🎯 FareedKhan-dev/train-llm-from-scratch landed on trending. Worth a proper look.

🔗 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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The Github repository "FareedKhan-dev/train-llm-from-scratch" is a project that allows users to train their own language model from scratch using a single GPU. The project is based on the paper "Attention is All You Need" and uses PyTorch to implement a transformer model. The repository provides a README file with detailed instructions on how to use the project, including how to download and preprocess the training data, and how to train the model.

The project uses the Pile dataset, which is a large-scale dataset for training language models, and provides a script to download and preprocess the data. The code is organized into several directories, including src/, config/, data_loader/, and scripts/, each containing different components of the project.

The project is suitable for users who have a basic understanding of object-oriented programming, neural networks, and PyTorch. The repository provides a comparison of different GPUs and their capabilities for training language models, allowing users to choose the best option for their needs.

To get started, users can clone the repository, install the required dependencies, and modify the transformer architecture and training configurations as needed. The project provides several scripts to download and preprocess the data, train the model, and generate text using the trained model.

One-liner takeaway: Train your own language model from scratch with this open-source project and unlock the power of AI for your specific needs.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet stefan-jansen/machine-learning-for-trading: a gem from today's GitHub trending list.

🔗 https://github.com/stefan-jansen/machine-learning-for-trading
📝 Code for Machine Learning for Algorithmic Trading, 2nd edition.
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The stefan-jansen/machine-learning-for-trading GitHub repository accompanies the book "Machine Learning for Algorithmic Trading", a comprehensive guide to applying machine learning in trading. The repo contains over 150 notebooks that demonstrate how to extract signals from various data sources, train and tune models, and design, backtest, and evaluate trading strategies.

The repository covers key aspects of machine learning for trading, including data sourcing, financial feature engineering, and portfolio management. It also explores the use of deep learning models, such as CNN and RNN, with market and alternative data.

To get started, readers can review the notebooks, which provide numerous examples of how to work with and extract signals from market, fundamental, and alternative text and image data. The notebooks also demonstrate how to train and tune models that predict returns for different asset classes and investment horizons.

The target audience for this repository includes traders, data scientists, and finance professionals interested in leveraging machine learning for trading strategies. The repository is a valuable resource for anyone looking to learn about machine learning for trading, with its comprehensive coverage of key concepts, algorithms, and use cases.

In summary, the stefan-jansen/machine-learning-for-trading repository is a must-visit destination for anyone interested in machine learning for trading, offering a wealth of information, examples, and resources to help you get started. Machine learning can be your new trading edge!

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🧠 Channel: https://t.me/GithubRe

🎯 dmtrKovalenko/fff landed on trending. Worth a proper look.

🔗 https://github.com/dmtrKovalenko/fff
📝 The fastest and the most accurate file search toolkit for AI agents, Neovim, Rust, C, and NodeJS
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The dmtrKovalenko/fff GitHub repository offers a blazingly fast file search toolkit designed for both humans and AI agents. This toolkit boasts a range of key features, including typo-resistant path and content search, frecency-ranked file access, a background watcher, and a lightweight in-memory content index.

To get started with fff, users can choose from various installation methods, including a one-line install for Linux/macOS and Windows. The repository provides detailed instructions for each installation method, ensuring a seamless setup process.

From a technical standpoint, fff is built with performance in mind. It outperforms traditional CLIs like ripgrep and fzf in long-running processes that involve multiple searches. The toolkit also includes a range of technical highlights, such as smart-case search with auto-fuzzy fallback and git-aware annotations.

The fff toolkit is designed for a broad audience, including developers, AI researchers, and anyone who needs fast and efficient file search capabilities. Whether you're working with large codebases or simply need to find files quickly, fff has the tools and features to meet your needs.

In short, fff is an ultra-fast file search toolkit that's a game-changer for anyone who needs to find files quickly and efficiently - and that's a pretty sweet deal.

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🧠 Channel: https://t.me/GithubRe

🔥 codecrafters-io/build-your-own-x is trending — and it deserves your attention.

🔗 https://github.com/codecrafters-io/build-your-own-x
📝 Master programming by recreating your favorite technologies from scratch.
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The codecrafters-io/build-your-own-x GitHub repository is a collection of step-by-step guides for building various technologies from scratch. It's based on the idea that what you cannot create, you do not understand, a quote from Richard Feynman. The repository covers a wide range of topics, including 3D rendering, AI models, blockchains, bots, databases, and more.

These guides are written in various programming languages, such as C++, Java, Python, and JavaScript, making it accessible to developers with different skill sets. The repository is perfect for junior developers looking to improve their skills, students seeking to learn by doing, and experienced developers who want to explore new areas of interest.

Some technical highlights include building a 3D renderer using C++ and JavaScript, creating an AI model with Python, and developing a blockchain using JavaScript and Rust.

Overall, this repository provides a unique opportunity for developers to learn by building real-world projects. So, get ready to code your way to a deeper understanding of various technologies - build something from scratch and you'll never forget how it works.

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🧠 Channel: https://t.me/GithubRe

Don't miss this opportunity!

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

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

🔗 https://github.com/microsoft/markitdown
📝 Python tool for converting files and office documents to Markdown.
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Introducing MarkItDown, a lightweight Python utility for converting various file formats to Markdown, designed for use with Large Language Models (LLMs) and text analysis pipelines. Key features include support for multiple file formats, such as PDF, PowerPoint, Word, Excel, images, audio, HTML, and more.

To install MarkItDown, use pip: pip install 'markitdown[all]'. For usage, you can use the command-line interface: markitdown path-to-file.pdf > document.md or use the Python API:

from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("test.xlsx")
print(result.text_content)

Technical highlights include optional dependencies for specific file formats, plugins for additional functionality, and support for Azure Content Understanding and Document Intelligence. The library is ideal for developers and data scientists working with LLMs and text analysis pipelines.

In summary, MarkItDown is a powerful and flexible tool for converting files to Markdown, and its ease of use and customization options make it a great choice for anyone working with text data - give MarkItDown a try and start converting your files to Markdown today!

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🧠 Channel: https://t.me/GithubRe

🔥 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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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 lives on your server. The web UI offers full parity with the CLI experience, allowing you to access all the features of the Hermes Agent from a browser.

The web UI features a three-panel layout, with a left sidebar for sessions and navigation, a center panel for chat, and a right panel for workspace file browsing. It also includes a composer footer with model, profile, and workspace controls, as well as a circular context ring that shows token usage at a glance.

The web UI is built using Python and vanilla JavaScript, with no build step, framework, or bundler required. It provides a secure and convenient way to access your Hermes Agent from anywhere, using a single command to start and a single command to SSH tunnel for access.

Key features include chat and agent functionality, session management, workspace file browsing, and security features like password configuration. The web UI also supports multiple messaging platforms, including Telegram, Discord, and Slack.

The target audience for the Hermes Web UI includes developers, researchers, and anyone who wants to interact with the Hermes Agent from a web interface.

Overall, the Hermes Web UI provides a convenient and secure way to interact with the Hermes Agent, making it an excellent tool for anyone who wants to harness the power of autonomous agents.

Takeaway: The Hermes Web UI is a game-changer for anyone who wants to interact with autonomous agents from a web interface, offering a secure, convenient, and feature-rich experience.

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🧠 Channel: https://t.me/GithubRe

🚀 Meet supermemoryai/supermemory: a gem from today's GitHub trending list.

🔗 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 an AI memory and context engine designed to give your AI tools a brain that remembers and learns from conversations. With state-of-the-art performance across major benchmarks, it extracts facts, builds user profiles, and delivers the right context at the right time.

The key features of Supermemory include:
- Memory: Automatically learns from conversations and extracts facts.
- User Profiles: Maintains user context, including stable facts and recent activity.
- Hybrid Search: Combines knowledge base documents and personalized context in a single query.
- Connectors: Auto-syncs with Google Drive, Gmail, Notion, and other services.

To use Supermemory, you can either build your own personal brain with the app or add memory to your AI products with a single API. The app allows you to give your AI persistent memory, while the API provides the entire context stack for AI agents and apps.

Supermemory has drop-in wrappers for major AI frameworks and is compatible with various clients, including Claude Desktop and VS Code. It also supports hybrid search modes, user profiles, and connectors for auto-syncing external data.

In terms of technical highlights, Supermemory has achieved state-of-the-art results in benchmarks such as LongMemEval, LoCoMo, and ConvoMem. The MemoryBench framework allows for standardized and reproducible benchmarks of memory providers.

Overall, Supermemory is suitable for anyone looking to enhance their AI tools with a powerful memory and context engine, whether you're a developer building AI products or an individual looking to give your AI a personal brain.

With Supermemory, your AI will never forget again - and that's a game-changer!

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🧠 Channel: https://t.me/GithubRe