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 14 152 subscribers, ranking 14 012 in the Education category and 28 382 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 14 152 subscribers.
According to the latest data from 29 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 256 over the last 30 days and by 0 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 0.69% reactions from the total number of subscribers.
- Post reach: On average, each post receives 149 views. Within the first day, a publication typically gains 98 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 30 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.
14 152
Subscribers
No data24 hours
+257 days
+25630 days
Posts Archive
14 191
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. It's a decoder-only model that leverages a novel two-stage framework: a specialized tokenizer quantizes continuous, multi-dimensional K-line data into hierarchical discrete tokens, and a large, autoregressive Transformer is then pre-trained on these tokens.
The model is unique in its ability to handle the high-noise characteristics of financial data, making it a unified model for diverse quantitative tasks. You can access a
live demo to visualize Kronos's forecasting results for the BTC/USDT trading pair.
To get started, you can install the dependencies with pip install -r requirements.txt and load a pre-trained Kronos model and its corresponding tokenizer from the Hugging Face Hub. The model is suitable for quantitative analysts, data scientists, and developers interested in financial forecasting.
Here's a simple example of how to use the `KronosPredictor` class:
from model import Kronos, KronosTokenizer, KronosPredictor
tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
predictor = KronosPredictor(model, tokenizer, max_context=512)
14 191
π‘ diegosouzapw/OmniRoute just hit the trending charts β here's why it matters.
π https://github.com/diegosouzapw/OmniRoute
π Never stop coding. Free MIT AI gateway: one endpoint, 268+ providers (50+ free), 500+ models β Kimi, Claude, GPT, OpenAI, Gemini, GLM, DeepSeek, MiniMax. Works with Claude Code, Codex, Cursor, OpenCode, Cline & Copilot. Quota-aware auto-fallback, RTK+Caveman compression saves 15-95% tokens, MCP/A2A, Desktop/PWA. Built by 500+ contributors
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OmniRoute is a game-changing tool that simplifies the process of working with multiple AI providers. Its main purpose is to provide a unified endpoint for accessing over
250 AI providers, including 90+ free tiers. This means you can use various AI tools like Claude Code, Codex, and Cursor through a single configuration.
With OmniRoute, you can save up to 95% of tokens thanks to its RTK and Caveman compression features. It also offers auto-fallback across providers, ensuring that you never hit rate limits and can keep working without interruptions. The tool is production-grade, with features like circuit breakers, TLS stealth, and memory guardrails.
OmniRoute is designed for developers and teams who want to streamline their AI workflow and reduce costs. Its technical highlights include support for 18 routing strategies, a simple and intuitive dashboard, and a large community of users and contributors.
In short, OmniRoute is a powerful tool that helps you maximize your AI potential while minimizing costs and complexities - never stop coding with OmniRoute!
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π§ Channel: https://t.me/GithubRe
14 191
π jamiepine/voicebox caught my eye on GitHub Trending today.
π https://github.com/jamiepine/voicebox
π The open-source AI voice studio. Clone, dictate, create.
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Voicebox is an open-source AI voice studio that lets you clone voices, generate speech, and dictate into any app. It's a local-first alternative to cloud-based services, offering complete privacy as all models and data stay on your machine.
Key features include
7 TTS engines, 23 languages, voice cloning, and post-processing effects like pitch shift and reverb. You can also use paralinguistic tags for expressive speech and create custom voice personalities with a bundled local LLM.
Voicebox is API-first, with a REST API and MCP server for integrating voice I/O into your apps. It's built with Tauri (Rust) for native performance and runs on macOS, Windows, Linux, and Docker.
The target audience is anyone looking for a free, open-source voice studio with advanced features and complete control over their data.
In short, Voicebox is your one-stop-shop for all voice-related needs - it's like having a personal voice assistant at your fingertips!
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π§ Channel: https://t.me/GithubRe14 191
π‘ chrislgarry/Apollo-11 just hit the trending charts β here's why it matters.
π https://github.com/chrislgarry/Apollo-11
π Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.
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The Apollo-11 GitHub repository is a treasure trove for space enthusiasts and historians, containing the original source code for the Apollo 11 guidance computer (AGC) used in the
Command Module (Comanche055) and Lunar Module (Luminary099). This project aims to provide a single repository for the original Apollo 11 source code, allowing users to explore and learn from the code that made the historic moon landing possible.
The repository includes translations in multiple languages, making it accessible to a broader audience. To compile the original source code, users can refer to the Virtual AGC project. The repository also provides information on contributing, attribution, and contract and approvals, offering a comprehensive understanding of the project's context and significance.
The target audience for this repository includes space enthusiasts, historians, and developers interested in exploring the Apollo 11 source code. The project's technical highlights include the use of the yaYUL assembler and the Colossus 2A program.
Apollo-11 is a unique opportunity to delve into the history of space exploration and understand the technology that made it possible. One fascinating aspect of this project is the level of detail and accuracy that has been maintained, with contributors working to ensure that the code is as faithful to the original as possible.
In summary, the Apollo-11 repository is a valuable resource for anyone interested in space history, and its code is a testament to human ingenuity - the code that took humans to the moon is now available for anyone to explore.
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π§ Channel: https://t.me/GithubRe14 191
π Deep-diving into likec4/likec4 β fresh off the trending list.
π https://github.com/likec4/likec4
π Visualize, collaborate, and evolve the software architecture with always actual and live diagrams from your code
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LikeC4 is a modeling language that lets you describe your software architecture and generate diagrams from it. Key features include flexibility in customizing notation, element types, and nested levels in the architecture model. You can use it to visualize, collaborate on, and evolve your software architecture with always up-to-date, live diagrams. To
get started, run npx likec4 start to preview your architecture.
The project has a strong community and is open to contributors. For help, you can join the Discord community or use GitHub Discussions.
LikeC4 is perfect for developers and architects who want to streamline their workflow and improve collaboration. The project is MIT-licensed and open-source, with ongoing development made possible by community support.
One-liner takeaway: LikeC4 revolutionizes software architecture design by making it visual, collaborative, and always up-to-date.
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π§ Channel: https://t.me/GithubRe
14 191
β‘ schollz/croc is making waves. Here's the full picture.
π https://github.com/schollz/croc
π Easily and securely send things from one computer to another π π¦
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croc is a secure, easy-to-use file transfer tool that works between any two computers. Its key features include end-to-end encryption, cross-platform compatibility, resumable transfers, and no need for a local server or port-forwarding.
To use
croc, simply download and install it, then send files with croc send [file(s)-or-folder] and receive them with croc [code-phrase].
It's written in Go and has been designed with security in mind, using password-authenticated key agreement (PAKE) for secure key exchange.
The croc community is active, and the tool has been widely adopted.
Overall, croc is a reliable and user-friendly solution for secure file transfers - give it a try and see how easy file sharing can be!
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π§ Channel: https://t.me/GithubRe
14 191
π― ayghri/i-have-adhd landed on trending. Worth a proper look.
π https://github.com/ayghri/i-have-adhd
π A skill for your coding agent to stop it from burying the answer. ADHD-friendly output.
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The ayghri/i-have-adhd GitHub repository is designed to provide ADHD-friendly outputs for coding assistants, making it easier for users to focus on the task at hand. The key feature of this repository is its ability to
stop burying the answer and instead provide action-first, step-by-step instructions.
To use this repository, you can install it as a plugin in your preferred coding assistant, such as Claude Code or Codex, using the provided claude plugin installcodex plugin add14 191
π Meet ruvnet/RuView: a gem from today's GitHub trending list.
π https://github.com/ruvnet/RuView
π Ο RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection β all without a single pixel of video.
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RuView is a WiFi sensing platform that transforms radio signals into spatial intelligence. It detects people, tracks movement, and monitors rooms through walls, in the dark, with no cameras or wearables. Key features include presence and occupancy detection, vital sign measurement, activity recognition, environment mapping, and sleep quality monitoring.
Technical highlights include the use of Channel State Information (CSI) from low-cost ESP32 sensors, spiking neural networks for local learning, and cryptographic attestation via an Ed25519 witness chain. The system integrates with major smart-home ecosystems like Home Assistant, Apple Home, Google Home, and Alexa.
Usage options range from a simple
docker pull and docker run for simulated data to live sensing with ESP32-S3 hardware or a full system with Cognitum Seed. The platform is designed for low-power edge applications and provides a range of edge modules for various use cases.
The target audience includes developers, researchers, and users interested in spatial intelligence, WiFi sensing, and smart home automation. With RuView, you can turn ordinary WiFi into a contactless sensor and unlock new possibilities for spatial awareness and automation.
One line takeaway: RuView revolutionizes spatial intelligence by transforming WiFi signals into actionable data, enabling a wide range of applications, from smart homes to healthcare, without the need for cameras or wearables.
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π§ Channel: https://t.me/GithubRe
14 191
π₯ koala73/worldmonitor is trending β and it deserves your attention.
π https://github.com/koala73/worldmonitor
π Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
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The World Monitor is a real-time global intelligence dashboard that provides AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking. Its key features include
500+ curated news feeds, a dual map engine, and cross-stream correlation for convergence of military, economic, disaster, and escalation signals.
To use World Monitor, you can git clone the repository, npm install, and npm run dev to start the app. The project has a tech stack that includes Vanilla TypeScript, Vite, globe.gl, and deck.gl for the frontend, and Tauri 2 with Node.js sidecar for the desktop app.
World Monitor is designed for programmatic access, with a MCP server, REST API, and CLI tools. It also has SDKs for Python, Ruby, and Go. The project aggregates data from 65+ external providers and APIs, and its data sources catalog is available for reference.
The World Monitor community welcomes contributions, and the project is licensed under AGPL-3.0-only. Overall, World Monitor is a powerful tool for geopolitical monitoring and analysis, and its open-source nature makes it a great resource for researchers, analysts, and developers.
The World Monitor is a game-changer for anyone looking to stay on top of global events - track the world, in real-time.
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π§ Channel: https://t.me/GithubRe
14 191
π‘ KnockOutEZ/wigolo just hit the trending charts β here's why it matters.
π https://github.com/KnockOutEZ/wigolo
π The go-to web for your AI coding agent β local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.
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wigolo is a local-first web intelligence platform designed for AI agents, providing a single surface for web-related tasks like search, fetch, crawl, extract, cache, and research. It runs on the user's machine, eliminating the need for API keys, cloud services, and metered bills. Key features include multi-engine web search, fetch and structured extraction, whole-site crawl and mapping, and verbatim excerpts with byte-pinned source spans.
Usage is straightforward: initialize the platform with
npx wigolo init, and then use various tools like wigolo search, wigolo fetch, and wigolo extract to perform tasks. The platform also supports interactive setup with --interactive or --wizard flags.
Technical highlights include a tiered router for fetching pages, a headless browser engine for handling anti-bot challenges, and a caching mechanism for storing query results. The platform also supports various LLM providers like Gemini, Anthropic, and OpenAI, allowing users to choose their preferred language model.
The target audience for wigolo includes developers, researchers, and users of AI agents, such as Claude Code, Cursor, and Codex. The platform is designed to be private by default, with all data stored locally under ~/.wigolo/.
In summary, wigolo is a powerful, local-first web intelligence platform that provides a single surface for AI agents to perform various web-related tasks, all while maintaining user privacy and eliminating the need for API keys and cloud services. Take control of your AI agent's web interactions with wigolo!
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π§ Channel: https://t.me/GithubRe
14 191
β‘ dottxt-ai/outlines is making waves. Here's the full picture.
π https://github.com/dottxt-ai/outlines
π Structured Outputs
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Introducing Outlines, a revolutionary tool that guarantees structured outputs for Large Language Models (LLMs). With Outlines, you can ensure that your LLMs produce predictable, high-quality outputs, eliminating the need for post-generation parsing or regex. This tool is designed to work seamlessly with any LLM, including OpenAI, Ollama, and vLLM, and provides
simple integration through a single line of code: model(prompt, output_type).
Outlines is built around a philosophy of simplicity, using Python's type system to define the desired output structure. You can use Literal types for simple classification, int for numerical values, or define complex objects using Pydantic models. The tool also supports union types for handling incomplete data or uncertain outputs.
To get started with Outlines, simply pip install outlines and connect to your preferred LLM using from_transformers. You can then use the model function to generate structured outputs, such as sentiment analysis, product categorization, or event parsing.
Outlines is trusted by top companies like NVIDIA, Cohere, and HuggingFace, and is designed for a wide range of applications, from customer support triage to e-commerce product categorization. Whether you're a developer, researcher, or business leader, Outlines is the perfect tool for unlocking the full potential of LLMs.
In short, Outlines is the key to unleashing the power of LLMs - try it today and discover a world of structured, predictable, and high-quality outputs! Outlines: where LLMs meet structure.
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π§ Channel: https://t.me/GithubRe14 191
π microsoft/Ontology-Playground caught my eye on GitHub Trending today.
π https://github.com/microsoft/Ontology-Playground
π Free, open-source web app for learning about ontologies and Microsoft Fabric IQ. Explore a catalogue of pre-built ontologies, design your own visually, export as RDF/XML, and share interactive diagrams. Zero backend, fully static.
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The Ontology Playground is a free, open-source web application for learning about ontologies and Microsoft Fabric IQ. This platform allows users to explore pre-built ontologies, design their own in a visual editor, export as RDF/XML, and share interactive diagrams. The key features include an
interactive graph exploration, a curated ontology catalogue, a visual ontology designer, and RDF import & export capabilities.
Developers can use the Ontology Playground to create and edit ontologies, and then deploy them to various environments, including Azure Static Web Apps and GitHub Pages. The project structure is well-organized, with separate directories for React components, data, lib, and styles.
The target audience includes developers, data scientists, and anyone interested in learning about ontologies and knowledge graphs. With its user-friendly interface and extensive documentation, the Ontology Playground is an ideal platform for both beginners and experienced professionals.
One-liner takeaway: The Ontology Playground is a powerful tool for creating, editing, and deploying ontologies, making it an essential resource for anyone working with knowledge graphs and Microsoft Fabric IQ.
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π§ Channel: https://t.me/GithubRe