⚡ D4Vinci/Scrapling is making waves. Here's the full picture.

🔗 https://github.com/D4Vinci/Scrapling
📝 🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!
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Scrapling is an adaptive web scraping framework that simplifies the process of extracting data from websites. Its key features include selection methods, fetchers, and spiders that enable users to scale up to concurrent, multi-session crawls with pause/resume and automatic proxy rotation. The framework is designed to handle website changes and anti-bot systems, ensuring that your data extraction efforts are not disrupted.

To use Scrapling, you can start by installing the library and then using its various components, such as the Fetcher and Spider classes, to fetch and parse web pages. For example:

from scrapling.fetchers import Fetcher
p = Fetcher.fetch('https://example.com', headless=True, network_idle=True)
products = p.css('.product', auto_save=True)

Scrapling is suitable for both beginners and experienced users, providing an easy-to-use interface for simple scraping tasks and advanced features for large-scale crawls. Whether you're a web scraper, data scientist, or just someone looking to extract data from websites, Scrapling has something for everyone. With its blazing fast performance, real-time stats, and streaming capabilities, Scrapling is the perfect tool for anyone looking to extract data from the web.
Scrapling: where web scraping meets simplicity - try it and scrape like a pro!

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🔥 nesquena/hermes-webui is trending — and it deserves your attention.

🔗 https://github.com/nesquena/hermes-webui
📝 Hermes WebUI: The best way to use Hermes Agent from the web or from your phone!
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The Hermes Web UI is a lightweight, dark-themed web app that provides a convenient interface for interacting with the Hermes Agent, a sophisticated autonomous agent that runs on your server. This web app offers full parity with the CLI experience, allowing you to perform all actions from a terminal, but with the convenience of a web interface.

Key features of the Hermes Web UI include a three-panel layout for easy navigation, a circular context ring to show token usage at a glance, and model, profile, and workspace controls that are always visible while composing. The web app also supports light and dark modes, as well as customizable settings and password configuration.

To use the Hermes Web UI, you can run the bootstrap.py script, which will detect the Hermes Agent and install any missing dependencies. You can also use the start.sh script to launch the web app. The web app is designed to be self-hosted and provider-agnostic, allowing you to use it with a variety of messaging platforms and AI providers.

In terms of technical highlights, the Hermes Web UI uses vanilla JavaScript and Python to provide a seamless and efficient user experience. The web app is also designed to be secure, with features such as SSH tunneling for secure access.

The Hermes Web UI is suitable for a wide range of users, from developers who want to interact with the Hermes Agent from a web interface, to non-technical users who want a simple and intuitive way to use the agent. Overall, the Hermes Web UI is a powerful and flexible tool that provides a convenient and user-friendly interface for interacting with the Hermes Agent.

The one-liner takeaway: Experience the power of the Hermes Agent from the convenience of a web interface with the Hermes Web UI.

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🧠 Channel: https://t.me/GithubRe

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Once you register, you will receive future courses for free.

🚀 Meet EveryInc/compound-engineering-plugin: a gem from today's GitHub trending list.

🔗 https://github.com/EveryInc/compound-engineering-plugin
📝 Official Compound Engineering plugin for Claude Code, Codex, Cursor, and more
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Compound Engineering Plugin is a game-changer for developers, making each unit of engineering work easier than the last. The plugin inverts traditional development by emphasizing planning and review, with 80% of the work done before writing code. It includes a range of skills and agents to streamline the development process, from brainstorming and planning to execution and review.

Key features of the plugin include /ce-brainstorm, /ce-plan, /ce-work, and /ce-code-review, which work together to create a seamless development cycle. The plugin also includes a /ce-compound feature, which allows developers to document their learnings and make future work easier.

The plugin is designed to be easy to use and can be installed through various platforms, including Claude Code, Codex, and GitHub Copilot. It's suitable for a wide range of developers, from individuals to large teams.

One of the most significant technical highlights of the plugin is its ability to compound knowledge, allowing developers to build on previous work and make future changes easier. The plugin also includes a range of pre-built agents that can be customized to meet the needs of individual developers or teams.

Overall, the Compound Engineering Plugin is a powerful tool that can help developers work more efficiently and effectively. With its emphasis on planning, review, and compounding knowledge, it's an essential tool for anyone looking to improve their development workflow. Streamline your development process and make each unit of work easier than the last – that's the power of Compound Engineering!

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🔍 Deep-diving into github/docs — fresh off the trending list.

🔗 https://github.com/github/docs
📝 The open-source repo for docs.github.com
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The github/docs repository is home to GitHub's open-source documentation, where anyone can contribute. This repo is for public contributions, while GitHub employees use a separate private repository, github/docs-internal, which syncs frequently with the public one.

Key features include content files (.md files in /content and select /data sections), while infrastructure files, workflows, and site-building code are not open for external modification. To get started, contributors can refer to resources like Finding ways to contribute to open source on GitHub and Set up Git.

The project is dual-licensed under Creative Commons Attribution 4.0 and MIT License. This repository is for developers, writers, and anyone interested in contributing to GitHub's documentation.

Contribute to github/docs and help shape the future of GitHub's documentation - every contribution counts, no matter how small.

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🔍 Deep-diving into OpenBMB/VoxCPM — fresh off the trending list.

🔗 https://github.com/OpenBMB/VoxCPM
📝 VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning
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VoxCPM2 is a tokenizer-free Text-to-Speech system that generates continuous speech representations via an end-to-end diffusion autoregressive architecture. This 2B parameter model supports 30 languages, Voice Design, Controllable Voice Cloning, and 48kHz studio-quality audio output.

Key features include:
- 30-Language Multilingual: synthesize text in any supported language
- Voice Design: create a new voice from a natural-language description
- Controllable Cloning: clone any voice with optional style guidance
- Ultimate Cloning: reproduce every vocal nuance from a reference audio and transcript
- 48kHz High-Quality Audio: output studio-quality audio
- Context-Aware Synthesis: automatic prosody and expressiveness inference
- Real-Time Streaming: low-latency streaming with RTF as low as ~0.13

To get started, you can use the Python API or CLI Usage for tasks like text-to-speech, voice design, and controllable voice cloning. The VoxCPM class provides methods like generate for text-to-speech synthesis and generate_streaming for real-time streaming.

For production deployment, you can use Nano-vLLM-VoxCPM for high-throughput serving or vLLM-Omni for multi-tenant deployments with native VoxCPM2 support.

Takeaway: VoxCPM2 is a powerful, open-source TTS system that offers unparalleled flexibility and quality for multilingual speech synthesis, voice design, and voice cloning, making it an ideal choice for production environments.

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📌 Spotted on GitHub Trending: revfactory/harness — let's break it down.

🔗 https://github.com/revfactory/harness
📝 A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.
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Harness is a team-architecture factory for Claude Code, designed to turn domain descriptions into coordinated agent teams with specialized skills. With six pre-defined team-architecture patterns, it's perfect for complex tasks.

Key features include agent team design, skill generation, and orchestration. Usage is straightforward: simply trigger the plugin with prompts like "Build a harness for this project" and it will generate agent definitions and skills tailored to your domain.

Technical highlights include support for Progressive Disclosure and inter-agent data passing. The plugin is suitable for a wide range of users, from developers to business strategists.

One-liner takeaway: With Harness, you can boost output quality by 60% and achieve a 100% win rate across 15 software engineering tasks!

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🧠 Channel: https://t.me/GithubRe

🔥 FareedKhan-dev/train-llm-from-scratch is trending — and it deserves your attention.

🔗 https://github.com/FareedKhan-dev/train-llm-from-scratch
📝 A straightforward method for training your LLM, from downloading data to generating text.
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Introduction to FareedKhan-dev/train-llm-from-scratch

The FareedKhan-dev/train-llm-from-scratch GitHub repository provides a comprehensive guide to training a large language model (LLM) from scratch using PyTorch. The project is based on the paper "Attention is All You Need" and allows users to train their own billion or million parameter LLM using a single GPU.

Key Features and Usage

The repository includes the following key features:
- Implementation of a transformer model from scratch using PyTorch
- Scripts for downloading and preprocessing the dataset
- Configurable training parameters
- Support for training models with different architectures

To use the repository, simply clone it, install the required dependencies, and run the provided scripts to download and preprocess the data. You can then train the model using the train_transformer.py script.

Technical Highlights

The project includes the following technical highlights:
- Implementation of a transformer model with attention mechanisms
- Use of PyTorch for building and training the model
- Support for configurable training parameters, such as vocabulary size and transformer configuration

Audience

The repository is targeted towards developers and researchers interested in natural language processing and large language models. It provides a comprehensive guide to training an LLM from scratch and can be used as a starting point for further research and development.

Takeaway

Train your own large language model from scratch with FareedKhan-dev/train-llm-from-scratch and unlock the power of natural language processing with a single GPU.

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🧠 Channel: https://t.me/GithubRe

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

🔗 https://github.com/supermemoryai/supermemory
📝 Memory engine and app that is extremely fast, scalable. The Memory API for the AI era.
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Supermemory is a state-of-the-art memory and context engine for AI, designed to give your AI a brain that can learn, remember, and recall information across conversations. With key features like automatic fact extraction, user profiling, hybrid search, and connectors for Google Drive, Gmail, and more, Supermemory is a powerful tool for building AI applications.

To use Supermemory, you can either use the app and browser extension for personal use or integrate the API into your AI products. The API provides a single interface for memory, RAG, user profiles, and connectors, making it easy to add memory and context to your agents and apps.

Some of the technical highlights of Supermemory include its ability to extract memories, build user profiles, and return relevant context without requiring embedding pipelines, vector DB config, or chunking strategies. It also supports framework integrations with popular AI frameworks like Vercel AI SDK, LangChain, and OpenAI Agents SDK.

Supermemory is designed for anyone building AI applications, from developers and researchers to companies and individuals looking to create more intelligent and context-aware AI systems.

In summary, Supermemory is a game-changer for AI applications, providing a powerful and easy-to-use memory and context engine that can help you build more intelligent and human-like AI systems - give your AI a brain that never forgets.

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🧠 Channel: https://t.me/GithubRe