🌟 smicallef/spiderfoot caught my eye on GitHub Trending today.

🔗 https://github.com/smicallef/spiderfoot
📝 SpiderFoot automates OSINT for threat intelligence and mapping your attack surface.
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SpiderFoot is an open-source intelligence (OSINT) automation tool that integrates with various data sources and utilizes multiple methods for data analysis. It offers a web-based interface and command-line functionality, making it easy to navigate and use. With over 200 modules, SpiderFoot can perform tasks such as host enumeration, email address extraction, and threat intelligence queries. The tool is written in Python 3 and is MIT-licensed.

Key Features:
- Web-based UI or CLI
- Over 200 modules
- Python 3.7+ support
- YAML-configurable correlation engine
- CSV/JSON/GEXF export
- API key export/import

Technical Highlights:
- SQLite back-end for custom querying
- TOR integration for dark web searching
- Dockerfile for Docker-based deployments

Audience:
- Security professionals
- Researchers
- Penetration testers

Takeaway: With its extensive module library and customizable correlation engine, SpiderFoot is the ultimate OSINT tool for anyone looking to streamline their intelligence gathering process.

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🧠 Channel: https://t.iss.one/GithubRe

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

🔗 https://github.com/localsend/localsend
📝 An open-source cross-platform alternative to AirDrop
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LocalSend is a free, open-source app that lets you securely share files and messages with nearby devices over your local network without needing an internet connection. The app uses a REST API and HTTPS encryption for secure communication.

Key features include cross-platform compatibility, secure communication protocol, and no requirement for an internet connection or third-party servers.

To use LocalSend, simply download and install the app on your device, and follow the setup instructions to configure your firewall and router settings.

From a technical perspective, LocalSend uses a secure communication protocol that generates a TLS/SSL certificate on the fly on each device, ensuring maximum security. The app is built using Flutter and Rust, and the code is available on GitHub for contributors to review and modify.

The app is suitable for anyone who wants to securely share files and messages with nearby devices, including individuals, businesses, and organizations.

In short, LocalSend is a fast, reliable, and secure solution for local file and message sharing - share files and messages with ease, without the need for internet.

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🧠 Channel: https://t.iss.one/GithubRe

🔍 Deep-diving into Lightricks/LTX-2 — fresh off the trending list.

🔗 https://github.com/Lightricks/LTX-2
📝 Official Python inference and LoRA trainer package for the LTX-2 audio–video generative model.
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The Lightricks/LTX-2 GitHub repository presents a groundbreaking DiT-based audio-video foundation model, LTX-2, which integrates all core capabilities of modern video generation into one model. This includes synchronized audio and video, high fidelity, multiple performance modes, production-ready outputs, API access, and open access.

To get started, users can clone the repository, install dependencies, and download the required models using the Hugging Face CLI. The repository provides a Quick Start guide, which demonstrates how to generate video using the distilled model and pipeline.

LTX-2 features various models, including the transformer, text encoder, video VAE, audio VAE, and spatial upscaler. Each model has different versions, allowing users to choose the best fit for their specific needs.

The repository also offers multiple pipelines, such as DistilledPipeline, DFRPipeline, and TI2VidTwoStagesPipeline, each with its unique features and use cases.

LTX-2 is suitable for a wide range of users, from researchers and developers to content creators and artists.

In a nutshell, LTX-2 revolutionizes video generation, and its capabilities are a game-changer: unleash your creativity with LTX-2, where AI meets art.

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🧠 Channel: https://t.iss.one/GithubRe

🔥 embabel/embabel-agent is trending — and it deserves your attention.

🔗 https://github.com/embabel/embabel-agent
📝 Agent framework for the JVM. Pronounced Em-BAY-bel /ɛmˈbeɪbəl/
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Embabel Agent Framework is a Java-based framework for building intelligent agents that can seamlessly mix large language model (LLM) interactions with code and domain models. It's designed to support sophisticated planning, extensibility, and reuse, making it an ideal choice for developers who want to create complex, dynamic systems.

The framework is built on top of the JVM, leveraging the strengths of Spring and Kotlin, and provides a natural usage model for Java developers. It's highly extensible, allowing developers to add new domain objects, actions, goals, and conditions without modifying existing code.

Key features include:

- Actions: Steps an agent takes
- Goals: What an agent is trying to achieve
- Conditions: Conditions to assess before executing an action or determining that a goal has been achieved
- Domain model: Objects underpinning the flow and informing actions, goals, and conditions

Technical highlights include:

- Sophisticated planning using Goal Oriented Action Planning (GOAP) or Utility AI
- Strong typing and object-oriented benefits
- Platform abstraction for clean separation between programming model and platform internals
- Designed for LLM mixing and cost-effective solution

Audience: Embabel Agent Framework is ideal for developers who want to create complex, dynamic systems that leverage the power of LLMs and agentic programming.

Get started with Embabel in under 5 minutes using the Java or Kotlin template, and explore the Embabel Agent Examples Repository for tutorials and examples.

Embabel Agent Framework: where intelligence meets adaptability.

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🧠 Channel: https://t.iss.one/GithubRe

⚡ cactus-compute/needle is making waves. Here's the full picture.

🔗 https://github.com/cactus-compute/needle
📝 14MB foundation model for tiny devices; phones, wearables, smart home, and robots.
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Needle 2 is a compact, open-source model for tool calling, device use, and structured extraction. It's a single 14MB binary that runs in approximately 28MB of RAM. Key features include a self-contained engine, simple contract for tool calls, and confidence-gated responses.

Needle 2 is built on the Simple Attention Network architecture, which includes a Hadamard MLP, GQA attention, and engram key-value memory. The model is compressed to CQ2-bit with Cactus Quants and can be used for a variety of tasks, including tool calling, data extraction, and more.

To use Needle 2, you can install the cactus-needle Python package and describe your tools using a simple contract. The model can then be used to call tools, extract structured data, and more.

Audience: This model is suitable for developers and researchers looking for a compact, efficient, and easy-to-use model for tool calling and data extraction tasks.

One-liner takeaway: Needle 2 is a powerful, compact model that makes it easy to build tool calling and data extraction applications with confidence-gated responses.

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🧠 Channel: https://t.iss.one/GithubRe

🚀 21-Day CCNA & CCNP Sprint – Aug 17 to Sep 6
🤝 Peer Group – Share Insights | Exchange Knowledge | Support Each Other

No more studying alone. Join a community of CCNA/CCNP candidates, learn together, and win prizes.
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① DM admin: "I'M IN + cert name"
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2️⃣ Explain it in your own words
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