、Twitter、Reddit、GitHub 等平台的痛点。它通过提供了一套简单的命令行工具，让 AI Agent 可以轻松地搜索、阅读和交互这些平台的内容。

主要特点：

* 支持多个平台，包括网页、YouTube、Twitter、Reddit、GitHub 等
* 提供简单的命令行工具，让 AI Agent 可以轻松地访问这些平台
* 自动配置和安装，方便用户使用
* 支持安全模式，确保用户的安全

安装命令：
```
帮我安装 Agent Reach：https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/install.md
```
总结：Agent Reach 是一个简单却强大的工具，让 AI Agent 可以无缝访问各大网站和平台，解决了访问这些平台的痛点。通过提供简单的命令行工具和自动配置，Agent Reach 让用户可以轻松地使用这些平台。只要一行代码，你的 AI Agent 就可以成为一个全能的互联网助手。

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🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: NVIDIA/cosmos — let's break it down.

🔗 https://github.com/NVIDIA/cosmos
📝 NVIDIA Cosmos is an open platform of world models, datasets, and tools that enables developers to build Physical AI for robots, autonomous vehicles, smart infrastructure, and more.
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NVIDIA Cosmos is an open platform for building Physical AI, providing a suite of world models, datasets, and tools. Cosmos 3 is the latest model family, designed to jointly process and generate language, images, video, audio, and action sequences. It has two runtime surfaces: Reasoner for world understanding and Generator for world generation.

Key features include:
- World understanding: analyze videos and images for captions, temporal events, and physical plausibility
- World generation: produce images, videos, sound, and action-conditioned rollouts from text, image, video, or action inputs
- Action modeling: predict policy actions for robotics and autonomous-driving settings

Cosmos 3 has a unified Mixture-of-Transformers architecture, combining an autoregressive transformer for reasoning with a diffusion transformer for multimodal generation. The model family includes Cosmos3-Nano, Cosmos3-Super, and specialized models for text-to-image and image-to-video generation.

To get started, create a Hugging Face access token, authenticate locally, and set up a virtual environment with the required dependencies. You can use HuggingFace Diffusers for research, training, and model development.

One-liner takeaway: NVIDIA Cosmos is revolutionizing Physical AI by providing a powerful platform for world understanding and generation, enabling developers to build more sophisticated robots, autonomous vehicles, and smart infrastructure.

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🧠 Channel: https://t.me/GithubRe

🌟 666ghj/MiroFish caught my eye on GitHub Trending today.

🔗 https://github.com/666ghj/MiroFish
📝 A Simple and Universal Swarm Intelligence Engine, Predicting Anything. 简洁通用的群体智能引擎，预测万物
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MiroFish is a cutting-edge AI prediction engine that utilizes multi-agent technology to forecast outcomes. By analyzing real-world data, it creates a parallel digital world where thousands of intelligent agents interact and evolve. This allows users to rehearse the future in a digital sandbox and make informed decisions after simulating various scenarios.

Key features include:
- Graph Building: extracting seed information and constructing a high-fidelity digital world
- Simulation: running parallel simulations to predict future trajectories
- Report Generation: generating detailed prediction reports

The workflow involves:
1. Graph building and environment setup
2. Simulation and report generation
3. Deep interaction with the simulated world

Technical highlights include:
- Utilization of OASIS (Open Agent Social Interaction Simulations) for the simulation engine
- Support for LLM API and Zep Cloud configurations

The target audience includes decision-makers, researchers, and individuals interested in exploring what if scenarios.

To get started, users can deploy MiroFish via source code or Docker, and join the conversation on social media platforms.

In a nutshell, MiroFish is all about predicting anything - from serious predictions to playful simulations, making it possible to rehearse the future and win decisions after countless simulations.

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🧠 Channel: https://t.me/GithubRe

⚡ mvanhorn/last30days-skill is making waves. Here's the full picture.

🔗 https://github.com/mvanhorn/last30days-skill
📝 AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
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The mvanhorn/last30days-skill GitHub repository is home to a revolutionary AI agent-led search engine. This engine scores results based on upvotes, likes, and real money, rather than editor opinions. Key features include zero-config setup, immediate functionality with Reddit, HN, Polymarket, and GitHub, and the ability to unlock more platforms like X, YouTube, and TikTok in just 30 seconds.

Technical highlights of this repository include the use of a pre-research brain built in Python, which resolves topics and figures out where to search before the search begins. The engine also features intelligent search, cross-source cluster merging, and single-pass comparisons, making it a powerful tool for finding relevant information.

This repository is perfect for anyone looking to stay up-to-date on the latest developments in their field, including developers, researchers, and industry professionals. With its ability to search multiple platforms at once and provide a brief summary of the most relevant information, mvanhorn/last30days-skill is an invaluable resource.

To get started, users can install the skill using /plugin marketplace add mvanhorn/last30days-skill or npx skills add mvanhorn/last30days-skill -g. The repository is constantly being updated with new features and improvements, making it an exciting project to follow.

One-liner takeaway: mvanhorn/last30days-skill is a game-changing search engine that uses AI to scour multiple platforms and provide you with the most relevant, up-to-date information on any topic.

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🧠 Channel: https://t.me/GithubRe