Security Onion - Open Source Platform for Threat Hunting & Security Monitoring
Security Onion is a powerful Linux distribution built for SOC analysts, threat hunters, and blue teams. It combines industry-leading security tools into a single platform for network monitoring, intrusion detection, log management, and incident investigation.
✨ Key Features:
• 🛡️ Enterprise Security Monitoring & Threat Hunting
• 🚨 Network IDS with Suricata & Host Monitoring
• 📡 Network Metadata powered by Zeek
• 📦 Full Packet Capture (PCAP) for deep investigations
• 🔍 Elastic Stack for powerful search & analytics
• 🖥️ Unified SOC dashboard for alerts, hunting & case management
• ☁️ Supports AWS, Azure & Google Cloud deployments
🔗 https://t.co/iLu8bPzmbq
#CyberSecurity #ThreatHunting #BlueTeam #SOC #OpenSource #GitHub #NetworkSecurity
Ever wished for an AI that thinks, codes, and creates without limits? Meet Qwen3.6-40B: a multi-stage tuned, uncensored giant built for image-text-to-text tasks. It's abliterated for raw creativity, and it's blowing up with 504k downloads. This is not your average model.
🚨 ¡BOMBA!
China acaba de lanzar un agente de IA que automatiza tu escritorio 100% en local.
Sin internet. Sin nube. Sin mandar tus datos a nadie.
Puede:
✅ Controlar cualquier aplicación de escritorio
✅ Abrir archivos y carpetas
✅ Navegar por webs
✅ Hacer tareas completas de forma autónoma
Y lo mejor: es 100% código abierto.
Esto ya no es futuro… es ahora.
¿Cuál sería la primera tarea que le pedirías? 👇
RT si te flipa y sígueme para más IA real que importa
This might be the most underrated MCP server I've found this week.
Stealth Browser MCP gives AI agents an undetectable browser that can automate sites most browser tools can't.
• Bypasses Cloudflare & anti-bot systems
• 97 browser automation tools
• AI-generated network hooks
• Pixel-perfect UI extraction
• Full CDP access
• Cross-platform support
100% Open Source (MIT License)
Repo:
https://t.co/66pVMNWdok
Voice cloning on CPU locally run regular lowend laptop,
This TTS a blazing-fast, lightweight text-to-speech model that runs smoothly on regular CPUs.
- 200ms first chunk
- Voice cloning
- Multiple languages support
- 6x real-time on a CPU
- https://t.co/ipT9Z2ork3
MaxKB is an open-source platform for building enterprise-grade RAG agents with an integrated pipeline for knowledge retrieval and AI workflows.
- RAG pipeline with automatic text splitting, vectorization, and document ingestion
- Agentic workflow engine with function library and MCP tool-use capabilities
- Model-agnostic support for private (DeepSeek, Llama, Qwen) and public (OpenAI, Claude) models
- Zero-coding integration into third-party business systems
If You Want To SEE How Tok/s Actually Looks. Like 5,10,20,30 all the way to 2000tok/s I found this dope site that helps you visualize it. I can say even at 20 tok/s speeds are not that bad especially for on a conversational scale. https://t.co/v8rRrWA7fk
No vuelvas a usar Ollama si quieres sacar el máximo rendimiento a tu IA local.
No exprime al máximo la potencia de tu GPU.
Y eso se nota cuando empiezas a servir modelos más exigentes.
Por eso nació vLLM.
Un proyecto desarrollado originalmente en UC Berkeley para ejecutar modelos de IA de forma mucho más rápida, eficiente y preparada para producción.
→ Hasta 2 veces más rendimiento en determinadas cargas de trabajo
→ Compatible con más de 200 arquitecturas de modelos
→ API compatible con OpenAI para integrarlo fácilmente
→ Funciona con NVIDIA, AMD, Apple Silicon, TPU y mucho más
Se instala con un simple:
pip install vllm
Mientras Ollama está pensado para que cualquiera pueda ejecutar modelos de IA...
vLLM está diseñado para exprimir al máximo el hardware y servir modelos con el mejor rendimiento posible.
Tiene más de 85k stars en GitHub, es 100% gratis y open-source.
Repo en comentarios 👇
It's crazy to think that most of my tasks can be done with local models now.
My current local model stack for @NousResearch Hermes on my single 3090:
Gemma4-12B-QAT (KV F16 + full context)-> normal everyday tasks. quick research.
Qwen3.6-35B-A3B-Q6/Q8 (KV q8_0 + 144k context + offloading) -> serious agentic tasks + coding tasks
Qwen3.6-27B-Q4/Q5 (KV q8_0 + 110k context) -> only for coding tasks
All running through llama.cpp.