🔍 Deep-diving into unslothai/unsloth — fresh off the trending list.

🔗 https://github.com/unslothai/unsloth
📝 Local UI to run and train LLMs and diffusion models, including Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4, FLUX and more.
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Unsloth is a cutting-edge desktop app that allows users to run and train AI models locally, with support for various types of models. It features a user-friendly interface, native support for CPU, NVIDIA, AMD, and Intel hardware, and multi-GPU setups. Users can download the app for Windows, macOS, or Linux, and start using it right away.

The app offers a range of features, including model training, fine-tuning, and deployment, as well as private and unlimited web search, deep research, and RAG. It also supports image and video diffusion, audio models, and reinforcement learning.

Unsloth is designed for developers, researchers, and AI enthusiasts who want to work with AI models locally, without relying on cloud services. The app is free to use and offers a range of community resources, including documentation, tutorials, and forums.

To get started with Unsloth, users can simply download the app and follow the installation instructions. The app also offers a web-based interface and a command-line interface for more advanced users.

One of the key benefits of Unsloth is its ability to run AI models locally, which provides a high level of security and control for users. The app also offers remote access capabilities, allowing users to access their models from anywhere.

Overall, Unsloth is a powerful and flexible tool for working with AI models locally, and it offers a range of features and benefits that make it an attractive choice for developers, researchers, and AI enthusiasts.

Takeaway: Unsloth is the ultimate tool for running and training AI models locally, offering a unique combination of power, flexibility, and security.

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🧠 Channel: https://t.iss.one/GithubRe

⚡ macro-inc/macro is making waves. Here's the full picture.

🔗 https://github.com/macro-inc/macro
📝 Macro is a unified workspace for teams: email, chat, docs, tasks, agents, calls, and CRM — @-linked together with shared AI memory.
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Hey there, let's check out the Macro GitHub repo. Macro is an all-in-one workspace that combines email, messages, docs, tasks, agents, and CRM into a single, fast interface with shared team-level memory. It's designed to be a single operating system for your team, eliminating the need for multiple tools.

The key features of Macro include:
- Modular blocks that work together like Lego, allowing for customization and extensibility
- Bidirectional graph for cross-references between different surfaces, such as docs and tasks
- Real-time collaborative editing with CRDTs, making it feel like you're editing on the same computer

In terms of technical highlights, Macro is built with SolidJS and Rust for speed and reliability. The CRDT collaboration system allows for seamless editing and conflict resolution, even with multiple agents operating simultaneously.

Macro is suitable for small companies or teams within larger companies, looking for an all-in-one workspace solution. The ideal audience includes teams seeking to streamline their workflow, reduce tool clutter, and improve collaboration.

One-liner takeaway: Simplify your team's workflow with Macro, the all-in-one workspace that's about to revolutionize how you get things done!

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🧠 Channel: https://t.iss.one/GithubRe

📌 Spotted on GitHub Trending: megadose/holehe — let's break it down.

🔗 https://github.com/megadose/holehe
📝 holehe allows you to check if the mail is used on different sites like twitter, instagram and will retrieve information on sites with the forgotten password function.
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Holehe is an Open-Source Intelligence (OSINT) tool that efficiently finds registered accounts from emails. It checks if an email is attached to an account on over 120 sites like Twitter, Instagram, and Imgur.

Key features include:
- retrieving information using the forgotten password function
- not alerting the target email
- running on Python 3

To use Holehe, you can install it via pip3 install holehe, git clone, or docker.

holehe [email protected] is a simple command to run the tool. For more complex usage, you can integrate it into your Python applications.

The output is a dictionary with information like rate limits, account existence, and sometimes partially obfuscated recovery emails and phone numbers.

This tool is perfect for security researchers, OSINT investigators, and anyone interested in email reconnaissance.

One-liner takeaway: Holehe is a must-have OSINT tool that helps you uncover hidden email accounts without alerting the target.

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🧠 Channel: https://t.iss.one/GithubRe

🔥 smicallef/spiderfoot is trending — and it deserves your attention.

🔗 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 numerous data sources and utilizes various methods for data analysis. It offers a web-based interface and command-line functionality, all written in Python 3 and MIT-licensed.

Key features include over 200 modules, a YAML-configurable correlation engine, and support for CSV/JSON/GEXF export. It also has TOR integration for dark web searches and a Dockerfile for Docker-based deployments.

This tool can be used for both offensive and defensive purposes, such as reconnaissance or gathering information about exposed internet assets. It's highly configurable, fully documented, and has a visual interface.

The target audience includes security professionals, developers, and anyone interested in OSINT and data analysis.

Overall, SpiderFoot is a powerful tool for automating OSINT tasks and analyzing large amounts of data. With its extensive features and customization options, it's an essential tool for anyone looking to streamline their OSINT workflow: SpiderFoot is your ultimate OSINT sidekick!

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🧠 Channel: https://t.iss.one/GithubRe

⚡ NVIDIA-NeMo/Switchyard is making waves. Here's the full picture.

🔗 https://github.com/NVIDIA-NeMo/Switchyard
📝 No description.
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Introducing Switchyard, a Rust proxy and library designed for Large Language Model (LLM) traffic management. It routes requests across multiple providers, translates between OpenAI and Anthropic APIs, records operational metrics, and provides typed, composable routing algorithms.

Main features include protocol translation, multi-backend routing, and operational metrics. You can use Switchyard as a launcher, server, or library, making it a versatile tool for managing LLM traffic.

To get started, you can choose the launcher path to run coding agents like Claude Code or Codex through Switchyard, the server path to run Switchyard as a standalone proxy, or the library path to embed routing algorithms in your own Rust application.

Technical highlights include support for various routing strategies, such as LLM classifier, stage router, escalation router, and random routing. Switchyard also provides a simple architecture for routing and translation.

Switchyard is designed for developers and researchers working with LLMs, especially those who need to manage traffic across multiple models and providers.

In a nutshell, Switchyard is your one-stop solution for LLM traffic management - route, translate, and optimize your way to AI efficiency.

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🧠 Channel: https://t.iss.one/GithubRe