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GitHub Trends
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See what the GitHub community is most excited about today. A bot automatically fetches new repositories from https://github.com/trending and sends them to the channel. Author and maintainer: https://github.com/katursis
Mostrar másEl país no está especificadoTecnologías y Aplicaciones11 561
📈 Análisis del canal de Telegram GitHub Trends
El canal GitHub Trends (@githubtrending) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 10 770 suscriptores, ocupando la posición 11 561 en la categoría Tecnologías y Aplicaciones.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 10 770 suscriptores.
Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 207, y en las últimas 24 horas de 1, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 5.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.81% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 594 visualizaciones. En el primer día suele acumular 302 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como linux, workflow, setup, claude, command.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“See what the GitHub community is most excited about today.
A bot automatically fetches new repositories from https://github.com/trending and sends them to the channel.
Author and maintainer: https://github.com/katursis”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 07 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
10 770
Suscriptores
+124 horas
+427 días
+20730 días
Archivo de publicaciones
10 770
#go #git #go #golang #hacktoberfest #hooks #lefthook #manager
Lefthook is a fast Git hooks manager built in Go for Node.js, Ruby, Python, and other projects. Install it easily via Go, NPM, gem, or pipx, then configure hooks in a simple lefthook.yml file and run `lefthook install`. It runs commands in parallel, filters files with globs/regex, supports scripts, tags, Docker, and local overrides for speed and control. This saves you time on commits/pushes by automating linting and checks quickly without dependencies, keeping code clean effortlessly.
https://github.com/evilmartians/lefthook
10 770
#cmake
CMake-based MinGW-w64 Cross Toolchain
https://github.com/shinchiro/mpv-winbuild-cmake
10 770
#rust
Miri is a tool that detects bugs in unsafe Rust code by finding undefined behavior—situations where your program violates safety rules and can behave unpredictably. When you write unsafe code, you bypass Rust's normal safety checks, so you must manually ensure your code follows strict requirements like proper memory alignment, no data races, and correct pointer usage. Miri catches violations of these requirements by running your code in a special interpreter that monitors every operation. It detects problems like out-of-bounds memory access, use-after-free errors, uninitialized data, and misaligned pointers. You can easily use Miri by installing it with Rust's nightly toolchain and running `cargo miri test` on your project. The benefit is that Miri finds subtle bugs that would otherwise cause crashes or security vulnerabilities in production, making it an essential tool for anyone writing unsafe Rust code.
https://github.com/rust-lang/miri
10 770
#javascript #aicoding #free
AIClient-2-API is a free Node.js proxy that turns client-only AI models like Gemini 3 Pro, Claude 4.5 Opus, Qwen3 Coder Plus, and Kiro into one easy OpenAI-compatible API you run locally with Docker or a script. Access a web console at localhost:3000 to add keys, switch models, monitor health, and log chats—no code changes needed for tools like Cherry-Studio. You benefit by using top free/cheap advanced AIs seamlessly, bypassing limits with smart account pooling for 99.9% uptime, saving costs, and building private datasets from logs.
https://github.com/justlovemaki/AIClient-2-API
10 770
#python
This repository offers Anthropic's Claude Skills—folders with instructions, scripts, and resources that dynamically teach Claude specialized tasks like branded documents, data analysis, or workflows. Examples cover creative, technical, and enterprise uses; install via Claude Code, .ai, or API, or create your own with a simple SKILL.md template. You benefit by automating repetitive work, boosting productivity, ensuring consistent results, and capturing your team's knowledge for reliable, scalable AI performance.
https://github.com/anthropics/skills
10 770
#powershell
EntraGoat creates a safe, vulnerable Microsoft Entra ID setup in your test tenant using PowerShell and a web interface for easy deployment of scenarios like privilege escalation attacks. Clone the GitHub repo, install tools, run scripts for challenges with flags, solutions, and cleanups—no extra costs. You benefit by safely practicing real-world identity hacks, spotting misconfigurations, and boosting your skills to secure production systems without risks.
https://github.com/Semperis/EntraGoat
10 770
#python #gemini #gemini_ai #gemini_api #gemini_flash #gemini_pro #information_extration #large_language_models #llm #nlp #python #structured_data
**LangExtract** is a free Python library that uses AI models like Gemini to pull structured data—like names, emotions, or meds—from messy text such as reports or books. It links every fact to its exact spot in the original, creates interactive visuals for easy checks, handles huge files fast with chunking and parallel runs, and works with cloud or local models without fine-tuning. You benefit by quickly turning unstructured docs into reliable, organized data for analysis, saving time and boosting accuracy in fields like healthcare or research.
https://github.com/google/langextract
10 770
#python #cv #cv_builder #cv_generator #cv_template #python #resume #resume_builder #resume_generator #resume_template #typst
RenderCV lets you write your CV as simple YAML text, then run one command like `rendercv render yourfile.yaml` to get a perfect PDF with pro typography—no layout fights or formatting hassle. Version-control it easily, focus purely on content, customize themes/colors/fonts, validate strictly, and use any language. **You save time, get pixel-perfect resumes every time, and maintain versions without chaos.** (78 words)[1][5][6][7]
https://github.com/rendercv/rendercv
10 770
#python
**Reachy Mini** is an open-source desktop robot, 11 inches tall and 3.3 lbs, with a 6-DoF expressive head, 360° body rotation, animated antennas, wide-angle camera, microphones, speaker, and Hugging Face AI integration for 1.7M+ models. Assemble in 2-3 hours as a kit; choose Lite (USB-powered) or Wireless (Raspberry Pi, battery). Use simple Python SDK for quick control, apps like conversation or hand-tracking, and simulation. **You benefit** by easily building, testing, and sharing AI robots at home or work, speeding up embodied AI experiments affordably.
https://github.com/pollen-robotics/reachy_mini
10 770
#rust #ai #change_data_capture #context_engineering #data #data_engineering #data_indexing #data_infrastructure #data_processing #etl #hacktoberfest #help_wanted #indexing #knowledge_graph #llm #pipeline #python #rag #real_time #rust #semantic_search
**CocoIndex** is a fast, open-source Python tool (Rust core) for transforming data into AI formats like vector indexes or knowledge graphs. Define simple data flows in ~100 lines of code using plug-and-play blocks for sources, embeddings, and targets—install via `pip install cocoindex`, add Postgres, and run. It auto-syncs fresh data with minimal recompute on changes, tracking lineage. **You save time building scalable RAG/semantic search pipelines effortlessly, avoiding complex ETL and stale data issues for production-ready AI apps.**
https://github.com/cocoindex-io/cocoindex
10 770
#python #ai #bug_detection #code_audit #code_quality #code_review #developer_tools #devsecops #google_gemini #llm #react #sast #security_scanner #supabase #typescript #vite #vulnerability_scanner #xai
**DeepAudit** is an AI-powered code audit tool using multi-agent collaboration to deeply scan projects for vulnerabilities like SQL injection, XSS, and path traversal. Import code from GitHub/GitLab or paste snippets; agents plan, analyze with RAG knowledge, and verify issues via secure Docker sandbox PoCs, generating PDF reports with fix suggestions. Deploy easily with one Docker command, supports local Ollama models for privacy, and cuts traditional tools' high false positives. **You benefit** by automating secure audits like a pro hacker—saving time, reducing errors, ensuring real exploits are caught, and speeding safe releases without manual hassle.
https://github.com/lintsinghua/DeepAudit
10 770
#go #gemma3 #go #gpt_oss #granite4 #llama #llama3 #llm #on_device_ai #phi3 #qwen3 #qwen3vl #sdk #stable_diffusion #vlm
NexaSDK runs AI models locally on CPUs, GPUs, and NPUs with a single command, supports GGUF/MLX/.nexa formats, and offers NPU-first Android and macOS support for fast, multimodal (text, image, audio) inference, plus an OpenAI‑compatible API for easy integration. This gives you low-latency, private on-device AI across laptops, phones, and embedded systems, reduces cloud costs and data exposure, and lets you deploy and test new models immediately on target hardware for faster development and better user experience.
https://github.com/NexaAI/nexa-sdk
10 770
#typescript #documentation_generator #nuxt #nuxt_theme
Docus is a CLI tool that quickly scaffolds a complete, modern documentation site using Markdown and Vue (Nuxt 4), with responsive design, dark mode, i18n, full-text search, enhanced Markdown components, TypeScript support, and built-in AI/LLM integration via llms.txt and a native MCP server for editor/IDE tools like Cursor and VS Code, letting you start a docs site with npx create-docus and npm run dev so it runs locally instantly. Benefit: you get a production-ready, customizable docs site fast—saving setup time and giving built-in search, localization, performance, and AI tooling to improve authoring and user experience.
https://github.com/nuxt-content/docus
10 770
#python
Mini-SGLang is a compact, easy-to-read inference framework (~5,000 Python lines) that runs and serves large language models with high speed using optimizations like radix cache, chunked prefill, overlap scheduling, tensor parallelism, and FlashAttention/FlashInfer kernels. It’s CUDA-dependent, quick to install from source, and can launch an OpenAI-compatible API or interactive shell for single- or multi‑GPU serving, letting you test or deploy models (e.g., Qwen, Llama) with low latency and scalable throughput. Benefit: you get a transparent, modifiable engine to deploy fast, efficient LLM inference for development, benchmarking, or production use.
https://github.com/sgl-project/mini-sglang
10 770
#typescript #agent #agentic #agentic_ai #agents #agents_sdk #ai #ai_agents #aiagentframework #genai #genai_chatbot #llm #llms #multi_agent #multi_agent_systems #multi_agents #multi_agents_collaboration
**Agent Development Kit (ADK) for TypeScript** is an open-source toolkit to build, test, and deploy advanced AI agents with full control in code. Key features include rich tools like Google Search, custom functions, and multi-agent hierarchies for scalable apps, plus a dev UI for easy debugging. Install via `npm install @google/adk`. **You benefit** by creating flexible, versioned AI agents that integrate tightly with Google Cloud, run anywhere from laptop to cloud, and speed up development like regular software.(78 words)[1][5][6]
https://github.com/google/adk-js
10 770
#python #large_language_models #llm #penetration_testing #python
**PentestGPT** is a free, open-source AI tool that automates penetration testing like solving CTF challenges in web, crypto, and more. Install easily with Docker, add your API key (Anthropic, OpenAI, or local LLMs), then run `pentestgpt --target [IP]` for interactive guidance on scans, exploits, and reports. New v1.0 adds autonomous agents and session saving. It boosts your speed and accuracy in ethical hacking, helping beginners learn steps fast and pros tackle complex targets efficiently. (78 words)[1][3][7]
https://github.com/GreyDGL/PentestGPT
10 770
#rich_text_format #lcd_display #python #serial_communication #smart_display #smart_screen #system_monitor #system_monitoring #turing_smart_screen #xuanfang
**turing-smart-screen-python** is free open-source Python software (3.9+) for small USB-C IPS smart screens like Turing 3.5"/5", XuanFang, and others on Windows, Linux, Raspberry Pi, or macOS. Use it as a standalone system monitor showing CPU/GPU usage, temps, memory, and custom data via easy themes (with editor and community shares), or integrate into your Python projects to display text, images, progress bars, brightness, rotation, and RGB LEDs. It auto-detects ports with a simple GUI wizard—no coding needed. You benefit by turning your screen into a customizable HW dashboard or app display affordably, cross-platform, without vendor limits.
https://github.com/mathoudebine/turing-smart-screen-python
10 770
#python
**ty** is a super-fast Python type checker and language server built in Rust by Astral (makers of uv and Ruff). It's 10-100x faster than mypy or Pyright, with rich error messages, IDE features like auto-complete and hover help, and support for big projects or partial typing. Try it via `uvx ty check`. This helps you catch bugs early, code faster with real-time feedback, and boost productivity in editors like VS Code.
https://github.com/astral-sh/ty
10 770
#c_lang #driver #flash #jedec #jedec_sfdp #qspi #sfdp #sfdp_flash #spi_flash #universal_driver
**SFUD** is an open-source library that drives many SPI/QSPI Flash chips from brands like Winbond and Macronix. It auto-detects chip specs via the **SFDP** standard or a built-in table, letting you read, write, erase, and init with simple APIs after easy config. This helps you avoid risks from Flash shortages or upgrades, boosts software reuse across projects, cuts dev time, and enables tools like programmers—saving effort on varied hardware.
https://github.com/armink/SFUD
10 770
#python #gym #gym_environment #reinforcement_learning #reinforcement_learning_agent #reinforcement_learning_environments #rl_environment #rl_training
NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.)[7][1]. It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs[1][7]. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior.
https://github.com/NVIDIA-NeMo/Gym
