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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.
Ko'proq ko'rsatishπ Telegram kanali Github Top Repositories analitikasi
Github Top Repositories (@githubre) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 13 267 obunachidan iborat bo'lib, TaΚΌlim toifasida 15 384-o'rinni va Hindiston mintaqasida 32 523-o'rinni egallagan.
π Auditoriya koβrsatkichlari va dinamika
Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ sanasidan buyon loyiha tez oβsib, 13 267 obunachiga ega boβldi.
09 Iyun, 2026 dagi oxirgi maβlumotlarga koβra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 373 ga, soβnggi 24 soatda esa 13 ga oβzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oβrtacha 1.17% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.73% ini tashkil etuvchi reaksiyalarni toβplaydi.
- Post qamrovi: Har bir post oβrtacha 155 marta koβriladi; birinchi sutkada odatda 97 ta koβrish yigβiladi.
- Reaksiyalar va oβzaro taβsir: Auditoriya faol: har bir postga oβrtacha 1 ta reaksiya keladi.
- Tematik yoβnalishlar: Kontent repository, fork, programming, statistic, description kabi asosiy mavzularga jamlangan.
π Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taβriflaydi:
βTop GitHub repositories in one place π
Explore the best projects in programming, AI, data science, and more.β
Yuqori yangilanish chastotasi (oxirgi maβlumot 10 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni TaΚΌlim toifasidagi muhim taβsir nuqtasiga aylantirishini koβrsatadi.
13 267
Obunachilar
+1324 soatlar
+737 kunlar
+37330 kunlar
Postlar arxiv
13 273
π Deep-diving into opencv/opencv β fresh off the trending list.
π https://github.com/opencv/opencv
π Open Source Computer Vision Library
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The OpenCV library is a game-changer for computer vision enthusiasts, providing a vast array of functions and tools for image and video processing, feature detection, object recognition, and more. With its
extensive documentation and active community, OpenCV is the perfect platform for developers, researchers, and students to explore and innovate in the field of computer vision.
To get started, users can access the official homepage for courses, documentation, and forums, or contribute to the library by submitting pull requests and following the guidelines. The library's technical highlights include its ability to support various programming languages, such as C++, Python, and Java, making it a versatile tool for diverse applications.
Whether you're a seasoned developer or just starting out, OpenCV is an invaluable resource for anyone looking to push the boundaries of computer vision. So why not join the community today and start building something amazing with OpenCV? Empower your vision with the world's leading computer vision library!
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π§ Channel: https://t.me/GithubRe13 273
π Spotted on GitHub Trending: roboflow/supervision β let's break it down.
π https://github.com/roboflow/supervision
π We write your reusable computer vision tools. π
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Supervision is a Python library that simplifies the development of computer vision applications. Its primary purpose is to provide reusable tools for common tasks such as loading datasets, drawing detections, and counting objects in a specific zone. The library is model-agnostic, allowing users to plug in any classification, detection, or segmentation model.
To get started, you can
pip install supervision in a Python environment with version 3.9 or higher. The library offers a range of key features, including connectors for popular libraries like Ultralytics, Transformers, and MMDetection, as well as highly customizable annotators for visualizing detections.
For usage, you can use the library to load datasets from various formats, split and merge datasets, and save them in different formats. The library also provides a range of technical highlights, including support for object detection, tracking, and speed estimation.
The library is designed for developers and researchers working on computer vision projects, particularly those who want to build applications faster and more reliably. With its user-friendly documentation and active community, Supervision is an excellent choice for anyone looking to simplify their computer vision workflow.
One-liner takeaway: Supervision simplifies computer vision development by providing reusable tools and a model-agnostic approach, making it an excellent choice for developers and researchers looking to build applications faster and more reliably.
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π§ Channel: https://t.me/GithubRe
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π₯ RyanCodrai/turbovec is trending β and it deserves your attention.
π https://github.com/RyanCodrai/turbovec
π A vector index built on TurboQuant, written in Rust with Python bindings
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Turbovec is a Rust vector index with Python bindings, built on Google Research's TurboQuant algorithm. It allows for
online ingest, adding vectors without a train step, and is faster than FAISS with hand-written NEON and AVX-512BW kernels. Key features include filter-at-search-time capabilities, pure local deployment, and support for stable external ids.
Technical highlights include a 10-16x compression ratio, Lloyd-Max scalar quantization, and bit-packing. The length-renormalized scoring ensures unbiased inner-product estimation.
Audience: Developers building RAG (Retrieval-Augmented Generation) stacks where privacy, memory, or latency matters.
To get started, install via pip install turbovec or cargo add turbovec, and explore the Python or Rust APIs.
Turbovec is ideal for applications requiring efficient and private vector search. Try it out and experience the power of TurboQuant! You'll be searching faster and more efficiently in no time: Turbovec - search at the speed of thought.
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π§ Channel: https://t.me/GithubRe
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π mvanhorn/last30days-skill caught my eye on GitHub Trending today.
π https://github.com/mvanhorn/last30days-skill
π AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
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The last30days-skill is an AI agent-led search engine that scores results by upvotes, likes, and real money, rather than editors. It searches across multiple platforms, including Reddit, X, YouTube, and GitHub, to provide a more comprehensive view of a topic. The skill is easy to use, with a
/last30days command that can be run in various platforms, including Claude Code, Codex, and Gemini CLI.
Key features include shareable HTML briefs, intelligent search, and cross-source cluster merging. It's useful for research, sales calls, and staying up-to-date with the latest information on a topic. The skill is designed for anyone looking for a more accurate and comprehensive search engine, including researchers, sales professionals, and anyone looking to stay informed.
One-liner takeaway: With last30days-skill, you can search what actually matters, not just what editors think is relevant.
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π§ Channel: https://t.me/GithubRe13 273
π aaif-goose/goose caught my eye on GitHub Trending today.
π https://github.com/aaif-goose/goose
π an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM
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Meet goose, your native open source AI agent that runs on your machine, helping you with code, research, writing, automation, and more. It's available as a desktop app for macOS, Linux, and Windows, a full CLI for terminal workflows, and an API to embed it anywhere.
goose is built in Rust for performance and portability, and it works with 15+ providers, including Anthropic, OpenAI, and Google. You can connect to 70+ extensions via the Model Context Protocol open standard. To get started, you can download the desktop app or install the CLI using a simple script:
curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash13 273
π‘ luongnv89/claude-howto just hit the trending charts β here's why it matters.
π https://github.com/luongnv89/claude-howto
π A visual, example-driven guide to Claude Code β from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
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The luongnv89/claude-howto GitHub repository is a comprehensive, structured guide to mastering Claude Code. It's designed to help you go from basic usage to orchestrating agents, hooks, skills, and MCP servers in a weekend. The guide features visual tutorials,
copy-paste templates, and a guided learning path that takes you from beginner to power user in 11-13 hours.
Key features include:
* 10 tutorial modules covering every Claude Code feature
* Copy-paste configs for slash commands, CLAUDE.md templates, hook scripts, MCP configs, subagent definitions, and full plugin bundles
* Mermaid diagrams showing how each feature works internally
* A guided learning path with time estimates and interactive quizzes to identify knowledge gaps
The guide is suitable for developers of all levels, from those who have just installed Claude Code to experienced users looking to combine features into workflows. It's actively maintained, compatible with Claude Code 2.1+, and available under the MIT license, making it free to use forever.
To get started, you can clone the repository, copy a slash command template, and try it in 15 minutes. The guide also includes a 1-hour essential setup to get you up and running quickly.
In summary, luongnv89/claude-howto is the ultimate resource for unlocking Claude Code's full potential, and with it, you can master Claude Code in a weekend and take your productivity to the next level - start learning now and 10x your productivity!
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π§ Channel: https://t.me/GithubRe13 273
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π Spotted on GitHub Trending: TapXWorld/ChinaTextbook β let's break it down.
π https://github.com/TapXWorld/ChinaTextbook
π ζζε°ει«γ倧ε¦PDFζζγ
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ChinaTextbook is a GitHub repository that provides free access to Chinese textbooks, promoting equal access to education. The repository includes textbooks for elementary school, junior high school, high school, and university levels, covering subjects like mathematics.
To use the repository, simply browse through the folders and download the desired textbooks. Note that some files are split into smaller parts due to GitHub's upload size limits. To merge these files, you can use the
mergePDFs-windows-amd64.exe program provided in the repository.
The project encourages open-source contributions and community involvement. You can support the project by donating or joining their Telegram community to stay updated on the latest developments.
One-liner takeaway: ChinaTextbook is a valuable resource for anyone looking for free access to Chinese educational materials, promoting education equality and open access to knowledge.
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π§ Channel: https://t.me/GithubRe13 273
π₯ CopilotKit/CopilotKit is trending β and it deserves your attention.
π https://github.com/CopilotKit/CopilotKit
π The Frontend Stack for Agents & Generative UI. React, Angular, Mobile, Slack, and more. Makers of the AG-UI Protocol
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CopilotKit is a multi-platform agentic framework that enables you to build full-stack agentic applications, Generative UI, and chat applications. The framework allows agents to power your web app, mobile app, and team's Slack workspace. It features a chat UI, backend tool rendering, generative UI, shared state, and human-in-the-loop workflows. CopilotKit supports various platforms, including React, Angular, Vue, and React Native.
The framework is built on top of the
AG-UI Protocol, which is adopted by major companies like Google, LangChain, AWS, Microsoft, Mastra, and PydanticAI. CopilotKit provides a range of tools and features, including a useAgent hook, generative UI, and self-learning agents. The framework is designed to be easy to use, with a simple installation process and a comprehensive documentation.
CopilotKit is suitable for developers, product teams, and companies looking to build agentic applications and integrate AI into their products. The framework is constantly evolving, with new features and updates being added regularly. To get started with CopilotKit, you can install it using npx copilotkit@latest create -f <framework> or npx copilotkit@latest init for existing projects.
With CopilotKit, you can add AI to your app in just 1 minute and unlock the power of agentic applications. Build the future of AI-powered applications with CopilotKit - where agents meet applications.
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π§ Channel: https://t.me/GithubRe
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π‘ roboflow/supervision just hit the trending charts β here's why it matters.
π https://github.com/roboflow/supervision
π We write your reusable computer vision tools. π
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Supervision is a Python library for building computer vision applications. Its purpose is to provide a simple and efficient way to work with computer vision models, datasets, and annotations. The library offers key features such as model-agnostic detection, segmentation, and classification, as well as tools for data loading, splitting, and merging.
To use Supervision, you can install it via pip:
pip install supervision. The library supports various models and datasets, including Ultralytics, Transformers, and MMDetection, and provides connectors for popular libraries.
Technical highlights of Supervision include its ability to load and annotate images and videos, as well as its support for customizable annotators. The library is designed for data scientists and machine learning engineers who want to build and deploy computer vision applications quickly and efficiently.
In summary, Supervision is a powerful library that simplifies computer vision development - build computer vision apps faster with Supervision.
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π§ Channel: https://t.me/GithubRe13 273
π‘ MemPalace/mempalace just hit the trending charts β here's why it matters.
π https://github.com/MemPalace/mempalace
π The best-benchmarked open-source AI memory system. And it's free.
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MemPalace is a local-first AI memory solution that stores conversation history as verbatim text and retrieves it with semantic search. It features a pluggable backend, with ChromaDB as the default, and supports alternative backends like sqlite_exact, qdrant, and pgvector.
To get started, users can install MemPalace using
uv tool install mempalace or pipx install mempalace, then initialize it with mempalace init ~/projects/myapp. The mempalace mine command is used to mine content into the palace, while mempalace search retrieves relevant information.
MemPalace boasts an impressive 96.6% R@5 raw on the LongMemEval benchmark, with no API calls required. It also includes a temporal entity-relationship graph and supports MCP tools for palace reads/writes, knowledge-graph operations, and more.
The target audience for MemPalace includes developers, researchers, and individuals seeking a robust, local-first AI memory solution.
Overall, MemPalace offers a powerful and flexible solution for storing and retrieving conversation history, making it an excellent choice for those seeking a reliable and efficient AI memory system.
Takeaway: MemPalace revolutionizes local-first AI memory, making it a game-changer for anyone seeking to harness the power of semantic search.
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π§ Channel: https://t.me/GithubRe13 273
π― Andyyyy64/whichllm landed on trending. Worth a proper look.
π https://github.com/Andyyyy64/whichllm
π Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly.
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whichllm is a handy tool that helps you find the best local LLM (Large Language Model) that can run on your hardware. It auto-detects your GPU, CPU, and RAM, and then ranks the top models from HuggingFace that fit your system. You can use it to simulate a GPU before buying, compare upgrade candidates, and even start a chat with a model.
The tool uses evidence-based ranking, not just size heuristics, to choose the top pick. It also considers recency-aware scores, so stale leaderboards are demoted. You can use
whichllm to get a copy-paste Python snippet for any model, and it supports various model formats like GGUF, AWQ, and GPTQ.
Technical highlights include architecture-aware estimates, live data from the HuggingFace API, and a simple, scriptable command-line interface. whichllm is designed for developers, researchers, and anyone who wants to work with LLMs.
To get started, simply run uvx whichllm@latest or install it using brew install andyyyy64/whichllm/whichllm or pip install whichllm.
Here's a punchy one-liner takeaway: With whichllm, you can easily find the perfect LLM for your hardware and start building amazing AI projects!
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π§ Channel: https://t.me/GithubRe
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β‘ openai/plugins is making waves. Here's the full picture.
π https://github.com/openai/plugins
π OpenAI Plugins
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The openai/plugins repository is a treasure trove of community-driven innovation, hosting a wide range of Codex plugin examples to streamline your workflow. Each plugin is carefully organized under its own directory, complete with a
plugin.json manifest and optional supporting files like skills/, agents/, and assets/.
Some highlighted examples include plugins for Figma, Notion, iOS and macOS app development, web apps, Expo, and more. These plugins are designed to make your life easier, whether you're working on design systems, planning and research, or building and deploying apps.
The technical details are straightforward: each plugin has a required plugin.json file and may include additional files and directories. For example, a plugin might include a hooks.jsonagents/ directory for custom agent implementations.
This repository is perfect for developers, designers, and makers looking to tap into the power of Codex and automate their workflows.
In short, the openai/plugins repository is your one-stop shop for unlocking Codex's full potential - join the community and start building today!
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π§ Channel: https://t.me/GithubRe13 273
π‘ phuryn/pm-skills just hit the trending charts β here's why it matters.
π https://github.com/phuryn/pm-skills
π PM Skills Marketplace: 100+ agentic skills, commands, and plugins β from discovery to strategy, execution, launch, and growth.
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The pm-skills GitHub repository is a game-changer for product managers, offering a comprehensive AI-powered operating system for making better product decisions. With 68 PM skills and 42 chained workflows across 9 plugins, this marketplace provides a structured approach to product management, covering discovery, strategy, execution, launch, growth, and shipping AI-built code.
The
skills are the building blocks, encoding proven PM frameworks and guiding users through specific tasks. Commands chain one or more skills into end-to-end processes, while plugins group related skills and commands into installable packages.
To get started, users can install the marketplace using Claude Cowork or Claude Code, or even use the skills with other AI assistants like Codex, Gemini CLI, OpenCode, Cursor, or Kiro.
The repository includes a range of plugins, such as pm-product-discovery, pm-product-strategy, and pm-execution, each with its own set of skills and commands. For example, the /discover command chains four skills together: brainstorm-ideas, identify-assumptions, prioritize-assumptions, and brainstorm-experiments.
In summary, the pm-skills repository empowers product managers to make better product decisions with its comprehensive AI-powered operating system - Upgrade your product management workflow with pm-skills and start making data-driven decisions today!
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π§ Channel: https://t.me/GithubRe