Github Top Repositories

🔥 I send Gold alerts. You copy. No experience. No complex charts. 10 minutes/day from your phone. Join Tania’s Free Academy 👇 #ad 📢 InsideAd

🚀 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. ────────────────────────────── The stefan-jansen/machine-learning-for-trading GitHub repository is a comprehensive resource for learning about machine learning in trading. It's based on a book that covers a broad range of machine learning techniques, from linear regression to deep reinforcement learning, and demonstrates how to build, backtest, and evaluate a trading strategy driven by model predictions. The repository contains over 150 notebooks that put the concepts, algorithms, and use cases discussed in the book into action. These notebooks provide numerous examples that show how to work with and extract signals from market, fundamental, and alternative text and image data, how to train and tune models that predict returns for different asset classes and investment horizons, and how to design, backtest, and evaluate trading strategies. The ML4T workflow is a key concept in the repository, which starts with generating ideas for a well-defined investment universe, collecting relevant data, and extracting informative features. It also involves designing, tuning, and evaluating machine learning models suited to the predictive task. The repository is suitable for traders, data scientists, and machine learning enthusiasts who want to learn about machine learning in trading. The code examples rely on a wide range of Python libraries from the data science and finance domains, including pandas, TensorFlow, and zipline. To get started, users can install the required libraries and run the notebooks, which are usually in an executed state and often contain additional information not included due to space constraints. The repository also provides detailed instructions on setting up and using a Docker image to run the notebooks. In summary, the stefan-jansen/machine-learning-for-trading repository is a valuable resource for anyone who wants to learn about machine learning in trading and start building their own trading strategies. Machine learning can be a powerful tool for traders, and this repository provides the perfect starting point for exploring its potential. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into FareedKhan-dev/train-llm-from-scratch — fresh off the trending list. 🔗 https://github.com/FareedKhan-dev/train-llm-from-scratch 📝 A straightforward method for training your LLM, from downloading data to generating text. ────────────────────────────── The GitHub repository "FareedKhan-dev/train-llm-from-scratch" is designed to train a large language model (LLM) from scratch using PyTorch, based on the paper "Attention is All You Need". The repository provides scripts to train a 13 million or billion parameter LLM using a single GPU. The train-llm-from-scratch repository is structured into several directories, including src for model definitions, config for default configurations, data_loader for data loading functions, and scripts for training, data preprocessing, and text generation. To use the repository, you need to clone it, install the required dependencies, and download the training data using the provided scripts. The training data is from the Pile dataset, a diverse and large-scale dataset for training language models. You can modify the transformer architecture and training configurations according to your needs. The repository also provides a step-by-step code explanation to help you understand the implementation. The key technical highlights include the implementation of transformer blocks, multi-head attention, and multi-layer perceptron (MLP) modules. The repository is suitable for researchers and developers interested in natural language processing and large language models. One-liner takeaway: Train your own billion-parameter LLM from scratch with this repository, and unlock the power of large language models for your NLP tasks. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🔥 OpenBMB/VoxCPM is trending — and it deserves your attention. 🔗 https://github.com/OpenBMB/VoxCPM 📝 VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning ────────────────────────────── The VoxCPM2 project is a cutting-edge, tokenizer-free Text-to-Speech system that generates high-quality, multilingual speech using a diffusion autoregressive architecture. This innovative approach allows for ultra-realistic speech synthesis, voice design, and controllable voice cloning. With support for 30 languages and 48kHz studio-quality audio output, VoxCPM2 is a powerful tool for a wide range of applications. To get started, you can install VoxCPM2 using pip install voxcpm and then use the Python API or CLI to generate speech. For example, you can use the following Python code to generate speech:

from voxcpm import VoxCPM
import soundfile as sf

model = VoxCPM.from_pretrained(
  "openbmb/VoxCPM2",
  load_denoiser=False,
)

wav = model.generate(
    text="VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("demo.wav", wav, model.tts_model.sample_rate)

The project is fully open-source and commercial-ready, with weights and code released under the Apache-2.0 license. Whether you're a developer, researcher, or enthusiast, VoxCPM2 is an exciting project that's worth exploring. With its impressive features and ease of use, VoxCPM2 is set to revolutionize the world of speech synthesis: Experience the future of speech synthesis with VoxCPM2. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ can1357/oh-my-pi is making waves. Here's the full picture. 🔗 https://github.com/can1357/oh-my-pi 📝 ⌥ AI Coding agent for the terminal — hash-anchored edits, optimized tool harness, LSP, Python, browser, subagents, and more ────────────────────────────── Meet oh-my-pi, a powerful coding agent designed to streamline your development workflow. At its core, oh-my-pi is a capable agent surface that ships with 40+ providers, 32 built-in tools, and 13 LSP operations, making it an all-in-one solution for coding needs. To get started, you can install oh-my-pi using curl -fsSL https://omp.sh/install | sh on macOS or Linux, or bun install -g @oh-my-pi/pi-coding-agent with Bun. On Windows, use irm https://omp.sh/install.ps1 | iex in PowerShell. Oh-my-pi's key features include code execution with tool-calling, LSP wired into every write, and a real debugger. It also offers time-traveling stream rules, first-class subagents, and native support on Windows. What sets oh-my-pi apart is its seamless integration with existing tools and workflows. It reads PDFs on arxiv, inherits config from other tools, and supports atomic commits with validated messages. With oh-my-pi, you can review code with priorities and verdicts, edit by content hash, and curate memory for the agent to learn from. It's also editor-drivable, allowing you to run the agent inside your favorite editor. In short, oh-my-pi is the ultimate coding sidekick - it's like having a superpower in your terminal. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

💡 godotengine/godot just hit the trending charts — here's why it matters. 🔗 https://github.com/godotengine/godot 📝 Godot Engine – Multi-platform 2D and 3D game engine ────────────────────────────── The Godot Engine is a feature-packed, cross-platform game engine for creating 2D and 3D games. It provides a comprehensive set of tools for game development, including a unified interface, and allows for one-click exporting to various platforms like desktop, mobile, and web. The engine is completely free and open source under the MIT license, giving users full control over their games. Godot is community-driven, with a strong focus on community involvement and contribution. To get started, users can download official binaries or compile from source. The engine has a large community, with various community channels and resources available, including official documentation and demos. Here's a simple example of how to create a scene in Godot using GDScript:

extends Node
func _ready():
    print("Hello, World!")

Overall, Godot Engine is an excellent choice for game developers, and its community-driven approach makes it an exciting project to be a part of. Godot Engine: empowering game developers to create without limits - join the community and start creating today! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

📌 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. ────────────────────────────── Harness is a team-architecture factory for Claude Code, enabling the creation of complex agent teams and skills from simple domain descriptions. With six pre-defined team-architecture patterns, Harness streamlines the process of building and managing agent teams. Key features include agent team design, skill generation, orchestration, and validation. To use Harness, simply trigger it with a prompt like "Build a harness for this project" and it will automatically generate the necessary agent definitions and skills tailored to your domain. The plugin is structured with a plugin.json manifest, SKILL.md definitions, and references for skill authoring and testing guides. Technical highlights include the use of

plugin marketplace add

for installation and

plugin install harness@harness-marketplace

for setup. The plugin also supports multiple execution modes, including Agent Teams and Subagents, allowing for flexibility in deployment. Harness is designed for a wide range of audiences, from developers and researchers to business users and educators. Whether you're looking to build a complex software system or simply automate a workflow, Harness provides a powerful tool for creating and managing agent teams. In a nutshell: Harness revolutionizes team-architecture creation, making it faster and more efficient than ever before! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ TauricResearch/TradingAgents is making waves. Here's the full picture. 🔗 https://github.com/TauricResearch/TradingAgents 📝 TradingAgents: Multi-Agents LLM Financial Trading Framework ────────────────────────────── The TradingAgents framework is a cutting-edge, open-source tool for simulating real-world trading firms using multi-agent systems and large language models (LLMs). Its primary purpose is to facilitate research into the dynamics of financial markets and the development of more sophisticated trading strategies. Key features of TradingAgents include support for multiple LLM providers (e.g., OpenAI, Google, Anthropic), modular architecture for easy customization, and a user-friendly CLI for interacting with the framework. To get started with TradingAgents, users can clone the repository, install the package, and launch the interactive CLI. The framework also provides a Python API for more advanced usage. TradingAgents is designed for researchers and developers interested in exploring the applications of LLMs in financial markets. Overall, TradingAgents offers a powerful tool for simulating and analyzing complex trading scenarios, making it an exciting development for anyone interested in the intersection of finance and AI: TradingAgents is revolutionizing the way we approach financial modeling and trading strategy development. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

🎰 Welcome Bonus 1200% — Maczo Crypto Casino 🎮 Crypto exchange · Sports · Live casino — all in one place 💳 USDT instant deposit & withdrawal → https://tglink.io/c07f00ebd9d268

📌 Spotted on GitHub Trending: EveryInc/compound-engineering-plugin — let's break it down. 🔗 https://github.com/EveryInc/compound-engineering-plugin 📝 Official Compound Engineering plugin for Claude Code, Codex, Cursor, and more ────────────────────────────── The Compound Engineering Plugin is designed to make each unit of engineering work easier than the last, inverting the traditional development process that often accumulates technical debt. This plugin provides AI skills and agents to streamline the development workflow, focusing on planning, review, and knowledge codification to ensure high-quality changes. The core features include: - /ce-brainstorm and /ce-plan for thorough planning - /ce-code-review and /ce-doc-review for catching issues - /ce-compound for codifying knowledge To get started, run /ce-setup in any project, and explore the 37 skills and 51 agents available. The plugin can be installed via various platforms, including Claude Code, Cursor, Codex, GitHub Copilot, Factory Droid, Qwen Code, OpenCode, Pi, Gemini, and Kiro, using specific installation commands. In summary, the Compound Engineering Plugin is a game-changer for developers, making each change easier than the last by compounding knowledge and streamlining the development process. Install it now and start coding smarter, not harder! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ p-e-w/heretic is making waves. Here's the full picture. 🔗 https://github.com/p-e-w/heretic 📝 Fully automatic censorship removal for language models ────────────────────────────── The Heretic GitHub repository is a cutting-edge project that enables fully automatic censorship removal for language models. This innovative tool combines advanced directional ablation, also known as "abliteration," with a TPE-based parameter optimizer powered by Optuna. The result is a decensored model that retains as much of the original model's intelligence as possible, without requiring any human effort or understanding of transformer internals. Key Features: - Automatic censorship removal - Supports most dense models, including multimodal and MoE architectures - Compatible with model quantization using bitsandbytes - Provides features for research into model internals and interpretability Usage: To get started, simply install the heretic-llm package and run the heretic command with the desired model. For example:

pip install -U heretic-llm
heretic Qwen/Qwen3-4B-Instruct-2507

Technical Highlights: - Employs PaCMAP projection for residual vector analysis - Offers options for generating plots and printing residual geometry details - Supports a variety of configuration parameters for customization Audience: Heretic is designed for anyone interested in working with language models, from researchers to developers and enthusiasts. The tool's automatic nature makes it accessible to those without extensive knowledge of transformer internals. In summary, Heretic is a powerful tool that makes it easy to remove censorship from language models, and with its cutting-edge features and ease of use, it's a game-changer for the field - decensor your models with just a few clicks! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

⚡ pbakaus/impeccable is making waves. Here's the full picture. 🔗 https://github.com/pbakaus/impeccable 📝 The design language that makes your AI harness better at design. ────────────────────────────── The Impeccable GitHub repository is a game-changer for frontend design, offering a comprehensive design skill with 7 domain-specific references and 23 commands to help you create impeccable designs. The key features include a shared design vocabulary with your AI, deterministic anti-pattern rules, and a CLI for detecting anti-patterns. To get started, you can visit impeccable.style to download ready-to-use bundles or install the skill using the CLI installer. The repository includes 23 commands such as polish, audit, and critique, which can be accessed through the single /impeccable skill. The technical highlights of Impeccable include its ability to load domain reference files on every command, a brand-vs-product register that adjusts defaults, and explicit guidance on what to avoid in design. The repository supports various tools, including Cursor, Claude Code, and Codex CLI, making it a versatile solution for designers and developers. Whether you're a beginner or an experienced designer, Impeccable is an essential tool to have in your arsenal. With its wide range of features and support for multiple tools, it's an excellent choice for anyone looking to improve their design skills. In short, Impeccable is the ultimate design companion that helps you create stunning, user-friendly interfaces with ease - design like a pro, every time. ────────────────────────────── 🧠 Channel: https://t.me/GithubRe

Watch and Earn 1,000,000 USDT Toobit Sports season. Come to grab 1,000,000 usdt on Toobit! Ad. 18+

🔥 D4Vinci/Scrapling is trending — and it deserves your attention. 🔗 https://github.com/D4Vinci/Scrapling 📝 🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl! ────────────────────────────── Scrapling is an adaptive web scraping framework that simplifies the process of extracting data from websites. Its key features include automatic element relocation when website structures change, bypassing anti-bot systems like Cloudflare Turnstile, and concurrent, multi-session crawls with pause/resume and automatic proxy rotation. To use Scrapling, you can start by creating a StealthyFetcher object and using its fetch method to retrieve a webpage. You can then use the css method to select and extract data from the webpage. For larger-scale crawls, you can create a Spider class that inherits from Scrapling's Spider base class. Scrapling is designed to be fast and efficient, with real-time stats and streaming capabilities. It's suitable for both beginners and experienced web scrapers, and its extensive documentation and active community make it easy to get started and find help when needed. Here's a simple example of how to use Scrapling:

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

Overall, Scrapling is a powerful and flexible web scraping framework that can help you extract the data you need from the web. With its easy-to-use API and extensive features, it's an ideal choice for anyone looking to scrape the web. Scrape the web like a pro with Scrapling - one library, zero compromises! ────────────────────────────── 🧠 Channel: https://t.me/GithubRe