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 14 150 subscribers, ranking 14 019 in the Education category and 28 451 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 14 150 subscribers.
According to the latest data from 28 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 273 over the last 30 days and by 9 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 1.05%. Within the first 24 hours after publication, content typically collects 0.70% reactions from the total number of subscribers.
- Post reach: On average, each post receives 149 views. Within the first day, a publication typically gains 99 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 29 August, 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.
14 150
Subscribers
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+27330 days
Posts Archive
14 150
π― vercel/next.js landed on trending. Worth a proper look.
π https://github.com/vercel/next.js
π The React Framework
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Next.js is a popular React framework for building server-rendered, statically generated, and performance-optimized web applications. Its main purpose is to simplify the development process, allowing developers to focus on creating amazing user experiences.
Key features include server-side rendering, static site generation, and internationalization. To get started, simply create a new project using
npm init next-app or yarn create next-app, and you're ready to go.
From a technical perspective, Next.js provides a comprehensive set of tools and features, including built-in support for Webpack and Babel, as well as API routes for building custom server-side logic.
The framework is designed for frontend developers of all levels, from beginners to experienced professionals. Whether you're building a small blog or a complex e-commerce platform, Next.js has the tools and features you need to succeed.
In short: Next.js is the ultimate React framework for building fast, scalable, and performance-optimized web applications - so why wait, start building today!
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π§ Channel: https://t.me/GithubRe14 150
π₯ roboflow/supervision is trending β and it deserves your attention.
π https://github.com/roboflow/supervision
π We write your reusable computer vision tools. π
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The roboflow/supervision GitHub repository offers a comprehensive toolkit for building computer vision applications. Its purpose is to provide a set of reusable tools for tasks such as loading datasets, drawing detections, and counting objects in zones. The repository includes key features like model-agnostic design, connectors for popular libraries, customizable annotators, and dataset utilities.
To get started, users can
pip install supervision and explore the quickstart guide, which covers topics like loading models, using annotators, and working with datasets. The repository also provides technical highlights such as support for various model types, including classification, detection, and segmentation models, and integration with popular libraries like Ultralytics and Transformers.
The target audience for this repository includes data scientists, machine learning engineers, and developers working on computer vision projects. With its extensive documentation, tutorials, and community support, the roboflow/supervision repository is an excellent resource for anyone looking to build and deploy computer vision applications.
The repository is well-documented, with a comprehensive guide, tutorials, and a community-driven discussion forum. It is also actively maintained, with a strong focus on community engagement and contribution.
In summary, roboflow/supervision is a powerful toolkit for building computer vision applications, offering a wide range of features, tools, and resources to support developers and data scientists. With its flexible design, extensive documentation, and active community, it's an excellent choice for anyone working on computer vision projects: build computer vision applications faster and more reliably with roboflow/supervision.
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π§ Channel: https://t.me/GithubRe
14 150
π― obra/superpowers landed on trending. Worth a proper look.
π https://github.com/obra/superpowers
π An agentic skills framework & software development methodology that works.
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Superpowers is a cutting-edge software development methodology that empowers your coding agents with a set of composable skills and initial instructions. The purpose of Superpowers is to streamline the development process, from brainstorming to implementation, ensuring that your agent uses its skills effectively.
Key features include test-driven development, systematic debugging, and collaboration tools. To use Superpowers, simply install it as a plugin in your preferred coding agent, such as
Claude Code, Antigravity, or Codex App.
From a technical standpoint, Superpowers is built around a skills library that includes testing, debugging, and collaboration tools. The target audience for Superpowers is developers who want to improve their coding efficiency and quality.
Here's a sample installation command for Claude Code:
/plugin install superpowers@claude-plugins-official14 150
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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The agent-skills GitHub repository provides a set of production-grade engineering skills for AI coding agents. These skills encode workflows, quality gates, and best practices used by senior engineers throughout the development process. The repository includes
24 skills that cover the entire development lifecycle, from defining what to build to shipping to production.
Key features include 8 slash commands that map to different stages of development, such as /spec, /plan, /build, and /ship. These commands activate the right skills automatically, ensuring that agents follow best practices consistently. The repository also includes a quick start guide that allows users to install the skills using the skills CLI or integrate them with various agents, such as Claude Code, Cursor, and Codex.
The skills are designed to be used by developers, engineers, and teams who want to improve the quality and efficiency of their development process. By using these skills, agents can automate tasks, reduce manual steps, and ensure that code meets high standards.
Technical highlights include the use of Markdown files to define the skills, which makes it easy to create, modify, and extend them. The skills also include verification gates and anti-rationalization tables to ensure that agents are following best practices and not introducing biases or errors.
In summary, the agent-skills repository provides a powerful set of tools for improving the development process with AI coding agents. With its production-grade engineering skills, 8 slash commands, and quick start guide, it's an essential resource for any team looking to streamline their development workflow. Automate your development process with agent-skills and take your team's productivity to the next level!
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π§ Channel: https://t.me/GithubRe14 150
β‘ esengine/DeepSeek-Reasonix is making waves. Here's the full picture.
π https://github.com/esengine/DeepSeek-Reasonix
π DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability β leave it running.
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Reasonix is a DeepSeek-native AI coding agent designed for your terminal, engineered around prefix-cache stability to keep token costs low. The legacy TypeScript line is in maintenance mode, with active development moved to the
Go rewrite in the main-v2 branch.
Key features include cache stability, prefix-cache mechanic, and DeepSeek API integration. To use Reasonix, simply install it globally with npm install -g reasonix and run reasonix code in your project directory.
The agent is suitable for developers and power users who want to leverage AI for coding tasks. With its cache-first loop and four mechanisms to keep cacheable bytes stable, Reasonix provides a cost-effective solution for coding tasks.
One notable example is a real user who achieved a 99.82% cache hit rate, resulting in significant cost savings.
The project has a bilingual Discord community for setup help, workflow showcases, and feature discussions.
To get started, grab a DeepSeek API key and install Reasonix globally.
In summary, Reasonix is a powerful AI coding agent that helps you code more efficiently with its prefix-cache stability and DeepSeek API integration - try it out and experience the power of AI-assisted coding.
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π§ Channel: https://t.me/GithubRe
14 150
π firecrawl/pdf-inspector caught my eye on GitHub Trending today.
π https://github.com/firecrawl/pdf-inspector
π Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.
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Pdf-inspector is a fast Rust library for PDF classification and text extraction. It detects whether a PDF is text-based or scanned, extracts text with position awareness, and converts to clean Markdown β all without OCR. Key features include smart classification, text extraction, Markdown conversion, table detection, and CID font support.
The library is
lightweight, with a single dependency on lopdf for PDF parsing. It has bindings for Python, Node.js, and browser WebAssembly, making it accessible to various users.
To get started, you can install the library using cargo add pdf-inspector or pip install pdf-inspector, and then use it in your project. For example, in Python, you can use import pdf_inspector and result = pdf_inspector.process_pdf("document.pdf") to classify and extract text from a PDF.
Pdf-inspector is suitable for users who need to process PDFs at scale, such as in document processing pipelines. It helps save cost and latency by routing text-based PDFs to local extraction and scanned PDFs to OCR services.
The library is well-documented, with a README that includes a quick start guide, benchmark results, and API references for each language binding.
In summary, pdf-inspector is a fast and lightweight library for PDF classification and text extraction that helps users process PDFs efficiently and effectively β process your PDFs smarter, not harder.
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π§ Channel: https://t.me/GithubRe14 150
π― donnemartin/system-design-primer landed on trending. Worth a proper look.
π https://github.com/donnemartin/system-design-primer
π Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
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The System Design Primer is a comprehensive resource to help engineers learn how to design large-scale systems and prepare for system design interviews. It covers a wide range of topics, including scalability, performance, availability, and security. The repository provides an
organized collection of resources, including system design topics, interview questions with solutions, and Anki flashcard decks.
Key features include a study guide to help you prepare based on your interview timeline and a section on how to approach a system design interview question. The repository is continually updated and open to contributions from the community.
Some technical highlights of the repository include system design interview questions with solutions, object-oriented design interview questions with solutions, and additional system design interview questions. The repository uses spaced repetition to help you retain key system design concepts.
The target audience for this repository includes engineers who want to improve their system design skills and those who are preparing for system design interviews. Overall, the System Design Primer is a valuable resource for any engineer looking to improve their system design skills.
The system design interview is not just about designing systems, it's about communicating your design effectively.
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π§ Channel: https://t.me/GithubRe
14 150
π Deep-diving into TencentCloud/TencentDB-Agent-Memory β fresh off the trending list.
π https://github.com/TencentCloud/TencentDB-Agent-Memory
π TencentDB Agent Memory is a team-level memory hub for AI Agents β turning conversations, docs, and code into four reusable memory assets (Chat Memory, Skill, LLM-Wiki, Code-Graph) that are governed, shared, and equipped across agents and frameworks.
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The TencentDB Agent Memory is a cutting-edge solution designed to enhance the capabilities of AI agents by providing them with a robust memory system. This system combines symbolic short-term memory and layered long-term memory to enable agents to learn from workflows, retain task context, and reuse past experiences. By offloading heavy tool logs and condensing them into compact Mermaid symbols, the system reduces token usage and improves task success rates.
The key features of TencentDB Agent Memory include:
*
Memory layering: a hierarchical approach to memory formation and recall, allowing for progressive disclosure and heterogeneous storage.
* Symbolic memory: a Mermaid symbol graph that encodes task state transitions, enabling precise and concise memory representation.
* Context offloading: the ability to offload full tool logs to external files, reducing token cost while preserving traceability.
The system has been integrated with OpenClaw and Hermes agents, with impressive results, including a 61.38% reduction in token usage and a 51.52% improvement in pass rate.
To get started with TencentDB Agent Memory, users can follow the Quick Start guide, which provides step-by-step instructions for installing and configuring the plugin with OpenClaw or Hermes agents.
In summary, TencentDB Agent Memory is a powerful solution that enables AI agents to remember what's important, so humans can focus on what truly matters β and that's a game-changer!
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π§ Channel: https://t.me/GithubRe
14 150
β‘ huangruiteng/loopx is making waves. Here's the full picture.
π https://github.com/huangruiteng/loopx
π Lightweight loop engineering state kernel for long-running AI agent teams. Agent-loop agnostic across Codex, Claude Code, and other coding agents, with durable goals, quota-aware auto-wake, executable todos, evidence logs, and verifiable handoffs.
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LoopX is a lightweight, local control plane for loop engineering, designed to keep long-running AI agent work reviewable, restartable, and easier to hand off across turns, tools, and agents. It maintains a compact, durable control state, including objectives, gates, todos, evidence, and quota, ensuring that human judgment is always involved when needed. Key features include a simple, agent-agnostic design, support for multiple AI runtimes (e.g., Codex, Claude Code), and a
quota system that decides whether a turn should deliver, ask, wait, or stop. Usage involves installing LoopX, connecting it to your project, and using the loopx command to manage the loop. Technical highlights include a small core tick, deliberate separation of concerns, and a focus on local-first design. LoopX is suitable for developers, researchers, and operators working with long-running AI agent projects, such as multi-day engineering, research, or experiments. In short, LoopX helps you keep the loop moving while keeping human judgment in the driver's seat.
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π§ Channel: https://t.me/GithubRe
14 150
π‘ EveryInc/compound-engineering-plugin just hit the trending charts β here's why it matters.
π https://github.com/EveryInc/compound-engineering-plugin
π Official Compound Engineering plugin for Claude Code, Codex, Cursor, and more
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Compound Engineering Plugin makes each unit of engineering work easier than the last. This plugin, available on GitHub, provides AI skills to plan, review, and codify knowledge, inverting traditional development's complexity accumulation.
Key features include:
- Brainstorming and planning before writing code
- Reviewing to catch issues and calibrate judgment
- Codifying knowledge for reusability
- Supporting multiple platforms, including Codex, Claude, and Cursor
To use, simply install the plugin from the marketplace or via command line, then invoke skills with
$skill-name (e.g. $ce-plan) or /skill-name (e.g. /ce-brainstorm).
Technical highlights include a self-contained install, with no separate custom-agent install required, and support for multiple editors and CLI tools.
The target audience is developers and engineers looking to streamline their workflow and improve quality.
With Compound Engineering, you can automate your development process and make each unit of work easier than the last - try it out and discover a whole new way to code!
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π§ Channel: https://t.me/GithubRe14 150
π― esengine/DeepSeek-Reasonix landed on trending. Worth a proper look.
π https://github.com/esengine/DeepSeek-Reasonix
π DeepSeek-native AI coding agent for your terminal. Engineered around prefix-cache stability β leave it running.
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Reasonix is a DeepSeek-native AI coding agent designed for terminal use, focusing on
prefix-cache stability to keep token costs low. This legacy TypeScript line is in maintenance mode, with active development moved to the Go rewrite in the main-v2 branch.
Key features include:
- Cache stability as an invariant in the loop design
- DeepSeek-only for byte-stable prefix-cache mechanics
- Real-time feedback with token costs and cache hits
To get started, install Reasonix globally with npm install -g reasonix or run it once with npx reasonix code. The reasonix command launches the coding agent in the current directory, while other subcommands like chat, run, and doctor provide additional functionality.
The desktop client offers a GUI over the same loop, with a prerelease version available for download. Configuration is done through a JSON file at ~/.reasonix/config.json and per-project overrides.
Audience: Developers, especially those familiar with DeepSeek and interested in AI-powered coding tools.
One-liner takeaway: Reasonix is your AI coding companion, designed to optimize token costs and streamline your development workflow with DeepSeek-native stability.
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π§ Channel: https://t.me/GithubRe14 150
β±οΈ Give Iris Classroom one minute.
You might discover:
π¬ Why onions make you cry
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π§ Why your brain remembers some things and forgets others
Just fascinating science, made simple.
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14 150
β‘ browser-use/video-use is making waves. Here's the full picture.
π https://github.com/browser-use/video-use
π Edit videos with coding agents
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Introducing video-use, a revolutionary open-source tool that enables you to edit videos with ease using Claude Code. Simply drop your raw footage into a folder, interact with Claude Code, and get a polished
final.mp4 file in return. This powerful tool works with any type of content, from talking heads to travel videos, without requiring presets or menus.
Key features of video-use include:
- Cutting out filler words and dead space
- Auto color grading
- 30ms audio fades
- Burning subtitles
- Generating animation overlays
Technical highlights include a self-evaluation process that checks the rendered output at every cut boundary, ensuring a seamless viewing experience. The tool also persists session memory, allowing you to pick up where you left off in your next editing session.
Usage is straightforward: simply clone the repository, install the dependencies, and register the skill with your agent. Then, point your agent at a folder of raw takes and let video-use do the rest.
Audience for video-use includes anyone looking to simplify their video editing workflow, from content creators to filmmakers.
In short, video-use is a game-changer for video editing - with its automated features and intuitive interface, you can edit like a pro without being one!
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π§ Channel: https://t.me/GithubRe
