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Source channel @githubtrending · Post #15510 · Feb 20

#go#ai_agents#ai_security_tool#anthropic#autonomous_agents#golang#gpt#graphql#multi_agent_system#offensive_security#open_source#openai#penetration_testing#penetration_testing_tools#react#security_automation#security_testing#security_tools#self_hosted PentAGI is an AI-powered tool that automates penetration testing with smart agents using 20+ pro tools like nmap and metasploit in a safe Docker sandbox. It researches vulnerabilities, executes attacks, stores knowledge for reuse, and creates detailed reports via a simple web UI. Quick setup needs Docker, an LLM API key (OpenAI/Anthropic), and `docker compose up -d`. This saves you hours of manual work, speeds up secure testing, cuts errors, and helps find issues faster for better protection. https://github.com/vxcontrol/pentagi

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Interesting Planet 🌍

@interesting_planet_facts · Post #1053 · 11/19/2025, 06:11 PM

🌎 In 1977, the Soviet Venera 14 probe recorded mysterious low-frequency “thunder”-like sounds on Venus. Scientists now attribute these to seismic activity or wind interacting with the planet’s dense atmosphere. Venus’s surface winds move slowly, but thick air carries sound much farther than on Earth. ✨ #Venus⚡#sounds⚡#space 👉subscribe Interesting Planet 👉more Channels ​

djangoproject

@djangoproject · Post #255 · 02/02/2017, 06:57 PM

https://github.com/tyiannak/pyAudioAnalysis #pyAudioAnalysis is a Python library covering a wide range of audio analysis tasks. Through pyAudioAnalysis you can: Extract #audio features and representations (e.g. mfccs, spectrogram, chromagram) Classify unknown #sounds Train, parameter tune and evaluate classifiers of audio segments Detect audio events and exclude silence periods from long recordings Perform supervised segmentation (joint segmentation - classification) Perform unsupervised segmentation (e.g. speaker diarization) Extract audio thumbnails Train and use audio regression models (example application: emotion recognition) Apply dimensionality reduction to visualize audio data and content similarities