Github Top Repositories
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Top GitHub repositories in one place π Explore the best projects in programming, AI, data science, and more.
Show moreπ Analytical overview of Telegram channel Github Top Repositories
Channel Github Top Repositories (@githubre) in the English language segment is an active participant. Currently, the community unites 15 233 subscribers, ranking 13 394 in the Education category and 26 058 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 15 233 subscribers.
According to the latest data from 07 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 379 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 0.92%. Within the first 24 hours after publication, content typically collects 0.62% reactions from the total number of subscribers.
- Post reach: On average, each post receives 140 views. Within the first day, a publication typically gains 95 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as repository, fork, programming, statistic, description.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βTop GitHub repositories in one place π
Explore the best projects in programming, AI, data science, and more.β
Thanks to the high frequency of updates (latest data received on 08 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
15 233
Subscribers
+1124 hours
+767 days
+37930 days
Posts Archive
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π Spotted on GitHub Trending: addyosmani/agent-skills β let's break it down.
π https://github.com/addyosmani/agent-skills
π Production-grade engineering skills for AI coding agents.
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Production-grade engineering skills for AI coding agents are available in the
addyosmani/agent-skills GitHub repository. These skills encode workflows, quality gates, and best practices that senior engineers use when building software, ensuring AI agents follow them consistently across every development phase. The repository includes 7 slash commands that map to the development lifecycle, such as /spec, /plan, /build, /test, /review, /code-simplify, and /ship. Each command activates the right skills automatically, and skills can also be activated based on what you're doing, such as designing an API or building UI. The repository provides a quick start guide for various platforms, including Claude Code, Cursor, Gemini CLI, Windsurf, OpenCode, GitHub Copilot, Kiro IDE & CLI, and Codex. With 20 skills available, developers can ensure their AI coding agents follow best practices and produce high-quality code. These skills cover various aspects of software development, including definition, planning, building, verification, review, and shipping. The repository also includes agent personas and reference checklists to support the skills. Overall, the addyosmani/agent-skills repository provides a valuable resource for developers to improve the quality and consistency of their code. Upgrade your coding game with production-grade engineering skills for AI agents!
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π§ Channel: https://t.me/GithubRe15 232
π Meet LearningCircuit/local-deep-research: a gem from today's GitHub trending list.
π https://github.com/LearningCircuit/local-deep-research
π ~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.
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Local Deep Research is an AI-powered research assistant that helps you perform deep, agentic research using multiple LLMs and search engines with proper citations. It's designed to run locally for privacy, allowing you to use any LLM and build your own searchable knowledge base. You own your data and can see exactly how it works.
Key features include:
- Automatic research across web, academic papers, and your own documents
- Synthesis of research into a report with proper citations
- 20+ research strategies for quick facts, deep analysis, or academic research
- Encrypted library for storing and searching your documents
- Support for multiple LLMs and search engines
Usage is straightforward: simply pull and run the Docker image, or install using pip. You can also use Docker Compose for a more streamlined setup.
From a technical standpoint, Local Deep Research uses SQLCipher for encrypted storage, and includes features like in-memory credentials and supply chain security. The project also prioritizes security transparency, with documented scanner suppressions and security alerts.
This project is ideal for researchers, students, and anyone looking for a powerful, private research tool. With its focus on security, privacy, and customization, Local Deep Research is an excellent choice for those who want to take control of their research process.
In short, Local Deep Research is a game-changer for anyone looking to level up their research skills - take control of your research, and unlock new insights with Local Deep Research.
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π§ Channel: https://t.me/GithubRe
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π InsForge/InsForge caught my eye on GitHub Trending today.
π https://github.com/InsForge/InsForge
π InsForge is a Postgres-based backend with auth, storage, compute, hosting, and AI gateway. Built for coding agents.
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InsForge is a backend development platform designed for AI-native developers, providing a semantic layer between AI coding agents and backend primitives like databases, authentication, and storage. The platform allows agents to understand, operate, and inspect backend systems, enabling efficient development and deployment of applications.
Key features include a semantic layer for backend context engineering, support for multiple backend primitives, and a user-friendly interface for inspecting and configuring backend state.
To get started with
InsForge, you can either use the cloud-hosted version at insforge.dev or self-host it using Docker Compose. The platform provides a quickstart guide and comprehensive documentation to help you set up and use the platform.
Technical highlights of InsForge include its support for PostgreSQL, S3-compatible file storage, and OpenAI-compatible API. The platform also provides a model gateway, edge functions, and site deployment capabilities.
InsForge is designed for AI-native developers and provides a unique set of features that cater to their needs. Whether you're building a new application or migrating an existing one, InsForge provides a powerful backend platform to support your development needs.
In summary, InsForge is a powerful backend platform that provides a semantic layer for AI coding agents to interact with backend primitives, making it an ideal choice for AI-native developers. So why wait? Star the repository and start building with InsForge today - revolutionize your backend development with the power of AI!
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π§ Channel: https://t.me/GithubRe
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π Spotted on GitHub Trending: z-lab/dflash β let's break it down.
π https://github.com/z-lab/dflash
π DFlash: Block Diffusion for Flash Speculative Decoding
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The DFlash repository on GitHub introduces a lightweight block diffusion model designed for speculative decoding, enabling efficient and high-quality parallel drafting. This model is supported by various backends, including Transformers, SGLang, vLLM, and MLX.
To get started, users can install the required packages using
pip install commands and then serve models using vllm serve or python -m sglang.launch_server commands. The repository also provides a quick start guide for each backend and a benchmarking script to evaluate the performance of DFlash.
DFlash has been implemented with several models, including gemma-4-26B-A4B-it, Qwen3.5-27B, and LLaMA3.1-8B-Instruct, which can be found on the Hugging Face model hub.
The key highlight of DFlash is its ability to accelerate language models, making it an exciting development in the field of natural language processing.
In a nutshell, DFlash is a game-changer for language models, and its speculative decoding capabilities make it a must-try for anyone working with large language models.
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π§ Channel: https://t.me/GithubRe
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π‘ anthropics/financial-services just hit the trending charts β here's why it matters.
π https://github.com/anthropics/financial-services
π No description.
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The anthropics/financial-services GitHub repository provides a comprehensive set of reference agents, skills, and data connectors for various financial services workflows, including investment banking, equity research, private equity, and wealth management. These agents and skills can be installed as
Claude Cowork plugins or deployed through the Claude Managed Agents API for headless execution.
Key features include named agents that run end-to-end workflows, vertical plugins that bundle skills and data connectors by financial services vertical, and MCP integrations with various data providers. The repository is organized into plugins/managed-agent-cookbooks/Claude Cowork plugin or deploy the Managed Agents API and begin using the agents and skills to streamline their workflows.
In summary, the anthropics/financial-services repository provides a powerful toolkit for financial services professionals to automate and optimize their workflows, and with its flexible deployment options and comprehensive feature set, it's an indispensable resource for anyone looking to take their financial services workflow to the next level - Automate your financial workflows with ease.
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π§ Channel: https://t.me/GithubRe15 232
π Deep-diving into Hmbown/DeepSeek-TUI β fresh off the trending list.
π https://github.com/Hmbown/DeepSeek-TUI
π Coding agent for DeepSeek models that runs in your terminal
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DeepSeek TUI is a terminal-based coding agent that streamlines your workflow by integrating file editing, shell commands, web search, git management, and more. It's built around DeepSeek V4, offering features like auto mode, thinking-mode streaming, and a full tool suite.
To get started, install
deepseek-tui using npm, Cargo, or by downloading prebuilt binaries. You can then run deepseek in your terminal to access the TUI.
The app offers three modes: Plan (read-only), Agent (interactive), and YOLO (auto-approve). It also supports session save/resume, workspace rollback, and live cost tracking.
Key technical highlights include a ratatui interface, an async engine, and an OpenAI-compatible streaming client. The tool is designed for developers who want to boost their productivity and is available for Linux, macOS, and Windows.
In short, DeepSeek TUI is a powerful tool that helps you code smarter, not harder - streamline your workflow and take your productivity to the next level.
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π§ Channel: https://t.me/GithubRe
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π D4Vinci/Scrapling caught my eye on GitHub Trending today.
π https://github.com/D4Vinci/Scrapling
π π·οΈ 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 scraping websites, from a single request to a full-scale crawl. Its key features include automatic element relocation when website structures change, bypassing anti-bot systems like Cloudflare Turnstile, and a spider framework for concurrent, multi-session crawls with pause/resume and automatic proxy rotation.
To use Scrapling, you can start by using its
StealthyFetcher to fetch websites under the radar, then scrape data with its css method. For more complex tasks, you can create a custom Spider class to scale up to full crawls.
Scrapling is built for both Web Scrapers and regular users, providing a simple and efficient way to extract data from websites. With its real-time stats and streaming features, you can monitor your crawls and adjust them as needed.
Here's an 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)
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π‘ bytedance/deer-flow just hit the trending charts β here's why it matters.
π https://github.com/bytedance/deer-flow
π An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.
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DeerFlow is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to streamline complex tasks. It's powered by extensible skills and offers a flexible, modular architecture.
To get started, you can use the
make setup command to run an interactive wizard that guides you through the configuration process. This includes choosing an LLM provider, optional web search, and execution/safety preferences.
DeerFlow has several key features, including skills & tools, sub-agents, sandbox & file system, context engineering, and long-term memory. It also supports various deployment options, such as local development and Docker.
This project is ideal for developers and researchers looking to build custom AI-powered agents. With its InfoQuest integration and support for multiple LLM providers, DeerFlow offers a powerful platform for intelligent search and crawling.
One-liner takeaway: DeerFlow revolutionizes AI-powered task automation by providing a flexible, modular, and extensible framework for building custom super agents.
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π§ Channel: https://t.me/GithubRe15 232
π₯ bwya77/vscode-dark-islands is trending β and it deserves your attention.
π https://github.com/bwya77/vscode-dark-islands
π VSCode theme based off the easemate IDE and Jetbrains islands theme
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Islands Dark is a stunning dark color theme for Visual Studio Code, inspired by the easemate IDE. It features
floating glass-like panels, rounded corners, smooth animations, and a deeply refined UI.
The theme has two parts: a color theme and CSS customizations that create the floating glass panel look. To install, you can use the one-liner install or manual clone install. The theme is highly customizable, with key visual properties controlled by CSS custom properties.
Islands Dark is perfect for developers who want a unique and visually appealing coding experience. With its warm syntax highlighting and comprehensive language support, it's a great choice for coding in various programming languages.
The theme is suitable for developers of all levels, from beginners to experienced coders. Whether you're working on a small project or a large-scale application, Islands Dark provides a comfortable and efficient coding environment.
To get started, simply install the theme and customize it to your liking. With its easy installation process and highly customizable nature, you'll be coding in style in no time. Elevate your coding experience with Islands Dark - a theme that will make you love the darkness.
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π§ Channel: https://t.me/GithubRe
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15 232
π Deep-diving into shiyu-coder/Kronos β fresh off the trending list.
π https://github.com/shiyu-coder/Kronos
π Kronos: A Foundation Model for the Language of Financial Markets
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Kronos is the first open-source foundation model specifically designed for the language of financial markets, trained on data from over 45 global exchanges. This
decoder-only model is pre-trained to handle the unique characteristics of financial data, leveraging a novel two-stage framework. It first quantizes continuous, multi-dimensional K-line data into hierarchical discrete tokens using a specialized tokenizer, and then pre-trains a large, autoregressive Transformer on these tokens.
The model is designed for diverse quantitative tasks and is available in various capacities, including Kronos-mini, Kronos-small, Kronos-base, and Kronos-large, to suit different computational and application needs. A live demo is available to visualize Kronos's forecasting results.
To get started, you can install the dependencies using pip install -r requirements.txt and load a pre-trained model and its corresponding tokenizer from the Hugging Face Hub. The KronosPredictor class handles data preprocessing, normalization, prediction, and inverse normalization, making it straightforward to generate forecasts.
Kronos is perfect for quantitative analysts and researchers looking for a powerful tool to forecast financial markets - forecast your way to financial insights with Kronos.
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π§ Channel: https://t.me/GithubRe
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π₯ cheahjs/free-llm-api-resources is trending β and it deserves your attention.
π https://github.com/cheahjs/free-llm-api-resources
π A list of free LLM inference resources accessible via API.
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The cheahjs/free-llm-api-resources GitHub repository provides a list of services that offer free access or credits towards API-based Large Language Model (LLM) usage. The repository is updated by a Python script and includes a warning not to abuse these services.
The key features of this repository include:
- A list of free providers such as OpenRouter, Google AI Studio, and NVIDIA NIM
- A list of providers with trial credits such as Fireworks and Baseten
- Model limits and usage guidelines for each provider
To use this repository, simply browse through the list of providers, check their limits and usage guidelines, and start using their APIs.
Some technical highlights of this repository include:
-
OpenRouter with models like Gemma 3 12B Instruct and Llama 3.2 3B Instruct
- Google AI Studio with models like Gemini 3 Flash and Gemini 2.5 Flash
- NVIDIA NIM with various open models
This repository is useful for anyone looking to use LLM APIs without incurring significant costs, including developers, researchers, and students.
Don't abuse these free services, or we might lose them - use them wisely and build something amazing!
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π§ Channel: https://t.me/GithubRe