🔥 calesthio/OpenMontage is trending — and it deserves your attention.

🔗 https://github.com/calesthio/OpenMontage
📝 World's first open-source, agentic video production system. 12 pipelines, 52 tools, 500+ agent skills. Turn your AI coding assistant into a full video production studio.
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Unlock the power of video production with OpenMontage, the first open-source, agentic video production system. This innovative platform allows you to create stunning videos by simply describing what you want in plain language, and your agent handles the rest - from research and scripting to asset generation, editing, and final composition.

Key features of OpenMontage include:
- Starting from a reference video to create a grounded production plan
- Generating AI images, writing and narrating scripts, and finding royalty-free background music
- Rendering final videos with real-footage or image-based paths
- Utilizing various tools and providers, such as Remotion, HyperFrames, and FFmpeg

Technical highlights include:
- Support for multiple AI coding assistants, such as Claude Code, Cursor, and Copilot
- Optional API keys for additional tools and providers, such as FLUX, Google Veo, and OpenAI
- Local video generation capabilities with GPU support

OpenMontage is designed for creatives, developers, and anyone looking to produce high-quality videos without extensive video production experience. With its user-friendly interface and robust features, OpenMontage makes it easy to bring your video ideas to life.

Get started by cloning the repository, installing the prerequisites, and running the setup script. Then, simply describe your video idea to your AI coding assistant, and let OpenMontage handle the rest.

In short, OpenMontage is a game-changer for video production - turn your ideas into stunning videos with ease, and unleash your creativity.

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🚀 Meet chopratejas/headroom: a gem from today's GitHub trending list.

🔗 https://github.com/chopratejas/headroom
📝 Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.
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Headroom is a context compression layer for AI agents that reduces the number of tokens sent to LLMs by 60-95%. This library, proxy, and MCP solution works with various agents, including Claude, Codex, and Copilot, to compress tool outputs, logs, RAG chunks, files, and conversation history. Key features include:
- compress(messages) function in Python or TypeScript
- headroom proxy for zero-code changes
- headroom wrap for agent compatibility
- Cross-agent memory sharing
- Reversible compression with original caching

Technical highlights include:
- ContentRouter for content type detection
- SmartCrusher, CodeCompressor, and Kompress-base for compression
- CacheAligner for prefix stabilization

Audience: Developers and users of AI agents, particularly those working with LLMs.

To get started, simply install Headroom using pip install headroom-ai[all] or npm install headroom-ai, then pick your mode and see the savings.

One-liner takeaway: Headroom reduces the tokens sent to LLMs, resulting in significant cost savings without compromising accuracy, making it a valuable tool for AI agent users.

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🧠 Channel: https://xn--r1a.website/GithubRe

📌 Spotted on GitHub Trending: tursodatabase/turso — let's break it down.

🔗 https://github.com/tursodatabase/turso
📝 Turso is an in-process SQL database, compatible with SQLite.
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Meet Turso Database, an in-process SQL database that's compatible with SQLite. It's designed to provide a seamless experience for users who want a lightweight, easy-to-use database solution.

The key features of Turso Database include SQLite compatibility, concurrent writes using multi-version concurrency control, and change data capture for real-time tracking of database changes. It also supports multi-language bindings for languages like Go, JavaScript, Java, .NET, Python, and Rust.

To get started with Turso Database, you can use the tursodb command-line interface. Simply run tursodb to start the interactive shell, where you can execute SQL statements. You can also build and run the latest development version using cargo run.

Turso Database supports various programming languages, including Rust, JavaScript, Python, Go, .NET, and Java. For example, you can use the following Rust code to connect to a database:

let db = Builder::new_local("sqlite.db").build().await?;
let conn = db.connect()?;
let res = conn.query("SELECT * FROM users", ()).await?;

Or, you can use the following JavaScript code to connect to a database:

import { connect } from '@tursodatabase/database';
const db = await connect('sqlite.db');
const stmt = db.prepare('SELECT * FROM users');
const users = stmt.all();
console.log(users);

Turso Database is designed for users who want a fast, reliable, and easy-to-use database solution. Whether you're a developer, a data scientist, or a student, Turso Database provides a seamless experience for managing your data.

In a nutshell, Turso Database is the perfect choice for anyone looking for a lightweight, SQLite-compatible database solution that's easy to use and provides a seamless experience across multiple programming languages. Give Turso Database a try and experience the power of a fast, reliable, and easy-to-use database solution!

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🧠 Channel: https://xn--r1a.website/GithubRe

🌟 penpot/penpot caught my eye on GitHub Trending today.

🔗 https://github.com/penpot/penpot
📝 Penpot: The open-source design tool for design and code collaboration
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Penpot is an open-source design platform for teams building digital products at scale. Its key strength lies in giving you full ownership of your design infrastructure, with support for self-hosting, open standards like SVG, CSS, HTML, and JSON, and real-time collaboration.

The platform is designed for both designers and developers, with features like Design Tokens, Components, and Variants for scalable and consistent UI. The MCP server enables multi-directional workflows between design and code, and a powerful open API and plugin system makes the workspace programmable.

To get started, you can use Penpot in the browser or deploy it on your own servers. The community is active, with a contributing guide and many resources available, including documentation, tutorials, and dev diaries.

Penpot is licensed under the Mozilla Public License, and is an open-source project by Kaleidos.

Take control of your design infrastructure with Penpot - the ultimate open-source design platform for teams!

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🧠 Channel: https://xn--r1a.website/GithubRe

⚡ ZhuLinsen/daily_stock_analysis is making waves. Here's the full picture.

🔗 https://github.com/ZhuLinsen/daily_stock_analysis
📝 LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs.
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Daily Stock Analysis is an AI-powered stock analysis system that provides users with daily reports on their selected stocks. The system uses machine learning models to analyze market data, news, and social media sentiment to generate buy, sell, or hold recommendations.

Key Features:
The system supports multiple markets, including A-shares, Hong Kong stocks, US stocks, and more. It also provides a web-based interface for users to configure their stock portfolios, view analysis reports, and receive notifications.

Usage:
Users can configure the system to analyze their selected stocks and receive daily reports via various channels, including WeChat, Telegram, and email.

Technical Highlights:
The system uses a range of AI models, including Anspire and AIHubMix, to analyze market data and generate recommendations. It also supports multiple data sources, including TickFlow and AkShare.

Audience:
The system is designed for individual investors and financial professionals who want to stay on top of market trends and make informed investment decisions.

One-liner Takeaway:
Daily Stock Analysis is a powerful tool for investors who want to leverage AI-powered insights to make informed investment decisions and stay ahead of the market.

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🧠 Channel: https://xn--r1a.website/GithubRe

🚀 Meet koala73/worldmonitor: a gem from today's GitHub trending list.

🔗 https://github.com/koala73/worldmonitor
📝 Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
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The World Monitor is a real-time global intelligence dashboard that leverages AI to aggregate news, track geopolitics, and monitor infrastructure. Its key features include 500+ curated news feeds, a dual map engine, and a Country Instability Index (CII). To use, simply git clone the repository, npm install, and npm run dev. The project boasts a tech stack that includes Vanilla TypeScript, Vite, and Tauri 2, with a deployment process that utilizes Vercel Edge Functions and Railway relay. The audience for this project includes researchers, educators, and professionals seeking to stay informed about global events. With its permissive AGPL-3.0-only license, the World Monitor is an excellent choice for those seeking a customizable and self-hosted solution. One-liner takeaway: Stay ahead of the curve with the World Monitor, your one-stop-shop for real-time global intelligence.

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🧠 Channel: https://xn--r1a.website/GithubRe

🚀 Meet bytedance/deer-flow: a gem from today's GitHub trending list.

🔗 https://github.com/bytedance/deer-flow
📝 An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
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DeerFlow is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to do almost anything — powered by extensible skills. Its key features include skills and tools, sub-agents, sandbox and file system, context engineering, and long-term memory.

To get started with DeerFlow, you can clone the repository, run the setup wizard, and choose your LLM provider, web search, and execution preferences. The project uses config.yaml for configuration and supports multiple deployment options, including Docker and local development.

Some technical highlights of DeerFlow include its support for various LLM providers, such as OpenAI and Claude Code, and its integration with InfoQuest, a powerful search and crawling toolset. The project also provides a range of documentation and resources, including a official website with real demos and tutorials.

DeerFlow is designed for developers and researchers who want to build and deploy complex AI systems. Its one-line agent setup feature allows you to easily integrate DeerFlow with other tools and platforms.

In summary, DeerFlow is a powerful and flexible platform for building and deploying AI systems, and its extensible skills and super agent harness make it an ideal choice for developers and researchers who want to push the boundaries of what is possible with AI.
The one-liner takeaway: DeerFlow empowers you to build and deploy AI systems that can do almost anything, with its super agent harness and extensible skills at your fingertips.

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🧠 Channel: https://xn--r1a.website/GithubRe

💡 DeusData/codebase-memory-mcp just hit the trending charts — here's why it matters.

🔗 https://github.com/DeusData/codebase-memory-mcp
📝 High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.
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Meet codebase-memory-mcp, the fastest and most efficient code intelligence engine for AI coding agents. This powerful tool indexes an average repository in milliseconds and answers structural queries in under 1ms. It supports 158 languages and ships as a single static binary for macOS, Linux, and Windows, making it easy to download, install, and use.

The key features of codebase-memory-mcp include:

* Extreme indexing speed: indexes the Linux kernel in 3 minutes
* Plug and play: single static binary with no dependencies or API keys required
* High-quality parsing: uses tree-sitter AST analysis and hybrid LSP semantic type resolution
* Built-in graph visualization: 3D interactive UI for exploring the knowledge graph
* 14 MCP tools: search, trace, architecture, impact analysis, and more

To get started, simply run the install.sh script (or install.ps1 on Windows) and restart your coding agent. You can also manually install the binary and configure it to work with your preferred coding agent.

With codebase-memory-mcp, you can enjoy features like semantic search, structural search, and cross-service linking, making it an essential tool for any developer. So why wait? Try codebase-memory-mcp today and experience the power of code intelligence like never before - it's like having a superpower for your code.

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🧠 Channel: https://xn--r1a.website/GithubRe

🚀 Meet mukul975/Anthropic-Cybersecurity-Skills: a gem from today's GitHub trending list.

🔗 https://github.com/mukul975/Anthropic-Cybersecurity-Skills
📝 754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0
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Anthropic Cybersecurity Skills is an open-source library of 754 production-grade cybersecurity skills, covering 26 security domains and mapped to six industry frameworks, including MITRE ATT&CK, NIST CSF, and MITRE F3. This library enables AI agents to possess the security skills of a senior analyst, guiding them through expert-level security investigations in seconds.

The library is designed to be AI-native, with YAML frontmatter for sub-second discovery and structured Markdown for step-by-step execution. It includes skills such as cloud security, threat hunting, and web application security, making it a comprehensive resource for cybersecurity professionals.

To get started, users can clone the repository or use npx skills add mukul975/Anthropic-Cybersecurity-Skills. The library is compatible with various platforms, including Claude Code, GitHub Copilot, and OpenAI Codex CLI.

The project aims to bridge the cybersecurity workforce gap by providing AI agents with structured domain knowledge. It is an independent, community-created project, not affiliated with Anthropic PBC.

Key takeaway: Give your AI agent the security skills of a senior analyst with Anthropic Cybersecurity Skills, and revolutionize your security investigations.

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🧠 Channel: https://xn--r1a.website/GithubRe