🌟 ruvnet/ruflo caught my eye on GitHub Trending today.

🔗 https://github.com/ruvnet/ruflo
📝 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integration, and native Claude Code / Codex Integration
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Ruflo is a multi-agent AI orchestration platform that adds coordinated swarms, self-learning memory, federated comms, and enterprise security to Claude Code. It enables 100+ specialized AI agents to collaborate across machines, teams, and trust boundaries. With init, Ruflo gives Claude Code a nervous system, allowing agents to self-organize into swarms, learn from every task, and remember across sessions.

The key features of Ruflo include:
* Swarm Coordination: Hierarchical, mesh, and adaptive topologies with consensus
* Self-Learning: SONA neural patterns, ReasoningBank, and trajectory learning
* Vector Memory: HNSW-indexed AgentDB with faster search
* Plugin Marketplace: 32 native Claude Code plugins and 21 npm plugins
* Multi-Provider: Claude, GPT, Gemini, Cohere, Ollama with smart routing

To get started, you can install Ruflo as a native Claude Code plugin or use the CLI install method. The plugin marketplace offers a wide range of plugins, including core and orchestration, memory and knowledge, intelligence and learning, and security and compliance.

Ruflo also features a Web UI (Beta) that is self-hostable and offers a hosted demo at flo.ruv.io. The web UI allows for multi-model AI chat with built-in Model Context Protocol (MCP) tool calling, persistent vector memory, and swarm coordination.

Additionally, Ruflo has a Goal Planner UI at goal.ruv.io that enables autonomous agents to turn high-level goals into executable plans.

Overall, Ruflo is a powerful platform that enables the coordination of multiple AI agents to achieve complex tasks, making it an exciting tool for anyone interested in AI and machine learning. With its ease of use and extensive features, Ruflo is a game-changer in the world of AI orchestration. Ruflo is the future of AI collaboration.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet virattt/dexter: another gem from today's GitHub trending list.

🔗 https://github.com/virattt/dexter
📝 An autonomous agent for deep financial research
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I'm excited to share with you the GitHub repository virattt/dexter, an autonomous financial research agent that thinks, plans, and learns as it works. Dexter takes complex financial questions and turns them into clear, step-by-step research plans, using live market data to gather information and refine results until it has a confident, data-backed answer.

The key capabilities of Dexter include:
* Intelligent Task Planning: Automatically decomposes complex queries into structured research steps
* Autonomous Execution: Selects and executes the right tools to gather financial data
* Self-Validation: Checks its own work and iterates until tasks are complete
* Real-Time Financial Data: Access to income statements, balance sheets, and cash flow statements
* Safety Features: Built-in loop detection and step limits to prevent runaway execution

To get started with Dexter, you'll need to install the Bun runtime and obtain API keys for OpenAI, Financial Datasets, and Exa (optional). You can install Bun using curl:

curl -fsSL https://bun.com/install | bash

Then, clone the repository, install dependencies with Bun, and set up your environment variables.

Dexter can be run in interactive mode using bun start or with watch mode for development using bun dev. The repository also includes an evaluation suite that tests the agent against a dataset of financial questions.

For debugging, Dexter logs all tool calls to a scratchpad file, making it easy to inspect exactly what data the agent gathered and how it interpreted results. You can also use Dexter with WhatsApp by linking your phone to the gateway.

The project is licensed under the MIT License, and contributions are welcome. If you're interested in contributing, simply fork the repository, create a feature branch, commit your changes, and create a pull request.

Overall, Dexter is a powerful tool for financial research, and its autonomous capabilities make it an exciting development in the field. In my opinion, Dexter is a game-changer for financial research, and its potential applications are vast and promising.

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🧠 Channel: https://t.me/GithubRe

🌟 bwya77/vscode-dark-islands caught my eye on GitHub Trending today.

🔗 https://github.com/bwya77/vscode-dark-islands
📝 VSCode theme based off the easemate IDE and Jetbrains islands theme
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The Islands Dark theme for Visual Studio Code is a dark color theme inspired by the easemate IDE, featuring floating glass-like panels, rounded corners, smooth animations, and a deeply refined UI. Key features include a deep dark canvas, glass-effect borders, and rounded corners on all panels. To use the theme, you can install it via a one-liner command or manual clone install. The theme also includes Custom UI Style extensions and recommended icon themes for the best experience.

Technical highlights include the use of Custom UI Style for floating panels and glass borders, as well as IBM Plex Mono and FiraCode Nerd Font Mono fonts. The theme is customizable via CSS custom properties and includes a range of color variables and border radius variables to adjust the look and feel.

This theme is suitable for developers and designers who prefer a dark and sleek interface for Visual Studio Code. With its unique blend of style and functionality, the Islands Dark theme is a great choice for anyone looking to enhance their coding experience.
In short, Islands Dark is a must-try theme for anyone who wants to give their VS Code a fresh new look.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet mksglu/context-mode: a gem from today's GitHub trending list.

🔗 https://github.com/mksglu/context-mode
📝 Context window optimization for AI coding agents. Sandboxes tool output, 98% reduction. 14 platforms
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Context Mode is a solution to the problem of context overload in tools like Playwright and GitHub. It's an MCP server that solves four key issues: context saving, session continuity, think in code, and output compression. With Context Mode, tools keep raw data out of the context window, reducing context size by up to 98%. It also tracks file edits, git operations, and other events, allowing the model to pick up where it left off.

The ctx_execute function runs scripts that replace multiple tool calls, saving context. For example:

ctx_execute("javascript", `
  const files = fs.readdirSync('src').filter(f => f.endsWith('.ts'));
  files.forEach(f => console.log(f + ': ' + fs.readFileSync('src/'+f,'utf8').split('\\n').length + ' lines'));
`);

This approach reduces output tokens by ~65-75% while maintaining technical accuracy.

Installation varies by platform, with options for Claude Code, Gemini CLI, and VS Code Copilot. The demo showcases Context Mode's capabilities.

The takeaway: Context Mode is the key to unlocking efficient context management, and it's a game-changer for anyone working with MCP tools.

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🧠 Channel: https://t.me/GithubRe

🎯 ruvnet/ruflo landed on trending. Worth a proper look.

🔗 https://github.com/ruvnet/ruflo
📝 🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, self-learning swarm intelligence, RAG integration, and native Claude Code / Codex Integration
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Ruflo is a multi-agent AI orchestration platform for Claude Code. It enables 100+ specialized AI agents to collaborate across machines, teams, and trust boundaries. With Ruflo, agents self-organize into swarms, learn from every task, and remember across sessions. The platform provides a federated comms layer, self-learning capabilities, and vector memory.

To get started, users can choose between two installation paths: the Claude Code Plugin for a lightweight setup or the CLI install for the full Ruflo loop. The CLI install provides a comprehensive setup with 98 agents, 60+ commands, and 30 skills.

Ruflo's key features include swarm coordination, self-learning, and plugin marketplace with 32 native plugins. It also supports multi-provider models, including Claude, GPT, and Cohere, with smart routing. For security, Ruflo offers AIDefence, input validation, and CVE remediation.

Ruflo is suitable for developers, researchers, and organizations looking to leverage AI orchestration for coding, testing, security, and documentation. The platform is designed to be extensible, with a wasm plugin for running sandboxed WebAssembly agents.

Takeaway: Ruflo revolutionizes AI collaboration by providing a seamless, self-learning, and secure platform for multi-agent orchestration, making it an ultimate tool for anyone looking to harness the power of AI.

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🧠 Channel: https://t.me/GithubRe

💡 addyosmani/agent-skills just hit the trending charts — here's why it matters.

🔗 https://github.com/addyosmani/agent-skills
📝 Production-grade engineering skills for AI coding agents.
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The agent-skills repository provides a set of production-grade engineering skills for AI coding agents. These skills encode workflows, quality gates, and best practices that senior engineers use when building software. The repository includes 7 slash commands that map to the development lifecycle, activating the right skills automatically.

The skills can be used with various tools such as Claude Code, Cursor, Gemini CLI, Windsurf, OpenCode, and GitHub Copilot. The repository also includes 20 skills that cover different aspects of software development, from defining and planning to building, verifying, and shipping.

The skills are designed to be process-oriented, with a focus on workflows and step-by-step instructions rather than reference documentation. They also include anti-rationalization tables to help agents overcome common excuses for skipping steps.

The key takeaway is: equip your AI coding agents with human-like skills to streamline your development workflow.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet PriorLabs/TabPFN: a gem from today's GitHub trending list.

🔗 https://github.com/PriorLabs/TabPFN
📝 ⚡ TabPFN: Foundation Model for Tabular Data ⚡
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Introduction to TabPFN: TabPFN is a powerful, PyTorch-based implementation of the TabPFN model, designed for fast and local inference with CUDA support. It's ideal for classification and regression tasks on tabular data.

The tabpfn library provides an easy-to-use interface for creating and training TabPFN models. You can install it via pip: pip install tabpfn.

To get started, use the default TabPFN-2.6 model with the following code:

from tabpfn import TabPFNClassifier, TabPFNRegressor

clf = TabPFNClassifier()
clf.fit(X_train, y_train)  
predictions = clf.predict(X_test)

reg = TabPFNRegressor()
reg.fit(X_train, y_train)  
predictions = reg.predict(X_test)

Key Features and Tips:
- For optimal performance, use a GPU (even older ones with ~8GB VRAM work well).
- Batch prediction mode is recommended, as each predict call recomputes the training set.
- Avoid data preprocessing when feeding data to the model.

Technical Highlights: TabPFN is part of a larger ecosystem, including the TabPFN Client for cloud-based inference and TabPFN Extensions for advanced utilities and features.

Audience: This library is suitable for data scientists and machine learning engineers working with tabular data.

In summary, TabPFN is a powerful tool for tabular data modeling - give it a try and experience the difference for yourself.

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🧠 Channel: https://t.me/GithubRe