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Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
Больше📈 Аналитический обзор Telegram-канала Github Top Repositories
Канал Github Top Repositories (@githubre) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 13 260 подписчиков, занимая 15 384 место в категории Образование и 32 523 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 13 260 подписчиков.
Согласно последним данным от 09 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 373, а за последние 24 часа — 13, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.17%. В первые 24 часа после публикации контент обычно набирает 0.73% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 155 просмотров. В течение первых суток публикация набирает 97 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 1.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как repository, fork, programming, statistic, description.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
Благодаря высокой частоте обновлений (последние данные получены 10 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
13 260
Подписчики
+1324 часа
+737 дней
+37330 день
Архив постов
13 260
🌟 InsForge/InsForge caught my eye on GitHub Trending today.
🔗 https://github.com/InsForge/InsForge
📝 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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🧠 Channel: https://t.me/GithubRe
13 260
📌 Spotted on GitHub Trending: z-lab/dflash — let's break it down.
🔗 https://github.com/z-lab/dflash
📝 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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🧠 Channel: https://t.me/GithubRe
13 260
💡 anthropics/financial-services just hit the trending charts — here's why it matters.
🔗 https://github.com/anthropics/financial-services
📝 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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🧠 Channel: https://t.me/GithubRe13 260
🔍 Deep-diving into Hmbown/DeepSeek-TUI — fresh off the trending list.
🔗 https://github.com/Hmbown/DeepSeek-TUI
📝 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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🧠 Channel: https://t.me/GithubRe
13 260
🌟 D4Vinci/Scrapling caught my eye on GitHub Trending today.
🔗 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 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)
13 260
💡 bytedance/deer-flow just hit the trending charts — here's why it matters.
🔗 https://github.com/bytedance/deer-flow
📝 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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🧠 Channel: https://t.me/GithubRe13 260
🔥 bwya77/vscode-dark-islands is trending — and it deserves your attention.
🔗 https://github.com/bwya77/vscode-dark-islands
📝 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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🧠 Channel: https://t.me/GithubRe
13 260
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13 260
🔍 Deep-diving into shiyu-coder/Kronos — fresh off the trending list.
🔗 https://github.com/shiyu-coder/Kronos
📝 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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🧠 Channel: https://t.me/GithubRe
13 260
🔥 cheahjs/free-llm-api-resources is trending — and it deserves your attention.
🔗 https://github.com/cheahjs/free-llm-api-resources
📝 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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🧠 Channel: https://t.me/GithubRe13 260
🔍 Deep-diving into anthropics/financial-services — fresh off the trending list.
🔗 https://github.com/anthropics/financial-services
📝 No description.
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The anthropics/financial-services GitHub repository offers a collection of reference agents, skills, and data connectors designed to streamline financial services workflows, including investment banking, equity research, private equity, and wealth management.
Key features include named agents that automate end-to-end workflows, vertical plugins that bundle skills and commands for specific financial services verticals, and data connectors that integrate with various data providers.
To get started, users can install the repository as a
Claude Cowork plugin or deploy it through the Claude Managed Agents API. The repository is file-based, using markdown and JSON, with no build step required.
Technical highlights include the use of MCP servers for data integration and the ability to customize connectors and skills to fit specific firm needs. The repository is suitable for financial services professionals looking to automate workflows and improve efficiency.
In a nutshell, the anthropics/financial-services repository is a powerful tool for financial services teams, offering a range of automated workflows and integrations to boost productivity — automate your financial workflows with ease.
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🧠 Channel: https://t.me/GithubRe13 260
📌 Spotted on GitHub Trending: virattt/dexter — let's break it down.
🔗 https://github.com/virattt/dexter
📝 An autonomous agent for deep financial research
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Dexter is an autonomous financial research agent that thinks, plans, and learns as it works. Its purpose is to perform complex financial analysis using task planning, self-reflection, and real-time market data.
The key features of Dexter include:
- Intelligent Task Planning: breaking down complex queries into structured research steps
- Autonomous Execution: selecting and executing the right tools to gather financial data
- Self-Validation: checking its own work and iterating until tasks are complete
- Real-Time Financial Data: accessing income statements, balance sheets, and cash flow statements
- Safety Features: built-in loop detection and step limits to prevent runaway execution
To use Dexter, you need to install the
Bun runtime, obtain API keys for OpenAI, Financial Datasets, and Exa (optional), and then clone and install the repository. You can run Dexter in interactive mode or with watch mode for development.
Dexter is suitable for financial researchers, data analysts, and anyone looking to automate complex financial analysis tasks.
One-liner takeaway: Dexter is your go-to autonomous financial research agent to streamline complex analysis tasks and provide data-backed answers with ease.
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🧠 Channel: https://t.me/GithubRe
13 260
⚡ InsForge/InsForge is making waves. Here's the full picture.
🔗 https://github.com/InsForge/InsForge
📝 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. Its key features include authentication, database, storage, model gateway, edge functions, and compute. To get started, you can either use the
cloud-hosted version at insforge.dev or self-host using Docker Compose. The platform is suitable for developers looking to build AI-powered applications, and its documentation and community support are available for those who need help. InsForge is licensed under the Apache License 2.0, making it a great choice for open-source projects. Star the repository to show your support and get notified about new releases - InsForge: where AI meets backend development.
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🧠 Channel: https://t.me/GithubRe